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Computational Approaches to the Chemical Equilibrium Constant in Protein‐ligand Binding
Author(s) -
MontalvoAcosta Joel José,
Cecchini Marco
Publication year - 2016
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201600052
Subject(s) - computer science , computation , limit (mathematics) , pipeline (software) , interpretation (philosophy) , drug discovery , constant (computer programming) , ligand (biochemistry) , quantitative structure–activity relationship , protein ligand , computational chemistry , statistical physics , chemistry , mathematics , algorithm , machine learning , physics , mathematical analysis , biochemistry , receptor , organic chemistry , programming language
The physiological role played by protein‐ligand recognition has motivated the development of several computational approaches to the ligand binding affinity. Some of them, termed rigorous, have a strong theoretical foundation but involve too much computation to be generally useful. Some others alleviate the computational burden by introducing strong approximations and/or empirical calibrations, which also limit their general use. Most importantly, there is no straightforward correlation between the predictive power and the level of approximation introduced. Here, we present a general framework for the quantitative interpretation of protein‐ligand binding based on statistical mechanics. Within this framework, we re‐derive self‐consistently the fundamental equations of some popular approaches to the binding constant and pinpoint the inherent approximations. Our analysis represents a first step towards the development of variants with optimum accuracy/efficiency ratio for each stage of the drug discovery pipeline.