Frontiers in Computational Chemistry

G protein-coupled receptors (GPCRs) represent the most important family of drug targets to date. However, state-of-the-art experimental procedures, able to characterize in deep both GPCR modulation in health and disease and the molecular mechanisms of drug action at these receptors, have provided a more nuanced picture than previously expected. Several aspects of GPCR function, which are currently being characterized, clarify some regulatory processes regarding these receptors and, at the same time, introduce novel levels of complexity which should be taken into consideration for rational drug design. In this scenario, computational approaches can help in several ways rationalize the increasing amount of data on GPCRs and their ligands. On the one hand, a set of databases devoted to these receptors provide excellent starting points for data mining. On the other, exploitation of the ever-increasing ligand and structure-based information by novel computational techniques can help addressing emerging questions in the GPCR field. Some of these questions comprise the refined modulation of GPCR signaling states by biased agonists, the exploitation of GPCR oligomers as drug targets, the analysis of polypharmacology in GPCR ligands, the development of strategies for receptor deorphanization or the prediction of off-target interactions of known drugs targeting these receptors. In this chapter, we will cover some of these strategies for knowledge-based rational design for GPCRs and will discuss the main hurdles which they may need to overcome to yield novel, safer and more efficacious drugs possessing polished mechanisms of action.

Drug repurposing/reprofiling has attracted considerable attention during the last decade. The object of such approach is to discover second or further medical uses of known chemicals, i. e. targeting existing, withdrawn or abandoned drugs, or yet to be pursued clinical candidates to new disease areas. Recently (2011-2012), the US and UK governments launched public-private joint initiatives towards finding new uses of previously shelved compounds (drug rescue). While in the past repurposing emerged from serendipitous findings and/or from rational exploitation of drug side-effects (e.g. sildenafil, aspirin), the current tendency in the drug development field focuses on knowledge-based drug repurposing, particularly, computer-aided repositioning approaches. The present chapter reviews different cheminformatic and bioinformatic applications, as well as high-throughput literature analysis, oriented to the discovery of new medical uses of known drugs. Applications of such strategies to the discovery of innovative medications for neglected or rare diseases are discussed. Finally, we also review publicly available resources (e.g. chemical libraries) valuable for reprofiling.

Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) and Molecular Mechanics Generalized Born Surface Area (MM-GBSA) are widely used methods for the prediction of binding free energies in drug design/discovery. Indeed, their computational efficiency makes them applicable also within virtual screening protocols. Thus, in order to be useful for drug design/discovery purposes, MM-PBSA and MM-GBSA binding energy predictions have to correlate well with experimental activities. Nowadays the global effort to find a way to improve the predictivity of MMPBSA/ GBSA calculations is also focused on the solvation term by using various approaches. This chapter reports on the application of MM-PBSA/GBSA methods within the process of drug discovery and, in particular, on strategies that can be applied to improve the correlation between MM-PBSA/GBSA predicted binding affinities and experimental pharmacological activities by acting on the way the solvent is treated in such calculations. Indeed, in PB and GB models, the solvent is described as a continuous medium with a fixed dielectric constant (i.e. ε = 80 for water), while a low internal dielectric constant is assigned to the solute (generally εin = 1 or 2 for proteins). However, the default approach could in some cases lead to a weak correlation between predicted binding free energies and experimental data. The aim of this chapter is to present and exemplify the ways to improve the prediction of ligand binding affinity by acting on the solvation term. Different methods are observed in the literature, e.g. tuning the εin value depending on the features of the binding site, including a selection of explicit water molecules in order to better describe the solute-solvent interactions, tuning the grid size in PB calculations and/or using different PB solvers, or modifying the non-polar term of the solvation free energy. The pros and cons of the above mentioned methods will be critically discussed in order to help the reader in choosing the most performing protocol in terms of both calculation time and prediction quality, depending on the molecular system under evaluation.

The expanding knowledge of the critical roles played by protein-protein interactions in cell proliferation, differentiation and apoptosis has highlighted proteinprotein interfaces as promising therapeutic targets for the treatment of various human diseases. However, targeting protein-protein interfaces is considered a particularly challenging task as protein interfaces are usually large and featureless, and lack welldefined cavities or binding contacts for small molecule recognition. Furthermore, the flexibility of protein-protein interfaces may lead to the formation of transient binding pockets that may be absent in the static structure of the free protein target or the proteinprotein complex. Despite these inherent challenges, virtual screening has recently emerged as a powerful technique complementing traditional high-throughput screening technologies in identifying new protein-protein interaction modulators. The rapid virtual screening of chemical libraries could weed out non-binding candidates in silico, thereby greatly reducing the operational costs associated with chemical synthesis and in vitro screening. This review aims to provide an introductory framework for the use of virtual screening in drug discovery and serves to highlight successful examples of the identification of novel protein-protein interaction modulators by virtual screening techniques.

