Title:Binding Specificity and Local Frustration in Structure-based Drug Discovery
Volume: 33
Issue: 8
Author(s): Zhiqiang Yan*, Yuqing Li, Ying Cao, Xuetao Tao, Jin Wang*Yongsheng Jiang*
Affiliation:
- Joint Research Centre on Medicine, the Affiliated Xiangshan Hospital of Wenzhou Medical University, Ningbo, Zhejiang, China
- Center for Theoretical Interdisciplinary Sciences, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
- Joint Research Centre on Medicine, the Affiliated Xiangshan Hospital of Wenzhou Medical University, Ningbo, Zhejiang, China
- Center for Theoretical Interdisciplinary Sciences, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
- Department of Chemistry and Physics, State University of New York at Stony Brook, Stony Brook, NY, USA
- Joint Research Centre on Medicine, the Affiliated Xiangshan Hospital of Wenzhou Medical University, Ningbo, Zhejiang, China
Keywords:
Energy landscape, biomolecular recognition, binding specificity, local frustration, drug screening, binding site, cryptic site.
Abstract: Evolution has optimized proteins to balance stability and function by reducing
unfavorable energy states, leading to regions of flexibility and frustration on protein
surfaces. These locally frustrated regions correspond to functionally important areas,
such as active sites and regions for ligand binding and conformational plasticity. Typical
strategies of structure-based drug discovery primarily concentrate on enhancing the binding
affinity during compound screening and target identification. However, this often
overlooks the binding specificity, which is critical for distinguishing specific binding
partners from competing ones and avoiding off-target effects. According to the energy
landscape theory, optimization of the intrinsic binding specificity involves globally minimizing
the frustrations existing in the biomolecular interactions. Recent studies have demonstrated
that identifying local frustrations provides a promising approach for screening
more specific compounds binding with targets, and quantifying binding specificity
complements typical strategies that focus on binding affinity only. This review explores
the principles and strategies of computationally quantifying the binding specificity and
local frustrations and discusses their applications in structure-based drug discovery.
Moreover, given the advancements of artificial intelligence in protein science, this review
aims to motivate the integration of AI and available approaches in quantifying the
binding specificity and local frustration. We expect that an AI-powered prediction model
will accelerate the drug discovery process and improve the success rate of hit compounds.
