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Machine Learning Models in Protein Bioinformatics
Lukasz Kurgan, Yaoqi Zhou
[Abstract] [FULL-TEXT INQUIRY] [PMID: 21787309 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00019]
A Sampling-Based Method for Ranking Protein Structural Models by Integrating Multiple Scores and Features
E Xiaohu Shi, Jingfen Zhang, Zhiquan He, Yi Shang and Dong Xu
[Abstract] [FULL-TEXT INQUIRY] [PMID: 21787308 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00020]
Neural Network Pairwise Interaction Fields for Protein Model Quality Assessment and Ab Initio Protein Folding
Alberto J.M. Martin, Claudio Mirabello, Gianluca Pollastri
[Abstract] [FULL-TEXT INQUIRY] [PMID: 21787307 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00021]
Molecular Surface Representation Using 3D Zernike Descriptors for Protein Shape Comparison and Docking
Daisuke Kihara, Lee Sael, Rayan Chikhi, Juan Esquivel-Rodriguez
[Abstract] [FULL-TEXT INQUIRY] [PMID: 21787306 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00022]
Conotoxin Superfamily Prediction Using Diffusion Maps Dimensionality Reduction and Subspace Classifier
Jiang-Bo Yin, Yong-Xian Fan, Hong-Bin Shen
[Abstract] [FULL-TEXT INQUIRY] [PMID: 21787305 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00023]
Structural Models of Protein-DNA Complexes Based on Interface Prediction and Docking
Sanbo Qin, Huan-Xiang Zhou
[Abstract] [FULL-TEXT INQUIRY] [PMID: 21787304 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00024]
On the Relationship Between Catalytic Residues and their Protein Contact Number
Shao-Wei Huang, Sung-Huan Yu, Chien-Hua Shih, Huei-Wen Guan, Tsun-Tsao Huang, Jenn-Kang Hwang
[Abstract] [FULL-TEXT INQUIRY] [PMID: 21787303 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00025]
Solvent and Lipid Accessibility Prediction as a Basis for Model Quality Assessment in Soluble and Membrane Proteins
Mukta Phatak, Rafa Adamczak, Baoqiang Cao, Michael Wagner, Jaros aw Meller
[Abstract] [FULL-TEXT INQUIRY] [PMID: 21787302 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00026]
Machine Learning Algorithms for Predicting Protein Folding Rates and Stability of Mutant Proteins: Comparison with Statistical Methods
M. Michael Gromiha, Liang-Tsung Huang
[Abstract] [FULL-TEXT INQUIRY] [PMID: 21787301 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00027]
Small Open Reading Frames: Current Prediction Techniques and Future Prospect
Haoyu Cheng, Wai Soon Chan, Zhixiu Li, Dan Wang, Song Liu, Yaoqi Zhou
[Abstract] [FULL-TEXT INQUIRY] [PMID: 21787300 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00028]
Structural Protein Descriptors in 1-Dimension and their Sequence-Based Predictions
Lukasz Kurgan, Fatemeh Miri Disfani
[Abstract] [FULL-TEXT INQUIRY] [PMID: 21787299 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00029]
Cellular Automata and Its Applications in Protein Bioinformatics
Xuan Xiao, Pu Wang, Kuo-Chen Chou
[Abstract] [FULL-TEXT INQUIRY] [PMID: 21787298 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00030]
Computational Methods for Identification of Functional Residues in Protein Structure
Fuxiao Xin, Predrag Radivojac
[Abstract] [FULL-TEXT INQUIRY] [PMID: 21787297 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00031]
A Closer look at “social” boundary genes reveals knowledge to gene expression profiles
Shang Gao, Jia Zeng, Abdallah M. ElSheikh, Ghada Naji, Reda Alhajj, Jon Rokne, Douglas Demetrick
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00032]
Identification of Plant Protein Kinases in Response to Abiotic and Biotic Stresses using SuperSAGE
Ederson Akio Kido, Pedranne Kelle de Araújo Barbosa, José Ribamar Costa Ferreira Neto, Valesca Pandolfi, Laureen Michelle Houllou-Kido, Sergio Crovella, Carlos Molina, Günter Kahl, Ana Maria Benko-Iseppon
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00033]
In silico Protein-protein Interaction Prediction with Sequence Alignment and Classifier Stacking
Simone Marinil, Qian Xu, Qiang Yang
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00034]
Systematic Annotation and Bioinformatics Analyses of Large-scale Oryza sativa Proteome
Lili Liu, Lin Bai, Cong Luo, Donglin Huang, Ming Chen
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00035]
Global and Threshold-free Transcriptional Regulatory Networks Reconstruction through Integrating ChIP-chip and Expression Data
Qi Liu, Yi Yang, Yixue Li, Zili Zhang
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00036]
Computational analysis of phosphoproteomics: progresses and perspectives
Jian Ren, Xinjiao Gao, Zexian Liu, Jun Cao, Qian Ma, Yu Xue
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00037]
Molecular determinants of enzyme cold adaptation: comparative structural and computational studies of cold- and warm-adapted enzymes
Elena Papaleo , Matteo Tiberti, Gaetano Invernizzi, Marco Pasi, Valeria Ranzani
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00038]
Lacticin 3147 - Biosynthesis, molecular analysis, immunity, bioengineering and applications
Srinivas Suda, Paul D. Cotter, Colin Hill, R. Paul Ross
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00039]
The archaeal Sac10b protein family: conserved proteins with divergent functions
Jinsong Xuan, Yingang Feng
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00040]
Determining the orientation and localization of membrane-bound peptides
Walter Hohlweg, Simone Kosol and Klaus Zangger
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00041]
The Probe Rules In Single Particle Tracking
Mathias P. Clausen and B. Christoffer Lagerholm
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00042]
Bilayer hydrophobic thickness and integral membrane protein function
Larisa E. Cybulski and Diego de Mendoza
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00043]
Quantification of protein-protein interactions within membranes by fluorescence correlation spectroscopy
Stephanie Bleicken, Miki Otsuki and Ana J. García-Sáez
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00044]
Novel Functions And Binding Mechanisms Of Carbohydrate-Binding Proteins Determined By Force Measurements
Deborah E. Leckband
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00045]
What can we learn from single molecule trajectories?
Verena Ruprecht, Markus Axmann, Stefan Wieser and Gerhard J. Schütz
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00046]
Disordered proteins and network disorder in network descriptions of protein structure, dynamics and function. Hypotheses and a comprehensive review
Peter Csermely, Kuljeet Singh Sandhu, Eszter Hazai, Zsolt Hoksza, Huba J.M. Kiss, Federico Miozzo, Dániel V. Veres; Francesco Piazza and Ruth Nussinov
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00047]
The Role Of Intrinsically Disordered Regions In The Structure And Functioning Of Small Heat Shock Proteins
N.B. Gusev
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00048]
Understanding Pre-Structured Motifs (PreSMos) in Intrinsically Unfolded Proteins
Si-Hyung Lee, Do-Hyoung Kim, Joan J. Han, Eun-Ji Cha, Ji-Eun Lim, Ye-Jin Cho, Chewook Lee, and Kyou-Hoon Han
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00049]
Comprehensive comparative assessment of in-silico predictors of disordered regions
Zhen-Ling Peng and Lukasz Kurgan
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00050]
How random are intrinsically disordered proteins?
A small angle scattering perspective
Veronique Receveur-Bréchot and Dominique Durand
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00051]
HSF transcription factor family, heat shock response, and protein intrinsic disorder
Sandy D. Westerheide, Rachel Raynes, Chase Powell, Bin Xue and Vladimir N. Uversky
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00052]
Disruption Of The V-Atpase Functionality As A Way To Uncouple Bone Formation And Resorption – A Novel Target For Treatment Of Osteoporosis
Thudium CS, Jensen VK, Karsdal MA and Henriksen K
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00053]
Targeting Reversible Disassembly as a Mechanism of Controlling V-ATPase Activity
Patricia M. Kane
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00054]
Regulation of V-ATPase Expression in Mammalian Cells
Beth S. Lee
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00055]
V-ATPase Subunit Interactions: The Long Road to Therapeutic Targeting
Norbert Kartner and Morris F. Manolson
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00056]
Novel insights into V-ATPase functioning: distinct roles for its accessory subunits ATP6AP1/Ac45 and ATP6AP2/(pro)renin receptor
Eric J.R. Jansen and Gerard J.M Martens
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00057]
Vacuolar H+-ATPase signaling pathway in cancer
Souad R. Sennoune and Raul Martínez-Zaguilán
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00058]
Rational identification of enoxacin as a novel V-ATPase-directed osteoclast inhibitor
Edgardo J. Toro, David A. Ostrov, Thomas J. Wronski and L. Shannon Holliday
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00059]
Recent progress in computational approaches to studying the M2 proton channel and its implication to drug design against influenza viruses
Qi-Shi Du and Ri-Bo Huang
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00060]
The archaeal Sac10b protein family: conserved proteins with divergent functions
Jinsong Xuan and Yingang Feng
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00061]
Editorial: Efficient Strategies for Signalling Pathway Mining
Qingfeng Chen and Baoshan Chen
[BSP/CPPS/E-Pub/00062]
Editorial: Membrane Proteins, a Biophysical Perspective
[BSP/CPPS/E-Pub/00063]
The V-ATPase as a Target for Antifungal Drugs
Yongqiang Zhang and Rajini Rao
[Abstract] [FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00064]
Abstracts
Machine Learning Models in Protein Bioinformatics
Lukasz Kurgan, Yaoqi Zhou
[FULL-TEXT INQUIRY] [PMID: 21787309 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00019]
Bioinformatics is a relatively new field concerned with the computational analysis and prediction of properties of biomolecules, DNA, RNA, and proteins, in particular, on a genomic/proteomic scale. Machine learning models play increasingly important roles in development of novel methodologies, summarization, and high-throughput analysis in the bioinformatics field. Advances in the related area, including protein structure and function prediction [1, 2], structural bioinformatics [3], and peptide analysis [4] were recently summarized, and several works that overview specific sub-areas of protein bioinformatics, such as prediction of secondary structure [5, 6], helical transmembrane proteins [7], localization and targeting [8], binding sites [9, 10], and RNA-binding [11], were published in the last couple of years. This issue provides a comprehensive overview of current efforts related to the analysis of protein data, from sequences to structures to functions. It consists of two parts, the first with five reviews and the second that includes seven original methodology papers.
