Abstract
In the latest few decades, molecular docking has imposed itself as one of the most used approaches for computational drug discovery. Several docking benchmarks have been published, comparing the performance of different algorithms in respect to a molecular target of interest, usually evaluating their ability in reproducing the experimental data, which, in most cases, comes from X-ray structures. In this study, we elucidated the variation of the performance of three docking algorithms, namely GOLD, Glide, and PLANTS, in replicating the coordinates of the crystallographic ligands of SARS-CoV-2 main protease (Mpro). Through the comparison of the data coming from docking experiments and the values derived from the calculation of the solvent exposure of the crystallographic ligands, we highlighted the importance of this last variable for docking performance. Indeed, we underlined how an increase in the percentage of the ligand surface exposed to the solvent in a crystallographic complex makes it harder for the docking algorithms to reproduce its conformation. We further validated our hypothesis through molecular dynamics simulations, showing that the less stable protein–ligand complexes (in terms of root-mean-square deviation and root-mean-square fluctuation) tend to be derived from the cases in which the solvent exposure of the ligand in the starting system is higher.
Highlights
In the 1980s, with the first study provided by Kuntz et al [1], the computational technique of molecular docking had its birth
All the colormaps present in this study are based on a colorimetric scale delineating the root-mean-square deviation (RMSD) values, starting from 0 Å, which corresponds to a molecular docking pose exactly super posable to the crystallographic one, and reaching values of 5 Å or higher, corresponding to a very low level of overlap between the coordinates of the pose produced and the ones of the crystallographic conformation
The x-axis lists all the different protein–ligand complexes, which are plotted against the different pairs docking program-scoring function used for this study, reported in the y-axis
Summary
In the 1980s, with the first study provided by Kuntz et al [1], the computational technique of molecular docking had its birth. The efficiency, speed, and robustness of this method make its presence a constant in every structure-based drug-discovery pipeline [2]. To give a brief explanation, molecular docking consists of a multistep computational process that aims to find the best conformation of a molecule to bind to another to form a stable complex [3]. In the field of medicinal chemistry, as is deductible, its main application is finding the best molecules to bind in a firm way to the desired target (a protein, a nucleic acid, etc.). The algorithm starts with the exploration of the conformations space of the ligands (exploiting the so-called “search algorithm”).
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call