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Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Review Article

Electrostatic Potential Energy in Protein-Drug Complexes

Author(s): Gabriela Bitencourt-Ferreira and Walter Filgueira de Azevedo Junior*

Volume 28 , Issue 24 , 2021

Published on: 01 February, 2021

Page: [4954 - 4971] Pages: 18

DOI: 10.2174/0929867328666210201150842

Price: $65

Abstract

Background: Electrostatic interactions are one of the forces guiding the binding of molecules to proteins. The assessment of this interaction through computational approaches makes it possible to evaluate the energy of protein-drug complexes.

Objective: Our purpose here is to review some of the methods used to calculate the electrostatic energy of protein-drug complexes and explore the capacity of these approaches for the generation of new computational tools for drug discovery using the abstraction of scoring function space.

Methods: Here, we present an overview of the AutoDock4 semi-empirical scoring function used to calculate binding affinity for protein-drug complexes. We focus our attention on electrostatic interactions and how to explore recently published results to increase the predictive performance of the computational models to estimate the energetics of protein- drug interactions. Public data available at Binding MOAD, BindingDB, and PDBbind were used to review the predictive performance of different approaches to predict binding affinity.

Results: A comprehensive outline of the scoring function used to evaluate potential energy available in docking programs is presented. Recent developments of computational models to predict protein-drug energetics were able to create targeted-scoring functions to predict binding to these proteins. These targeted models outperform classical scoring functions and highlight the importance of electrostatic interactions in the definition of the binding.

Conclusion: Here, we reviewed the development of scoring functions to predict binding affinity through the application of a semi-empirical free energy scoring function. Our studies show the superior predictive performance of machine learning models when compared with classical scoring functions and the importance of electrostatic interactions for binding affinity.

Keywords: Semi-empirical force scoring function, permittivity function parameters, protein-ligand interaction, drug design, electrostatic interactions, AutoDock4, scoring function space.


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