The first review by Xin and Radivojac summarizes approaches for the computational identification of functional residues in protein structures and discusses their applications in functional proteomics, including prediction of catalytic residues, posttranslational modifications, and nucleic acid-binding sites. The second manuscript by Kurgan and Disfani provides a comprehensive review of ten one-dimensional structural descriptors of proteins and comparatively summarizes over eighty computational models that are used to predict these descriptors from the protein sequences, primarily focusing on the prediction of secondary structure, relative solvent accessibility, and disorder. The review by Gromiha and Huang discusses machine learning-based and statistical methods for the computational prediction of protein folding rates and stability. The fourth paper by Zhou and coworkers overviews and compares current techniques for the prediction of small open reading frames and emphasizes the need for further research in this area. The last review introduces cellular automata and concentrates on its applications in the protein bioinformatics. The first original research paper by Kihara and coworkers describes the three-dimensional Zernike descriptor, which is used to describe molecular surfaces, and overviews several applications of this descriptor. In the next paper, Qin and Zhou introduce their DISPLAR method that aims at the accurate protein structure-based prediction of DNA binding sites. The manuscript by Xu and coworkers describes and evaluates a new sampling-based machine learning method to rank protein structural models by integrating multiple scores and features. The next two original contributions describe new methodologies for the protein model quality assessment. The work by Martin, Mirabello, and Pollastri concerns an efficient knowledge-based approach that utilizes neural network pairwise interaction fields. The paper by Meller and coworkers introduces a method based on the prediction of relative solvent accessibility using support vector regression, which is applied to soluble and alpha-helical membrane proteins. The next contribution by Hwang et al. investigates a relation between contact numbers and catalytic residues to build a simple and effective predictor of the catalytic residues. We close the issue with the paper by Yin, Fan, and Shen which proposes and evaluates an accurate nearest neighbor-based method for the prediction of the conotoxin superfamily.
We are excited to deliver this comprehensive issue that tackles a diverse set of developments in the area of protein bioinformatics. We hope that it will constitute an indispensable resource for bioinformaticians, computer scientists, computational biologists, biophysicists, and biochemists.
Last but not least, we would like to thank all the authors who make this issue possible. We are in great debt to the 26 anonymous reviewers from around the world who delivered timely and useful comments to the authors. The guest editors also express their gratitude to the Editor-in-Chief Prof. Ben M. Dunn for his invitation and support that resulted in the successful completion of this special issue.
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A Sampling-Based Method for Ranking Protein Structural Models by Integrating Multiple Scores and Features
E Xiaohu Shi, Jingfen Zhang, Zhiquan He, Yi Shang and Dong Xu
[FULL-TEXT INQUIRY] [PMID: 21787308 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00020]
One of the major challenges in protein tertiary structure prediction is structure quality assessment. In many cases, protein structure prediction tools generate good structural models, but fail to select the best models from a huge number of candidates as the final output. In this study, we developed a sampling-based machine-learning method to rank protein structural models by integrating multiple scores and features. First, features such as predicted secondary structure, solvent accessibility and residue-residue contact information are integrated by two Radial Basis Function (RBF) models trained from different datasets. Then, the two RBF scores and five selected scoring functions developed by others, i.e., Opus-CA, Opus-PSP, DFIRE, RAPDF, and Cheng Score are synthesized by a sampling method. At last, another integrated RBF model ranks the structural models according to the features of sampling distribution. We tested the proposed method by using two different datasets, including the CASP server prediction models of all CASP8 targets and a set of models generated by our in-house software MUFOLD. The test result shows that our method outperforms any individual scoring function on both best model selection, and overall correlation between the predicted ranking and the actual ranking
of structural quality.
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Neural Network Pairwise Interaction Fields for Protein Model Quality Assessment and Ab Initio Protein Folding
Alberto J.M. Martin, Claudio Mirabello, Gianluca Pollastri
[FULL-TEXT INQUIRY] [PMID: 21787307 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00021]
In order to use a predicted protein structure one needs to know how good it is, as the utility of a model depends on its quality. To this aim, many Model Quality Assessment Programs (MQAP) have been developed over the last decade, with MQAP also being assessed at the CASP competition. We present a new knowledge-based MQAP which evaluates single protein structure models. We use a tree representation of the C trace to train a novel Neural Network Pairwise Interaction Field (NN-PIF) to predict the global quality of a model. NN-PIF allows fast evaluation of multiple structure models for a single sequence. In our tests on a large set of structures, our networks outperform most other methods based on different and more complex protein structure representations in global model quality prediction. Moreover, given NNPIF can evaluate protein conformations very fast, we train a separate version of the model to gauge its ability to fold protein structures ab initio. We show that the resulting system, which relies only on basic information about the sequence and the C trace of a conformation, generally improves the quality of the structures it is presented with and may yield promising predictions in the absence of structural templates, although more research is required to harness the full potential of the model.
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Molecular Surface Representation Using 3D Zernike Descriptors for Protein Shape Comparison and Docking
Daisuke Kihara, Lee Sael, Rayan Chikhi, Juan Esquivel-Rodriguez
[FULL-TEXT INQUIRY] [PMID: 21787306 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00022]
The tertiary structures of proteins have been solved in an increasing pace in recent years. To capitalize the enormous efforts paid for accumulating the structure data, efficient and effective computational methods need to be developed for comparing, searching, and investigating interactions of protein structures. We introduce the 3D Zernike descriptor (3DZD), an emerging technique to describe molecular surfaces. The 3DZD is a series expansion of mathematical three-dimensional function, and thus a tertiary structure is represented compactly by a vector of coefficients of terms in the series. A strong advantage of the 3DZD is that it is invariant to rotation of target object to be represented. These two characteristics of the 3DZD allow rapid comparison of surface shapes, which is sufficient for real-time structure database screening. In this article, we review various applications of the 3DZD, which have been recently proposed.
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Conotoxin Superfamily Prediction Using Diffusion Maps Dimensionality Reduction and Subspace Classifier
Jiang-Bo Yin, Yong-Xian Fan, Hong-Bin Shen
[FULL-TEXT INQUIRY] [PMID: 21787305 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00023]
Conotoxins are disulfide-rich small peptides that are invaluable channel-targeted peptides and target neuronal
receptors, which have been demonstrated to be potent pharmaceuticals in the treatment of Alzheimer’s disease, Parkinson’s disease, and epilepsy. Accurate prediction of conotoxin superfamily would have many important applications towards the understanding of its biological and pharmacological functions. In this study, a novel method, named dHKNN, is developed to predict conotoxin superfamily. Firstly, we extract the protein’s sequential features composed of physicochemical properties, evolutionary information, predicted secondary structures and amino acid composition. Secondly, we use the diffusion maps for dimensionality reduction, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain efficient representation of data geometric descriptions. Finally, an improved K-local hyperplane distance nearest neighbor subspace classifier method called dHKNN is proposed for predicting conotoxin superfamilies by considering the local density information in the diffusion space. An overall accuracy of 91.90% is obtained through the jackknife cross-validation test on a benchmark dataset, indicating the proposed dHKNN is very promising.
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Structural Models of Protein-DNA Complexes Based on Interface Prediction and Docking
Sanbo Qin, Huan-Xiang Zhou
[FULL-TEXT INQUIRY] [PMID: 21787304 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00024]
Protein-DNA interactions are the physical basis of gene expression and DNA modification. Structural models that reveal these interactions are essential for their understanding. As only a limited number of structures for protein-DNA complexes have been determined by experimental methods, computation methods provide a potential way to fill the need. We have developed the DISPLAR method to predict DNA binding sites on proteins. Predicted binding sites have been used to assist the building of structural models by docking, either by guiding the docking or by selecting near-native candidates from the docked poses. Here we applied the DISPLAR method to predict the DNA binding sites for 20 DNAbinding proteins, which have had their DNA binding sites characterized by NMR chemical shift perturbation. For two of these proteins, the structures of their complexes with DNA have also been determined. With the help of the DISPLAR predictions, we built structural models for these two complexes. Evaluations of both the 20 DNA binding sites and the structural models of the two protein-DNA complexes against experimental results demonstrate the significant promise of our model-building approach.
