Evaluation of protein–ligand affinity prediction using steered molecular dynamics simulations
| Autor: | Noriaki Okimoto, Atsushi Suenaga, Makoto Taiji |
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| Rok vydání: | 2016 |
| Předmět: |
Work values
Molecular Dynamics Simulation Ligands 010402 general chemistry 01 natural sciences Tacrolimus Binding Proteins Molecular dynamics Structural Biology Computational chemistry Catalytic Domain 0103 physical sciences Trypsin Molecular Biology Binding affinities Binding Sites 010304 chemical physics Chemistry Cyclin-Dependent Kinase 2 General Medicine Ligand (biochemistry) Affinities 0104 chemical sciences Thermodynamics Protein Conformation beta-Strand Biological system Protein Binding Protein ligand |
| DOI: | 10.6084/m9.figshare.4212999 |
| Popis: | In computational drug design, ranking a series of compound analogs in a manner that is consistent with experimental affinities remains a challenge. In this study, we evaluated the prediction of protein-ligand binding affinities using steered molecular dynamics simulations. First, we investigated the appropriate conditions for accurate predictions in these simulations. A conic harmonic restraint was applied to the system for efficient sampling of work values on the ligand unbinding pathway. We found that pulling velocity significantly influenced affinity predictions, but that the number of collectable trajectories was less influential. We identified the appropriate pulling velocity and collectable trajectories for binding affinity predictions as 1.25 Å/ns and 100, respectively, and these parameters were used to evaluate three target proteins (FK506 binding protein, trypsin, and cyclin-dependent kinase 2). For these proteins using our parameters, the accuracy of affinity prediction was higher and more stable when Jarzynski's equality was employed compared with the second-order cumulant expansion equation of Jarzynski's equality. Our results showed that steered molecular dynamics simulations are effective for predicting the rank order of ligands; thus, they are a potential tool for compound selection in hit-to-lead and lead optimization processes. |
| Databáze: | OpenAIRE |
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