Today biology is overwhelmed with ‘big data’, amassed from genomic projects carried out in various laboratories around the world using efficient high throughput technologies. Biologists are co-opting mathematical and computational techniques developed to address these data and derive meaningful interpretations. These developments have led to new disciplines: systems and synthetic biology. To explore these two evolving branches of biology one needs to be familiar with technologies such as genomics, bioinformatics and proteomics, mathematical and computational modeling techniques that help predict the dynamic behavior of the biological system, ruling out the trial-and-error methods of traditional genetic engineering. Systems and synthetic biology have developed hand-in-hand towards building artificial biological devices using engineered biological units as basic building blocks. Systems biology is an integrated approach for studying the dynamic and complex behaviors of biological components, which may be difficult to interpret and predict from properties of individual constituents making up the biological systems. While, synthetic biology aims to engineer biologically inspired devices, such as cellular regulatory circuits that do not exist in nature but are designed using well characterized genes, proteins and other biological components in appropriate combinations to perform a desired function. This is analogous to an electronic circuit board design that is fabricated using well characterized electrical components such as resistors, capacitors and so on. The in silico abstractions and predictions should be tightly linked to experimentation to be proved in vitro and in vivo systems for their successful applications in biotechnology. This chapter focuses on mathematical approaches and computational tools available to engineer biological regulatory circuits and how they can be implemented as next generation therapeutics in infectious disease.

The computational study of biologically active molecules plays important roles in drug development, as it provides information on molecular properties which, in turn, determine the biological activities of compounds. Within a living organism, molecules are within a medium and, therefore, their activity is exerted in a medium. Because of this, knowing how the presence of a medium influences the properties of a given molecule is important for drug development. This chapter aims at providing a comprehensive overview of the aspects relevant to the computational study of biologically active compounds in a medium. It outlines the main models currently utilised to take into account solute-solvent interactions and the solvent effects on the molecular properties of the solute, considering also the information abilities and limitations of each model and the challenges for further research. It discusses relevant criteria for the selection of the preferable solvents to consider in the study of a given molecule. Information, analyses and discussions are extensively supported by the consideration of examples from literature and from the authors’ direct experience.

The UNited RESidue (UNRES) force field has been developed for over two decades. This force field has been derived carefully as a potential of mean force of the system studied, which is further expressed in terms of the Kubo cluster-cumulant functions. New terms in the energy function to improve loop structures have been introduced recently. On the other hand, new concept was developed, in which wave-analysis physics is applied to the protein folding problem. At present, the energy function is based on the Landau Hamiltonian, the minima of which are stable conformations of protein fragments; these minima are obtained as kink solutions of the Discrete Nonlinear Schrödinger Equation. The parameters of the Hamiltonian have been obtained by statistical analysis of known protein structures. The unique feature of this approach is that the curvature description is sufficient for protein folding without any long-distance interactions other than the excluded-volume interactions. The combination of those two methodologies - molecular dynamics with the use of physics-base UNRES force field and the kink approach have been applied to study the flexibility and movement of the kinks as well as their formation and disappearance in the folding process.

Computational methods relying on first principles are fundamental for dissecting basic physicochemical properties of biological systems and unveiling mechanistic details that are often silent to experiments. The tireless improvement of theoretical schemes for molecular modelling and simulations, coupled to the increasing computational power of novel architectures and integrated with available experimental inputs, allows today exploring the functioning of biological systems with unprecedented accuracy. Indeed, molecular simulations at both the quantum mechanics and molecular mechanics levels are nowadays able to dissect with high confidence the structural and dynamical features of large systems in native-like conditions, up to the point that their mode of action can be modulated in a controlled fashion. These computational chemistry strategies are particularly appealing when applied to pharmaceutically relevant targets. In this chapter, we will present recent successes of computational investigations applied to a broad variety of biochemical systems that are promising or validated targets for drug discovery. In particular, we will show how molecular modelling at the quantum mechanics level is key for revealing the mechanistic details of catalysis in bacterial and viral metallo-enzymes. We will continue by discussing how accurate molecular mechanics-based free energy calculations can provide a new quantitative description of the function of systems of relevance for multidrug resistance in bacteria. In the final part of the chapter, we will show examples where computational and medicinal chemistry is fully integrated with structural and biochemical data to study function and inhibition of target enzymes implicated in cancer and other inflammatory-related diseases. The final goal of these studies is to develop new molecular entities potentially endowed with a desired pharmacological activity. This chapter will therefore define the contribution of emerging approaches and recent advances in the field of computational chemistry for translating the atomic-level understanding of complex biological phenomena into useful information to progress in molecular medicine.