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On the Relationship Between Catalytic Residues and their Protein Contact Number
Shao-Wei Huang, Sung-Huan Yu, Chien-Hua Shih, Huei-Wen Guan, Tsun-Tsao Huang, Jenn-Kang Hwang
[FULL-TEXT INQUIRY] [PMID: 21787303 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00025]
Due to advances in structural biology, an increasing number of protein structures of unknown function have been deposited in the Protein Data Bank (PDB). These proteins are usually characterized by novel structures and sequences. Conventional comparative methodology (such as sequence alignment, structure comparison, or template search) is unable to determine their function. Thus, it is important to identify protein's function directly from its structure, but this is not an easy task. One of the strategies used is to analyze whether there are distinctive structure-derived features associated with functional residues. If so, one may be able to identify the functional residues directly from a single structure. Recently, we have shown that protein weighted contact number is related to atomic thermal fluctuations and can be used to derive motional correlations in proteins. In this report, we analyze the weighted contact-number profiles of both catalytic residues and non-catalytic residues for a dataset of 760 structures. We found that catalytic residues have distinct distributions of weighted contact numbers from those of non-catalytic residues. Using this feature, we are able to effectively differentiate catalytic residues from other residues with a single optimized threshold value. Our method is simple to implement and compares favourably with other more sophisticated methods. In addition, we discuss the physics behind the relationship between catalytic residues and their contact numbers as well as other features (such as residue centrality or Bfactors) associated with catalytic residues.
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Solvent and Lipid Accessibility Prediction as a Basis for Model Quality Assessment in Soluble and Membrane Proteins
Mukta Phatak, Rafa Adamczak, Baoqiang Cao, Michael Wagner, Jaros aw Meller
[FULL-TEXT INQUIRY] [PMID: 21787302 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00026]
On-going efforts to improve protein structure prediction stimulate the development of scoring functions and methods for model quality assessment (MQA) that can be used to rank and select the best protein models for further refinement. In this work, sequence-based prediction of relative solvent accessibility (RSA) is employed as a basis for a simple MQA method for soluble proteins, and subsequently extended to the much less explored case of (alpha-helical) membrane
proteins. In analogy to soluble proteins, the level of exposure to the lipid of amino acid residues in transmembrane (TM) domains is captured in terms of the relative lipid accessibility (RLA), which is predicted from sequence using lowcomplexity Support Vector Regression models. On an independent set of 23 TM proteins, the new SVR-based predictor yields correlation coefficient (CC) of 0.56 between the predicted and observed RLA profiles, as opposed to CC of 0.13 for a baseline predictor that utilizes TMLIP2H empirical lipophilicity scale (with standard deviations of about 0.15). A simple MQA approach is then defined by ranking models of membrane proteins in terms of consistency between predicted and observed RLA profiles, as a measure of similarity to the native structure. The new method does not require a set of decoy models to optimize parameters, circumventing current limitations in this regard. Several different sets of models, including those generated by fragment based folding simulations, and decoys obtained by swapping TM helices to mimic errors in template based assignment, are used to assess the new approach. Predicted RLA profiles can be used to successfully discriminate near native models from non-native decoys in most cases, significantly improving the separation of correct and incorrectly folded models compared to a simple baseline approach that utilizes TMLIP2H. As suggested by the robust performance of a simple MQA method for soluble proteins that utilizes more accurate RSA predictions, further significant improvements are likely to be achieved. The steady growth in the number of resolved membrane protein structures is expected to yield enhanced RLA predictions, facilitating further efforts to improve de novo and template based prediction of membrane protein structure.
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Machine Learning Algorithms for Predicting Protein Folding Rates and Stability of Mutant Proteins: Comparison with Statistical Methods
M. Michael Gromiha, Liang-Tsung Huang
[FULL-TEXT INQUIRY] [PMID: 21787301 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00027]
Machine learning algorithms have wide range of applications in bioinformatics and computational biology such as prediction of protein secondary structures, solvent accessibility, binding site residues in protein complexes, protein folding rates, stability of mutant proteins, discrimination of proteins based on their structure and function. In this work, we focus on two aspects of predications: (i) protein folding rates and (ii) stability of proteins upon mutations. We briefly introduce the concepts of protein folding rates and stability along with available databases, features for prediction methods and measures for prediction performance. Subsequently, the development of structure based parameters and their relationship with protein folding rates will be outlined. The structure based parameters are helpful to understand the physical basis for protein folding and stability. Further, basic principles of major machine learning techniques will be mentioned and their applications for predicting protein folding rates and stability of mutant proteins will be illustrated. The machine learning techniques could achieve the highest accuracy for predicting protein folding rates and stability. In essence, statistical methods and machine learning algorithms are complimenting each other for understanding and predicting protein folding rates and the stability of protein mutants. The available online resources on protein folding rates and stability will be listed.
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Small Open Reading Frames: Current Prediction Techniques and Future Prospect
Haoyu Cheng, Wai Soon Chan, Zhixiu Li, Dan Wang, Song Liu, Yaoqi Zhou
[FULL-TEXT INQUIRY] [PMID: 21787300 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00028]
Evidence is accumulating that small open reading frames (sORF, <100 codons) play key roles in many important biological processes. Yet, they are generally ignored in gene annotation despite they are far more abundant than the genes with more than 100 codons. Here, we demonstrate that popular homolog search and codon-index techniques perform poorly for small genes relative to that for larger genes, while a method dedicated to sORF discovery has a similar level of accuracy as homology search. The result is largely due to the small dataset of experimentally verified sORF available for homology search and for training ab initio techniques. It highlights the urgent need for both experimental and computational studies in order to further advance the accuracy of sORF prediction.
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Structural Protein Descriptors in 1-Dimension and their Sequence-Based Predictions
Lukasz Kurgan, Fatemeh Miri Disfani
[FULL-TEXT INQUIRY] [PMID: 21787299 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00029]
The last few decades observed an increasing interest in development and application of 1-dimansional (1D) descriptors of protein structure. These descriptors project 3D structural features onto 1D strings of residue-wise structural assignments. They cover a wide-range of structural aspects including conformation of the backbone, burying depth/solvent exposure and flexibility of residues, and inter-chain residue-residue contacts. We perform first-of-its-kind comprehensive comparative review of the existing 1D structural descriptors. We define, review and categorize ten structural descriptors and we also describe, summarize and contrast over eighty computational models that are used to predict these descriptors from the protein sequences. We show that the majority of the recent sequence-based predictors utilize machine learning models, with the most popular being neural networks, support vector machines, hidden Markov models, and support vector and linear regressions. These methods provide high-throughput predictions and most of them are accessible to a non-expert user via web servers and/or stand-alone software packages. We empirically evaluate several recent sequence-based predictors of secondary structure, disorder, and solvent accessibility descriptors using a benchmark set based on CASP8 targets. Our analysis shows that the secondary structure can be predicted with over 80% accuracy and segment overlap (SOV), disorder with over 0.9 AUC, 0.6 Matthews Correlation Coefficient (MCC), and 75% SOV, and relative solvent accessibility with PCC of 0.7 and MCC of 0.6 (0.86 when homology is used). We demonstrate that the secondary structure predicted from sequence without the use of homology modeling is as good as the structure extracted from the 3D folds predicted by top-performing template-based methods.
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Cellular Automata and Its Applications in Protein Bioinformatics
Xuan Xiao, Pu Wang, Kuo-Chen Chou
[FULL-TEXT INQUIRY] [PMID: 21787298 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00030]
With the explosion of protein sequences generated in the postgenomic era, it is highly desirable to develop high-throughput tools for rapidly and reliably identifying various attributes of uncharacterized proteins based on their sequence information alone. The knowledge thus obtained can help us timely utilize these newly found protein sequences for both basic research and drug discovery. Many bioinformatics tools have been developed by means of machine learning methods. This review is focused on the applications of a new kind of science (cellular automata) in protein bioinformatics. A cellular automaton (CA) is an open, flexible and discrete dynamic model that holds enormous potentials in modeling complex systems, in spite of the simplicity of the model itself. Researchers, scientists and practitioners from different fields have utilized cellular automata for visualizing protein sequences, investigating their evolution processes, and predicting their various attributes. Owing to its impressive power, intuitiveness and relative simplicity, the CA approach has great potential for use as a tool for bioinformatics.
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Computational Methods for Identification of Functional Residues in Protein Structure
Fuxiao Xin, Predrag Radivojac
[FULL-TEXT INQUIRY] [PMID: 21787297 PubMed - indexed for MEDLINE] [BSP/CPPS/E-Pub/00031]
The recent accumulation of experimentally determined protein 3D structures combined with our ability to computationally model structure from amino acid sequence has resulted in an increased importance of structure-based methods for protein function prediction. Two types of methods for function prediction have been proposed: those that can accurately predict overall biochemical or biological roles of a protein and those that predict its functional residues. Here, we review approaches used for the computational identification of functional residues in protein structures and summarize their applications to a wide variety of problems in functional proteomics, such as the prediction of catalytic residues, posttranslational modifications, or nucleic acid-binding sites. We examine four different problems in order to perform a comparison between several recently proposed methods and, finally, conclude by identifying limitations and future challenges in this field.
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A Closer look at “social” boundary genes reveals knowledge to gene expression profiles
Shang Gao, Jia Zeng, Abdallah M. ElSheikh, Ghada Naji, Reda Alhajj, Jon Rokne, Douglas Demetrick
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00032]
As social network analysis is gaining popularity in modeling real world problems, the task of applying the social network model concepts and notions to biological data is still one of the most attractive research problems to be addressed. According, our work described in this paper focuses on a particular set of genes that reside on the community boundaries in gene co-expression networks. Stemmed from community mining problem in social networks, peripheries of communities (i.e., boundaries) can be used to aid certain biological analysis. The proposed method consists of three parts: 1) Finding communities of gene co-expression networks through clustering. 2) Analyzing stability of community structures by Monte Carlo method. 3) Designing of dynamic adoption of boundaries using geometric convexity. We validated our findings using breast cancer gene expression data from various studies. Our approach contributes to the new branch of applying social network mechanisms in biological data analysis, leading to new data mining strategies implied by witnessing social behaviors in gene expression analysis.
Keywords: co-expression modules, gene expression profiling, social network mining and analysis, social community, social behavior.
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Identification of Plant Protein Kinases in Response to Abiotic and Biotic Stresses using SuperSAGE
Ederson Akio Kido, Pedranne Kelle de Araújo Barbosa, José Ribamar Costa Ferreira Neto, Valesca Pandolfi, Laureen Michelle Houllou-Kido, Sergio Crovella, Carlos Molina, Günter Kahl, Ana Maria Benko-Iseppon
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00033]
Plants are sessile organisms subjected to many environmental adversities. For their survival they must sense and respond to biotic and abiotic stresses efficiently. During this process, protein kinases are essential in the perception of environmental stimuli, triggering signaling cascades. Kinases are among the largest and most important gene families for biotechnological purposes, bringing many challenges to the bioinformaticians due to the combination of conserved domains besides diversified regions. Cowpea [Vigna unguiculata (L.) Walp.] is an important legume that is adapted to different agroclimatic conditions, including drought, humidity and a range of temperatures. For this crop, the association of the SuperSAGE method with high-throughput sequencing technology would generate reliable transcriptome profiles with millions of tags counted and statistically analyzed. An approach evaluating biotic and abiotic stresses was carried out generating over 13 million cowpea SuperSAGE tags available from leaves/roots of plants under abiotic (mechanical injury and salinity) or biotic (CABMV, Cowpea aphid born mosaic virus) stresses. The annotation and identification of tags linked by blastN to previously well described ESTs, allowed the posterior identification of kinases. The annotation efficiency depended on the database used, with the KEGG figuring as a good source for annotated ESTs especially when complemented by an independent Gene Ontology categorization, as well as the Gene Index using selected species. The use of different approaches allowed the identification of 1,350 kinase candidates considering biotic libraries and 2,268 regarding abiotic libraries, based on a combination of both, adequate descriptions and GO terms. Additional searches in kinase specific databases allowed the identification of a relatively low number of additional kinases, uncovering the lack of kinase databases for non model organisms, especially plants. Concerning the kinase families, a total of 713 potential kinases were classified into 13 families of the CMGC and STE groups. Concerning the differentially expressed kinases, 169 of the 713 potential kinases were identified (p < 0.05), 100 up- and 69 down-regulated when comparing distinct libraries, allowing the generation of a comprehensive panel of the differentially expressed kinases under biotic and abiotic stresses in a non model plant as cowpea.
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In silico Protein-protein Interaction Prediction with Sequence Alignment and Classifier Stacking
Simone Marinil, Qian Xu, Qiang Yang
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00034]
Protein-Protein Interaction (PPI) prediction is a well known problem in Bioinformatics, for which a large number of techniques have been proposed in the past. However, prediction results have not been sufficiently satisfactory for guiding biologists in web-lab experiments. One reason is that not all useful information, such as pairwise protein interaction information based on sequence alignment, has been integrated together in PPI prediction. Alignment is a basic concept to measure sequence similarity in Proteomics that has been used in a number of applications ranging from protein recognition to protein subcellular localization. In this article, we propose a novel integrated approach to predicting PPI based on sequence alignment by jointly using a k-Nearest Neighbor classifier (SA-kNN) and a Support Vector Machine (SVM). SVM is a machine learning technique used in a wide range of Bioinformatics applications, thanks to the ability to alleviate the overfitting problems. We demonstrate that in our approach the two methods, SA-kNN and SVM, are complementary, which are combined in an ensemble to overcome their respective limitations. While the SVM is trained on Amino Acid (AA) compositions and protein signatures mined from literature, the SA-kNN makes use of the similarity of two protein pairs through alignment. Experimentally, our technique leads to a significant gain in accuracy, precision and sensitivity measures at ~5%, 16% and 10% respectively.
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Systematic Annotation and Bioinformatics Analyses of Large-scale Oryza sativa Proteome
Lili Liu, Lin Bai, Cong Luo, Donglin Huang, Ming Chen
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00035]
Much has been now recognized on the rice (Oryza sativa L.) proteomics by using the powerful experimental tool two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). 2D-PAGE can be utilized for monitoring global changes of quantitative protein expression in specific tissues under various conditions. However, systematic annotations of the protein spots generated by 2D-PAGE are still limited for rice. In this study, a new approach for Oryza sativa proteome annotation based on the 2D-gel maps was developed. Based on the publicly available 2D-PAGE data of rice, 11,201 gel spots were annotated accounting for 87.2% of the total spots on the gel maps. Gel spot alignments were performed for the annotated gel maps belonging to 23 rice tissues or organelles. In summary, 253 alignments between 23 tissues or organelles were performed, and 26,207 co-expressed proteins were identified using our analytical strategy. Large-scale bi-cluster analysis of 23 tissues/organelles proteomes of rice was carried out to detect novel functional proteins. Function and pathway analysis identified a number of common gene products with great potential in regulating specific physiological and biochemical events within various rice tissues/organs. It also suggested that the tissue- or organelle-specific proteins might be responsible for the functional divergence of these tissues or organelles. Taken together, this study provides us new strategies and informative resources for rice proteome research based on 2D-PAGE data.
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Global and Threshold-free Transcriptional Regulatory Networks Reconstruction through Integrating ChIP-chip and Expression Data
Qi Liu, Yi Yang, Yixue Li, Zili Zhang
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00036]
Inferring transcriptional regulatory networks from high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed TReNGO (Transcriptional Regulatory Networks reconstruction based on Global Optimization), a global and threshold-free algorithm with simulated annealing for inferring regulatory networks by the integration of ChIP-chip and expression data. Superior to existing methods, TReNGO was expected to find the optimal structure of transcriptional regulatory networks without any arbitrary thresholds or predetermined number of transcriptional modules (TMs). TReNGO was applied to both synthetic data and real yeast data in the rapamycin response. In these applications, we demonstrated an improved functional coherence of TMs and TF (transcription factor)-target predictions by TReNGO when compared to GRAM, COGRIM or to analyzing ChIP-chip data alone. We also demonstrated the ability of TReNGO to discover unexpected biological processes that TFs may be involved in and to also identify interesting novel combinations of TFs.
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Computational analysis of phosphoproteomics: progresses and perspectives
Jian Ren, Xinjiao Gao, Zexian Liu, Jun Cao, Qian Ma, Yu Xue
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00037]
Phosphorylation is one of the most essential post-translational modifications (PTMs) of proteins, regulates a variety of cellular signaling pathways, and at least partially determines the biological diversity. Recent progresses in phosphoproteomics have identified more than 100,000 phosphorylation sites, while this number will easily exceed one million in the next decade. In this regard, how to extract useful information from flood of phosphoproteomics data has emerged as a great challenge. In this review, we summarized the leading edges on computational analysis of phosphoproteomics, including discovery of phosphorylation motifs from phosphoproteomics data, systematic modeling of phosphorylation network, analysis of genetic variation that influences phosphorylation, and phosphorylation evolution. Based on existed knowledge, we also raised several perspectives for further studies. We believe that integration of experimental and computational analyses will propel the phosphoproteomics research into a new phase.
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Molecular determinants of enzyme cold adaptation: comparative structural and computational studies of cold- and warm-adapted enzymes
Elena Papaleo , Matteo Tiberti, Gaetano Invernizzi, Marco Pasi, Valeria Ranzani
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00038]
The identification of molecular mechanisms underlying enzyme cold adaptation is a hot-topic both for fundamental research and industrial applications. In the present contribution, we review the last decades of structural computational investigations on cold-adapted enzymes in comparison to their warm-adapted counterparts. Comparative sequence and structural studies allow the definition of a multitude of adaptation strategies. Different enzymes carried out diverse mechanisms to adapt to low temperatures, so that a general theory for enzyme cold adaptation cannot be formulated. However, some common features can be traced in dynamic and flexibility properties of these enzymes, as well as in their intra- and inter-molecular interaction networks. Interestingly, the current data suggest that a family-centered point of view is necessary in the comparative analyses of cold- and warm-adapted enzymes. In fact, enzymes belonging to the same family or superfamily, thus sharing at least the three-dimensional fold and common features of the functional sites, have evolved similar structural and dynamic patterns to overcome the detrimental effects of low temperatures.
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Lacticin 3147 - Biosynthesis, molecular analysis, immunity, bioengineering and applications
Srinivas Suda, Paul D. Cotter, Colin Hill, R. Paul Ross
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00039]
The continuing problem of the emergence of multidrug resistance in pathogens has resulted in renewed efforts to identify novel antimicrobials that could be used in clinical settings. Lantibiotics are bacterially produced gene encoded antimicrobial peptides which have been the focus of extensive investigation in recent years because of their broad spectrum of activity. Lantibiotics (lanthionine-containing antibiotics), which have traditionally been regarded as antimicrobials for use in food or veterinary medicine, may provide at least part of the solution to these problems. Lacticin 3147 is a two peptide lantibiotic (consisting of the peptides Ltnα and Ltnβ) which is active at low concentrations against many pathogens. It has been the subject of extensive research, which has generated significant insights into the mechanisms of lacticin 3147 biosynthesis, immunity, structure function relationships and the consequences of molecular bioengineering. The merits of employing lacticin 3147 to control spoilage microbes as well as its potential in the elimination of food, human and veterinary pathogens have also been highlighted. Here we review the knowledge which has been gained with respect to lacticin 3147 since its discovery in 1995.
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The archaeal Sac10b protein family: conserved proteins with divergent functions
Jinsong Xuan, Yingang Feng
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00040]
Here we review the present state of structural and functional studies of the Sac10b protein family, a class of highly conserved 10 kDa nucleic acid-binding proteins in archaea. Based on biochemical and structural studies, these proteins were originally assigned a role in the structural organization of chromatin; Sac10b proteins of hyperthermophilic archaea, for example, showed tight, unspecific DNA binding. More recently, however, Sac10b proteins of mesophilic archaea were found to interact preferentially with specific DNA sequences thereby affecting the expression of distinct genes. Furthermore, Sac10b proteins of hyperthermophilic, thermophilic and mesophilic archaea were also shown to bind to RNA with distinct affinities and specificities but functional consequences of RNA binding of these proteins, besides perhaps RNA stabilization, have not yet been observed. To better understand the physiological meaning of the various interactions of Sac10b proteins with nucleic acids, future work should concentrate on elucidating the molecular structures of complexes of Sac10b proteins of hyperthermophilic and mesophilic archaea with DNA and RNA. In addition, existing and new X-ray and NMR structures of individual hyperthermophilic Sac10b proteins may represent very good models for introducing thermostability especially in enzymes for industrial use.
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Determining the orientation and localization of membrane-bound peptides
Walter Hohlweg, Simone Kosol and Klaus Zangger
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00041]
Many naturally occurring bioactive peptides bind to biological membranes. Studying and elucidating the mode of interaction is often an essential step to understand their molecular and biological functions. To obtain the complete orientation and immersion depth of such compounds in the membrane or a membrane-mimetic system, a number of methods are available, which are separated in this review into four main classes: solution NMR, solid-state NMR, EPR and other methods. Solution NMR methods include the Nuclear Overhauser Effect (NOE) between peptide and membrane signals, residual dipolar couplings and the use of paramagnetic probes, either within the membrane-mimetic or in the solvent. The vast array of solid state NMR methods to study membrane-bound peptide orientation and localization includes the anisotropic chemical shift, PISA wheels, dipolar waves, the GALA, MAOS and REDOR methods and again the use of paramagnetic additives on relaxation rates. Paramagnetic additives, with their effect on spectral linewidths, have also been used in EPR spectroscopy. Additionally, the orientation of a peptide within a membrane can be obtained by the anisotropic hyperfine tensor of a rigidly attached nitroxide label. Besides these magnetic resonance techniques a series of other methods to probe the orientation of peptides in membranes has been developed, consisting of fluorescence-, infrared- and oriented circular dichroism spectroscopy, colorimetry, interface-sensitive X-ray and neutron scattering and Quartz crystal microbalance.
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The Probe Rules In Single Particle Tracking
Mathias P. Clausen and B. Christoffer Lagerholm
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00042]
Single particle tracking (SPT) enables light microscopy at a sub-diffraction limited spatial resolution by a combination of imaging at low molecular labeling densities and computational image processing. SPT and related single molecule imaging techniques have found a rapidly expanded use within the life sciences. This expanded use is due to an increased demand and requisite for developing a comprehensive understanding of the spatial dynamics of bio-molecular interactions at a spatial scale that is equivalent to the size of the molecules themselves, as well as by the emergence of new imaging techniques and probes that have made historically very demanding and specialized bio-imaging techniques more easily accessible and achievable. SPT has in particular found extensive use for analyzing the molecular organization of biological membranes. From these and other studies using complementary techniques it has been determined that the organization of native plasma membranes is heterogeneous over a very large range of spatial and temporal scales. The observed heterogeneities in the organization have the practical consequence that the SPT results in investigations of native plasma membranes are time dependent. Furthermore, because the accessible time dynamics, and also the spatial resolution, in an SPT experiment is mainly dependent on the luminous brightness and photostability of the particular SPT probe that is used, available SPT results are ultimately dependent on the SPT probes. The focus of this review is on the impact that the SPT probe has on the experimental results in SPT.
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Bilayer hydrophobic thickness and integral membrane protein function
Larisa E. Cybulski and Diego de Mendoza
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00043]
The influence of the lipid environment on the function of membrane proteins is increasingly recognized as crucial. Nevertheless, the molecular mechanisms underlying protein-lipid interactions remain obscure. Membrane lipid composition has a regulatory effect on membrane protein activity, and for a number of membrane proteins a clear correlation was found between protein activity and properties of the membrane bilayer such as fluidity.
Membrane thickness is an important property of a lipid bilayer. It is expected that hydrophobic thickness match the hydrophobic thickness of transmembrane segments of integral membrane proteins. Any mismatch between the hydrophobic thicknesses of the lipid bilayer and the protein would lead to some modification in either the structure of the protein or the structure of the bilayer, or both. Consequent rearrangements may result in changes in protein activity. Here we review the behavior of several transmembrane proteins whose activity is altered by hydrophobic core thickness.
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Quantification of protein-protein interactions within membranes by fluorescence correlation spectroscopy
Stephanie Bleicken, Miki Otsuki and Ana J. García-Sáez
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00044]
The characterization of interactions between membrane proteins as they take place within the lipid bilayer poses a technical challenge, which is currently very difficult and, in many cases, impossible to overcome. The recent development of a method based in the combination two-color fluorescence cross-correlation spectroscopy with scanning of the focal volume allows the detection and quantification of interactions between biomolecules inserted in biological membranes. This powerful strategy has allowed the quantitative analysis of diverse systems, such as the association between proteins of the Bcl-2 family involved in apoptosis regulation or the binding between a growth factor and its receptor during signaling. Here, we review the last developments to quantify protein/protein interactions in lipid membranes and focus on the use of fluorescence-correlation-spectroscopy approaches for that purpose.
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Novel Functions And Binding Mechanisms Of Carbohydrate-Binding Proteins Determined By Force Measurements
Deborah E. Leckband
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00045]
Cell surface carbohydrates are important targets for many cell surface receptors, and they mediate crucial biological processes ranging from pathogen infectivity to neutrophil adhesion to drug targeting. A central challenge is to identify relationships between lectin architecture and function that influence the adhesion strength, avidity, and kinetics of receptor-glycan bonds. This information is central both to understanding recognition mechanisms and to developing effective therapeutic agents for drug targeting or for preventing infection. Increasingly, force probes are used to assess structure activity relationships of both the glycan ligands and the receptors that bind them, as well as molecular mechanisms underlying binding and adhesion. This review describes recent advances in the use of different force measurement techniques to quantify receptor-glycan bond parameters, and to identify novel features of molecular mechanisms underlying recognition and adhesion. The examples discussed focus in particular on single bond rupture, surface force measurements, and micropipette manipulation. This review emphasizes the often-unique information obtained from studies of lectin interactions with carbohydrate ligands that complement more common structure determinations and solution binding studies.
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What can we learn from single molecule trajectories?
Verena Ruprecht, Markus Axmann, Stefan Wieser and Gerhard J. Schütz
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00046]
Diffusing membrane constituents are constantly exposed to a variety of forces that influence their stochastic path. Single molecule experiments allow for resolving trajectories at extremely high spatial and temporal accuracy, thereby offering insights into en route interactions of the tracer. In this review we discuss approaches to derive information about the underlying processes, based on single molecule tracking experiments. In particular, we focus on a new versatile way to analyze single molecule diffusion in the absence of a full analytical treatment. The method is based on comprehensive comparison of an experimental data set against the hypothetical outcome of multiple experiments performed on the computer. Since Monte Carlo simulations can be easily and rapidly performed even on state-of-the-art PCs, our method provides a simple way for testing various – even complicated – diffusion models. We describe the new method in detail, and show the applicability on two specific examples: firstly, kinetic rate constants can be derived for the transient interaction of mobile membrane proteins; secondly, residence time and corral size can be extracted for confined diffusion.
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Disordered proteins and network disorder in network descriptions of protein structure, dynamics and function. Hypotheses and a comprehensive review
Peter Csermely, Kuljeet Singh Sandhu, Eszter Hazai, Zsolt Hoksza, Huba J.M. Kiss, Federico Miozzo, Dániel V. Veres; Francesco Piazza and Ruth Nussinov
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00047]
During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into ‘cumulus-type’, i.e., those similar to puffy (white) clouds, and ‘stratus-type’, i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an ‘energy transfer’ mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by ‘multi-trajectories’; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach ‘rarely visited’ but functionally-related states. We also show the role of disorder in ‘spatial games’ of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks.
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The Role Of Intrinsically Disordered Regions In The Structure And Functioning Of Small Heat Shock Proteins
N.B. Gusev
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00048]
Small heat shock proteins (sHsp) form a large ubiquitous family of proteins expressed in all fila of living organisms. The members of this family have low molecular masses (13-43 kDa) and contain a conservative α-crystallin domain. This domain (about 90 residues) consists of several β-strands forming two β-sheets packed in immunoglobulin-like manner. The α-crystallin domain plays an important role in formation of stable sHsp dimers, which are the building blocks of the large sHsp oligomers. A large N-terminal domain and a short C-terminal extension flank the α-crystallin domain. Both the N-terminal domain and the C-terminal extension are flexible, susceptible to proteolysis, prone to posttranslational modifications, and are predominantly intrinsically disordered. Differently oriented N-terminal domains interact with each other, with the core α-crystallin domain of the same or neighboring dimers and play important role in formation of large sHsp oligomers. Phosphorylation of certain sites in the N-terminal domain affects the sHsp quaternary structure, the sHsp interaction with target proteins and the sHsp chaperone-like activity. The C-terminal extension often carrying the conservative tripeptide (I/V/L)-X-(I/V/L) is capable of binding to a hydrophobic groove on the surface of the core α-crystallin domain of neighboring dimer, thus affecting the plasticity and the overall structure of sHsp oligomers. The C-terminal extension interacts with target proteins and affects their interaction with the α-crystallin domain increasing solubility of the complexes formed by sHsp and their targets. Thus, disordered N- and C-terminal sequences play important role in the structure, regulation and functioning of sHsp.
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Understanding Pre-Structured Motifs (PreSMos) in Intrinsically Unfolded Proteins
Si-Hyung Lee, Do-Hyoung Kim, Joan J. Han, Eun-Ji Cha, Ji-Eun Lim, Ye-Jin Cho, Chewook Lee, and Kyou-Hoon Han
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00049]
Intrinsically unfolded proteins (IUPs) do not obey the golden rule of structural biology, 3D structure = function, as they manifest their inherent functions without resorting to three-dimensional structures. Absence of a compact globular topology in these proteins strongly implies that their ligand recognition processes should involve factors other than spatially well-defined binding pockets. Heteronuclear multidimensional (HetMulD) NMR spectroscopy assisted with a stable isotope labeling technology is a powerful tool for quantitatively investigating detailed structural features in IUPs. In particular, it allows us to delineate the presence and locations of pre-structured motifs (PreSMos) on a per-residue basis. PreSMos are the transient local structural elements that presage target-bound conformations and act as specificity determinants for IUP recognition by target proteins. Here, we present a brief chronicle of HetMulD NMR studies on IUPs carried out over the past two decades along with a discussion on the functional significance of PreSMos in IUPs.
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Comprehensive comparative assessment of in-silico predictors of disordered regions
Zhen-Ling Peng and Lukasz Kurgan
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00050]
The intrinsic disorder is relatively common in proteins, plays important roles in numerous cellular activities, and its prevalence was implicated in various human diseases. However, annotations of the disorder lag behind the rapidly increasing number of known protein chains. Last decade observed development of a relatively large number of in-silico methods that predict the disorder using the protein sequence as their input. We perform a first-of-its kind comprehensive empirical evaluation of the disorder predictors which is characterized by three novel aspects, (1) we evaluate the quality of the disorder predictions at the residue, segment, and chain levels; (2) we consider a large number of published and accessible to the end user predictors that are evaluated on a relatively big dataset with close to 500 proteins; and (3) we assess statistical significance of differences between the considered methods. Our study reveals that there is no universally superior predictor and that the top-performing methods are complementary. We show that while recent consensus-based predictors outperform other considered methods for the residue-level predictions, some older methods perform better for the prediction of the disordered segments. Our analysis indicates that certain predictors are biased to under-predict the disorder, while some other solutions tend to over-predict the number of the disordered residues. We also evaluate the utility of the predicted residue-level disorder for prediction of proteins with long disordered segments and prediction of the chain-level disorder content. Lastly, we provide recommendations concerning development of a new generation of consensus-based methods and specialized methods for improved prediction of the disorder content.
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How random are intrinsically disordered proteins? A small angle scattering perspective
Veronique Receveur-Bréchot and Dominique Durand
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00051]
While the crucial role of intrinsically disordered proteins (IDPs) in the cell cycle is now recognized, deciphering their molecular mode of action at the structural level still remains highly challenging and requires a combination of many biophysical approaches. Among them, small angle X-ray scattering (SAXS) has been extremely successful in the last decade and has become an indispensable technique for addressing many of the fundamental questions regarding the activities of IDPs. After introducing some experimental issues specific to IDPs and in relation to the latest technical developments, this article presents the interest of the theory of polymer physics to evaluate the flexibility of fully disordered proteins. The different strategies to obtain 3-dimensional models of IDPs, free in solution and associated in a complex, are then reviewed. Indeed, recent computational advances have made it possible to readily extract maximum information from the scattering curve with a special emphasis on highly flexible systems, such as multidomain proteins and IDPs. Furthermore, integrated computational approaches now enable the generation of ensembles of conformers to translate the unique flexible characteristics of IDPs by taking into consideration the constraints of more and more various complementary experiment. In particular, a combination of SAXS with high-resolution techniques, such as x-ray crystallography and NMR, allow us to provide reliable models and to gain unique structural insights about the protein over multiple structural scales. The latest neutron scattering experiments also promise new advances in the study of the conformational changes of macromolecules involving more complex systems.
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HSF transcription factor family, heat shock response, and protein intrinsic disorder
Sandy D. Westerheide, Rachel Raynes, Chase Powell, Bin Xue and Vladimir N. Uversky
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00052]
Intrinsically disordered proteins are highly abundant in all kingdoms of life, and several protein functional classes, such as transcription factors, transcriptional regulators, hub and scaffold proteins, signaling proteins, and chaperones are especially enriched in intrinsic disorder. One of the unique cellular reactions to protein damaging stress is the so-called heat shock response that results in the upregulation of heat shock proteins including molecular chaperones. This molecular protective mechanism is conserved from prokaryotes to eukaryotes and allows an organism to respond to various proteotoxic stressors, such as heat shock, oxidative stress, exposure to heavy metals, and drugs. The heat shock response-related proteins can be seeing in norm (e.g., during the cell growth and development) or be induced by various pathological conditions, such as infection, inflammation, and protein conformation diseases. The initiation of the heat shock response is manifested by the activation of a series of heat shock transcription factors (HSFs). This review analyzes the abundance and functional roles of intrinsic disorder in various heat shock transcription factors and clearly shows that the heat shock response requires HSF flexibility to be more efficient.
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Disruption Of The V-Atpase Functionality As A Way To Uncouple Bone Formation And Resorption – A Novel Target For Treatment Of Osteoporosis
Thudium CS, Jensen VK, Karsdal MA and Henriksen K
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00053]
The unique ability of the osteoclasts to resorb the calcified bone matrix is dependent on secretion of hydrochloric acid. This process is mediated by a vacuolar H+ ATPase (V-ATPase) and a chloride-proton antiporter.
The structural subunit of the V-ATPase, a3, is highly specific for osteoclasts, and mutations in a3 lead to infantile malignant osteopetrosis, a phenomenon characterized by increased bone mass, an increased number of non-resorbing osteoclasts, and a complete lack of bone resorption. Importantly, these individuals have normal or even increased osteoblast numbers and bone formation suggesting that the osteoclasts, but not their resorptive capability, relay an anabolic signal, and, hence, that bone formation can be uncoupled from bone resorption when the a3 subunit is eliminated by mutations, or possibly by pharmacological intervention.
The pharmacological profile of the a3 subunit as a highly specific target with a mode of action profile augmenting uncoupling and sustained bone formation, as derived from osteopetrotic patients and mice, highlights the relevance of the V-ATPase in future osteoporosis drug development. However, as illustrated by numerous attempts at developing specific inhibitors of the osteoclastic V-ATPase it is a very difficult target to work with, and an inhibitor possessing the desired profile remains elusive, although highly promising approaches recently have been launched.
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Targeting Reversible Disassembly as a Mechanism of Controlling V-ATPase Activity
Patricia M. Kane
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00054]
Vacuolar proton-translocating ATPases (V-ATPases) are highly conserved proton pumps consisting of a peripheral membrane subcomplex called V1, which contains the sites of ATP hydrolysis, attached to an integral membrane subcomplex called V0, which encompasses the proton pore. V-ATPase regulation by reversible dissociation, characterized by release of assembled V1 sectors into the cytosol and inhibition of both ATPase and proton transport activities, was first identified in tobacco hornworm and yeast. It has since become clear that modulation of V-ATPase assembly level is also a regulatory mechanism in mammalian cells. In this review, the implications of reversible disassembly for V-ATPase structure are discussed, along with insights into underlying subunit-subunit interactions provided by recent structural work. Although initial experiments focused on glucose deprivation as a trigger for disassembly, it is now clear that V-ATPase assembly can be regulated by other extracellular conditions. Consistent with a complex, integrated response to extracellular signals, a number of different regulatory proteins, including RAVE/rabconnectin, aldolase and other glycolytic enzymes, and protein kinase A have been suggested to control V-ATPase assembly and disassembly. It is likely that multiple signaling pathways dictate the ultimate level of assembly and activity. Tissue-specific V-ATPase inhibition is a potential therapy for osteoporosis and cancer; the possibility of exploiting reversible disassembly in design of novel V-ATPase inhibitors is discussed.
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Regulation of V-ATPase Expression in Mammalian Cells
Beth S. Lee
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00055]
Vacuolar ATPases (V-ATPases) are large multisubunit complexes that actively transport protons across cellular membranes to acidify intracellular compartments, thereby serving a critical housekeeping function. In addition, V-ATPases are also expressed on the plasma membrane of cell types such as kidney epithelia and osteoclasts, which require high levels of proton secretion to perform their specialized activities. This multiplicity of function is achieved by the expression of numerous V-ATPase subunit isoforms that are mixed and matched to produce complexes required for each cellular activity. Multiple regulatory mechanisms are necessary to allow coordinated expression of V-ATPase subunit proteins involved in both housekeeping and specialized functions. This review will summarize studies during the last two decades that have revealed transcriptional and post-transcriptional controls that govern expression of V-ATPase subunits. These studies are beginning to elucidate overarching mechanisms that permit coordinated expression of ubiquitous subunits while directing tissue-specific expression of others.
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V-ATPase Subunit Interactions: The Long Road to Therapeutic Targeting
Norbert Kartner and Morris F. Manolson
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00056]
Over the last three decades, V-ATPases have emerged from the obscurity of poorly understood membrane proton transport phenomena to being recognized as ubiquitous proton pumps that underlie vital cellular processes in all eukaryotic and many prokaryotic cells. These exquisitely complex molecular motors also engage in diverse specialized roles contributing to development, tissue function and pH homeostasis within complex organisms. Increasingly, mutations and misappropriation of V-ATPase function have been linked to diseases, ranging from sclerosing bone pathologies and renal tubular acidosis to bone-loss disorders and cancer metastasis. Much remains to be learned about the details of V-ATPase cell and molecular biology; nevertheless, interest in V-ATPases as potential therapeutic targets has burgeoned in recent years. In this review, we present a history of our involvement and contributions to the understanding of V-ATPase structure and function and our nascent and ongoing contributions to translating the knowledge gained from basic research on the nature of V-ATPases into tools for drug discovery. We focus here primarily on the treatment of bone-loss pathologies, like osteoporosis, and present proof-of-concept for a drug screening strategy based on targeting a3–B2 subunit interactions within the V-ATPase complex.
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Novel insights into V-ATPase functioning: distinct roles for its accessory subunits ATP6AP1/Ac45 and ATP6AP2/(pro)renin receptor
Eric J.R. Jansen and Gerard J.M Martens
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00057]
The vacuolar (H+)-ATPase (V-ATPase) is a universal proton pump and its activity is required for a variety of cell-biological processes such as membrane trafficking, receptor-mediated endocytosis, lysosomal protein degradation, osteoclastic bone resorption and maintenance of acid-base homeostasis by renal intercalated cells. In neuronal and neuroendocrine cells, the V-ATPase is the major regulator of intragranular acidification which is indispensable for correct prohormone processing and neurotransmitter uptake. In these specialized cells, the V-ATPase is equipped with the accessory subunits ATP6AP1/Ac45 and ATP6AP2/(pro)renin receptor. Recent studies have shown that Ac45 interacts with the V0-sector of the V-ATPase complex, thereby regulating the intragranular pH and Ca2+-dependent exocytotic membrane fusion. Thus, Ac45 can be considered as a V-ATPase regulator in the neuroendocrine secretory pathway. ATP6AP2 has recently been found to be identical to the (pro)renin receptor and has a dual role: (i) in the renin-angiotensin system that also regulates V-ATPase activity; (ii) acting as an adapter by binding to both the V-ATPase and the Wnt receptor complex, thereby recruiting the receptor complex into an acidic microenvironment. We here provide an overview of the two V-ATPase accessory subunits as novel key players in V-ATPase regulation. We argue that the accessory subunits are candidate genes for V-ATPase-related human disorders and promising targets for manipulating V-ATPase functioning in vivo.
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Vacuolar H+-ATPase signaling pathway in cancer
Souad R. Sennoune and Raul Martínez-Zaguilán
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00058]
Up-regulated aerobic glycolysis is a hallmark of malignant cancers. Little is understood about the reasons why malignant tumors up-regulate glycolysis and acidify their microenvironment. Signaling pathways involved in glucose changes are numerous. However, the identity of the internal glucose signal remains obscure. In this review we address the question of the significance of vacuolar proton ATPase (V-ATPase) and its relationship to up-regulated glycolysis in tumors. We know that glycolysis is extremely sensitive to changes in pH. Importantly, the V-ATPase activity is sensitive to glucose availability. Therefore, we propose that pH acts as the glucose signal via the V-ATPase that responds to changes in intracellular pH and acts as a sensor. We hypothesize that the increase in glycolysis leads to intracellular acidification and activates the V-ATPase to maintain a more alkaline intracellular pH in tumors by up-regulating glycolysis. This review attempts to provide a comprehensive description of the current knowledge about the role of V-ATPase in cancer, highlighting its role as a key player in the pH signaling pathway.
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Rational identification of enoxacin as a novel V-ATPase-directed osteoclast inhibitor
Edgardo J. Toro, David A. Ostrov, Thomas J. Wronski and L. Shannon Holliday
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00059]
Binding between vacuolar H+-ATPases (V-ATPases) and microfilaments is mediated by an actin binding domain in the B-subunit. Both isoforms of mammalian B-subunit and that of yeast bind microfilaments with a high affinity. A similar actin-binding activity has been demonstrated in the B-subunit of yeast. A conserved “profilin-like” domain in the B-subunit mediates this actin-binding activity, named due to its sequence and structural similarity to an actin-binding surface of the canonical actin binding protein profilin. Subtle mutations in the “profilin-like” domain eliminate actin binding activity without disrupting the ability of the altered protein to associate with the other subunits of V-ATPase to form a functional proton pump. Analysis of these mutated B-subunits suggests that the actin-binding activity is not required for the “housekeeping” functions of V-ATPases, but is important for certain specialized roles. In osteoclasts, the actin-binding activity is required for transport of V-ATPases to the plasma membrane, a prerequisite for bone resorption. A virtual screen led to the identification of enoxacin as a small molecule that bound to the actin-binding surface of the B2-subunit and competitively inhibited B2-subunit and actin interaction. Enoxacin disrupted osteoclastic bone resorption in vitro, but did not affect osteoblast formation or mineralization. Recently, enoxacin was identified as an inhibitor of the virulence of Candida albicans and more importantly of cancer growth and metastasis. Efforts are underway to determine the mechanisms by which enoxacin and other small molecule inhibitors of B2 and microfilament binding interaction selectively block bone resorption, the virulence of Candida, cancer growth, and metastasis.
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Recent progress in computational approaches to studying the M2 proton channel and its implication to drug design against influenza viruses
Qi-Shi Du and Ri-Bo Huang
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00060]
For quite a long period of time in history, many intense efforts have been made to determine the 3D (three-dimensional) structure of the M2 proton channel. The reason why the M2 proton channel has attracted so many attentions is because (1) it is the key for really understanding the life cycle of influenza viruses, and (2) it is indispensable for conducting rational drug design against the flu viruses. Recently, the long-sough 3D structures of the M2 proton channels for both influenza A and B viruses were consecutively successfully determined by the high-resolution NMR spectroscopy (Schnell J.R. and Chou, J.J., Nature, 2008, 451: 591-595; Wang, J., Pielak, R.M., McClintock, M.A., and Chou, J.J., Nature Structural & Molecular Biology, 2009,16: 1267-1271). Such a milestone work has provided a solid structural basis for in-depth understanding the action mechanism of the M2 channel and rationally designing effective drugs against influenza viruses. This review is devoted to, with the focus on the M2 proton channel of influenza A, addressing a series of relevant problems, such as how to correctly understand the novel allosteric inhibition mechanism inferred from the NMR structure that is completely different from the traditional view, what the possible impacts are to the previous functional studies in this area, and what kind of new strategy can be stimulated for drug development against influenza.
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The archaeal Sac10b protein family: conserved proteins with divergent functions
Jinsong Xuan and Yingang Feng
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00061]
Here we review the present state of structural and functional studies of the Sac10b protein family, a class of highly conserved 10 kDa nucleic acid-binding proteins in archaea. Based on biochemical and structural studies, these proteins were originally assigned a role in the structural organization of chromatin; Sac10b proteins of hyperthermophilic archaea, for example, showed tight, unspecific DNA binding. More recently, however, Sac10b proteins of mesophilic archaea were found to interact preferentially with specific DNA sequences thereby affecting the expression of distinct genes. Furthermore, Sac10b proteins of hyperthermophilic, thermophilic and mesophilic archaea were also shown to bind to RNA with distinct affinities and specificities but functional consequences of RNA binding of these proteins, besides perhaps RNA stabilization, have not yet been observed. To better understand the physiological meaning of the various interactions of Sac10b proteins with nucleic acids, future work should concentrate on elucidating the molecular structures of complexes of Sac10b proteins of hyperthermophilic and mesophilic archaea with DNA and RNA. In addition, existing and new X-ray and NMR structures of individual hyperthermophilic Sac10b proteins may represent very good models for introducing thermostability especially in enzymes for industrial use.
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Editorial: Efficient Strategies for Signalling Pathway Mining
Qingfeng Chen and Baoshan Chen
[BSP/CPPS/E-Pub/00062]
Systems biology is an interdisciplinary field within the life sciences that focuses on the systematic study of complex interactions in biological systems. The study of pathways is an important subfield of systems biology that is concerned with pathway algorithms, ontologies, visualizations, and databases. A pathway comprises one or more processes, each of which begins with input signals, uses various combinations of other input signals, such as cofactors, activators and inhibitors, and ends with output that exhibits functions. The most critical issue to be solved by researchers is the development of efficient algorithms for storage, modeling and analysis of complicated pathway data. It is critical to develop innovative approaches that aim at the sequential and cumulative actions of genetically distinct, but functionally related objects. However, there is a lack of intelligent technological strategies to tackle high-dimensional pathway data. There have been considerable efforts to develop effective methods for integration of biological databases and identification of kinase and gene-mediated pathways. It is essential that these aspects are understood and integrated into new algorithms and tools used in pathway data collection, analysis and management.
The importance of signalling pathways.
Cells depend on a large number of clearly defined signalling pathways [1] to regulate their activity. These signalling systems not only control development and regulate specific processes in adult cells, such as metabolism, proliferation, information processing in neurons and sensory perception. In general, cell signalling pathways coordinate with each other to regulate many different cellular processes. This intimate relationship between cell signalling and biology leads to valuable insights into the underlying genetic and phenotypic defects responsible for many of the major diseases of numerous species. A large number of diseases are caused by defects in signalling pathways, such as interference by pathogenic organisms and viruses. Most of the serious diseases in humans, such as heart disease and diabetes, appear to arise from subtle phenotypic modifications of pathways. This enables discovery of new ways of correcting many disease states.
MAPK/ERK pathway and cAMP dependent pathway are two major signalling pathways. The former is able to alter the translation of mRNA to proteins and regulates the activities of several transcription factors. By altering the levels and activities of transcription factors, MAPK gives rises to altered transcription of genes that are important for the cell cycle. The latter can activate enzymes and regulate gene expression. Many different cell responses are mediated by cAMP, such as increase in heart rate, and breakdown of glycogen and fat.
Many studies have been performed for characterizating signalling pathways. Nevertheless, the criteria by cells to determine the specific pathways and their regulatory networks are still unclear. It is thus important to explore the mechanisms that different pathways are combined to control a set of cellular processes, especially interaction between involved components, such as gene expression and phosphorylation for mediating enzyme inhibition, regulating protein-protein interaction and protein degradation. A number of pathway databases and repositories have been generated for management of the information of pathways together with their molecular components and reactions. It is critical to apply advanced data mining techniques for extraction of interesting knowledge about signalling pathways.
The need for efficient data mining strategies.
It is observed that the regulatory signals usually function in complex networks, characterized by an abundance of activators [2] or inhibitors. Further, multiple specific inhibitors of all conserved signal pathways have been identified as modulators in organ or tissue development, regulators in metabolism, and cause of diseases. All these findings generate tremendous and diverse pathway data with respect to varied species. The storage, management and deep analysis of the data have been a big challenge issue to comprehensively understand the complex regulatory networks during evolution.
To meet these requirements, we must develop innovative approaches aimed at the sequential and cumulative actions of genetically distinct, but functionally related objects. However, there is a lack of intelligent technology strategies to tackle the high-dimensional pathway data. There have been significant efforts for integration of biological databases and discovery of protein kinase and gene-mediated kinase pathways. It is essential that these aspects are understood and integrated into new algorithms and tools used in pathway data collection, analysis and management. These require a systematic way of combining technologies, signalling pathways and genomic data.
This special issue thus includes the original articles that propose solutions for this exciting new computational biology paradigm. The articles in this special issue focus on practical technologies and applications for pathway data analysis. We have tried to include articles from different research fields, such as plant biology and computer science, and different topics, such as protein-protein interaction and gene expression, which range over key issues of signalling pathways.
Contributions.
The included articles can be divided into three main topics: protein regulation, gene regulatory network, and gene expression.
Protein regulation. Lili Liu, Lin Bai, Cong Luo, Donglin Huang, Ming Chen propose a new approach for Oryza sativa proteome annotation. Many common gene products with great potential in regulating specific physiological and biochemical events within various rice tissues/organs are found. This article provides new strategies and informative resources for rice proteome research.
Jian Ren, Xinjiao Gao, Zexian Liu, Jun Cao, Qian Ma, Yu Xue summarize the leading edges on computational analysis of phosphoproteomics, including discovery of phosphorylation motifs from phosphoproteomics data, systematic modeling of phosphorylation network, analysis of genetic variation that influences phosphorylation, and phosphorylation evolution.
Simone Marini, Qian Xu, Qiang Yang develop a novel integrated approach to predicting protein-protein interaction based on sequence alignment by jointly using a k-Nearest Neighbor classifier (SA-kNN) and a Support Vector Machine (SVM). The SA-kNN makes use of the similarity of two protein pairs through alignment.
Gene regulatory network. Ederson Akio Kido, Ana Maria Benko-Iseppon et. al apply an approach evaluating biotic and abiotic stresses to generate over 13 million cowpea SuperSAGE tags available from leaves/roots of plants. This allows the identification of a comprehensive panel of the differentially expressed kinases under biotic and abiotic stresses in a non model plant as cowpea.
Qi Liu, Yi Yang, Yixue Li, and Zili Zhang develop a global and threshold-free algorithm for inferring regulatory networks by the integration of ChIP-chip and expression data. They demonstrate an improved functional coherence of TMs and TF (transcription factor)-target predictions, and demonstrated its ability to discover unexpected biological processes that TFs may be involved in and to also identify interesting novel combinations of TFs.
Gene expression. Shang Gao, Jia Zeng, Abdallah M. ElSheikh, Ghada Naji, Reda Alhajj, Jon Rokne, and Douglas Demetrick investigate a particular set of genes that reside on the community boundaries in gene co-expression networks. This shows the prospective of social network mechanisms in biological data analysis, and demands for new data mining strategies implied by witnessing social behaviors in gene expression analysis.
Conclusions.
The signalling pathways have become a critical research filed in post genome era since they play central roles in many cellular functions. Thus, the study of characterized pathways and their components is not only a big challenge and a good opportunity for researchers. The inherent complexity and diversity of system biology demand for collaboration of scientists from biology, computer science and mathematics. This gives rise to promising and challenging research issues for signalling pathways described in this special issue.
• Protein regulation, including protein-protein interaction network, annotation and analysis of large scale proteome, phosphorylation sites.
• Gene regulatory network, including genes related stress-resistance mechanisms and gene transcriptional regulatory network.
• Gene expression, including social behaviors in gene expression analysis.
Acknowledgements
The work reported in this paper was partially supported by the National Natural Science Foundation of China Project 60973074 and 30960203.
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Editorial: Membrane Proteins, a Biophysical Perspective
[BSP/CPPS/E-Pub/00063]
One third of the genome of any organism encodes intrinsic membrane proteins. Along with peripheral membrane proteins and proteins that only transiently interact with the membrane, these molecules are hard to study but of great importance to understand both the normal and pathological life of the cell. Biophysical methods are of the utmost importance when trying to unravel the molecular details of processes that have been described by biochemical methods. In particular, biophysical methods are particularly well suited to study membrane proteins.
We present here a sample of current understanding of membrane-associated proteins function and structure, studied by state-of-the-art biophysical, biochemical and modeling techniques. In the manuscript by Borioli, a novel hypothesis about immediate-early proto-oncoproteins structure-function relationship in regard to their ability to transiently interact with membranes is discussed. Colombo and Fasana discuss recent data regarding the unassisted insertion of tail-anchored proteins into the membrane of the endoplasmic reticulum. Cybulski and de Mendoza’s manuscript stresses the importance of bilayer thickness for membrane protein function.
We present four manuscripts that use single-molecule methods to understand membrane-protein, protein-protein and carbohydrate-protein interactions in membranes, highlighting the increasing importance of these techniques for the development of the field. The manuscript by Bleicken and co-workers describes the use of Fluorescence Correlation Spectroscopy to address the experimentally challenging study of the interaction between membrane proteins as they find each other in the bilayer. Clausen and Lagerholm discuss in detail the effect of the probe size and photophysics in the results obtained from single particle tracking of probes attached to membrane components. Ruprecht and co-workers present a new way to analyze single molecule diffusion in the absence of a full analytical treatment. The method is based on the comparison of an experimental data set against the outcome of multiple experiments performed on the computer by Monte Carlo simulations. The manuscript by Leckband emphasizes the unique information obtained by force measurements on the binding mechanisms of lectins to cell-surface carbohydrates. Finally, Banning and co-authors summarize the recent findings on Reggie/Flotillin mechanisms of association in membrane microdomains and in oligomers.
I am excited to deliver this issue that tackles a diverse set of developments in the area of the biophysics of membrane-associated proteins. I hope that it will be interesting for biophysicists, cell biologists and biochemists.
I would like to thank all the authors who made this issue possible. I am in great debt to the anonymous reviewers from around the world who delivered timely and useful comments to the authors. I used here a double-blind method which I hope contributed to make the reviewing process more objective than it usually is. I also express my gratitude to the Editor-in-Chief Prof. Ben M. Dunn for his invitation and kind support that resulted in the successful completion of this special issue. Last but not least, a message of gratitude to the Assistant Manager of Bentham Science Publishers Sadia Rafique, who helped me with great efficiency to put together all the last details of the issue.
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The V-ATPase as a Target for Antifungal Drugs
Yongqiang Zhang and Rajini Rao
[FULL-TEXT INQUIRY] [BSP/CPPS/E-Pub/00064]
The ubiquitous and essential V-ATPase is a worthy chemotherapeutic target in the escalating battle against invasive fungal infections. Pathogenic fungi require optimum V-ATPase function for secretion of virulence factors, induction of stress response pathways, hyphal morphology and homeostasis of pH and other cations in order to successfully survive within and colonize the host. This review discusses why impairment of V-ATPase activity confers multidrug sensitivity and loss of virulence. Recent evidence points to the V-ATPase as a novel downstream target of the azole class of antifungals that inhibit the biogenesis of ergosterol. Depletion of ergosterol from vacuolar membranes led to progressive alkalization of yeast vacuoles, loss of V-ATPase activity and growth inhibition that could be rescued by exogenous ergosterol feeding. Other studies point to a critical role for sphingolipids, phospholipids and cardiolipin in V-ATPase function. Thus, drugs that inhibit the V-ATPase directly, or indirectly by modulating the membrane milieu, can profoundly affect fungal viability and virulence. These findings justify a systematic screen for fungal specific V-ATPase inhibitors or membrane active compounds that can be used in antifungal chemotherapy.
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