Name
Abstract | ||
|---|---|---|
Analysis of protein - small molecule interactions is crucial in the discovery of new drug candidates and lead structure optimization. Small biomolecules (ligands) are highly flexible and may adopt numerous conformations upon binding to the protein. Scoring functions are traditionally used in many docking protocols and have key impact on a quality of structure-based virtual screening. A correct scoring function should be able to guide search algorithm to find and recognize native-like docking poses. In ideal case scoring function should be able to predict binding affinity. Despite extensive research, scoring remains a major challenge in structure-based virtual screening. We apply Stochastic Roadmap Simulation (SRS) and finite absorbing Markov chain theory to build a model of protein-ligand binding process [1, 2]. We propose a computational quantity - time to escape (TTE) from a funnel of attraction around binding site as a measure of binding affinity. The results based on PDBBind Core Set [3] show statistically significant correlation between actual binding affinity and calculated TTE. |
Year | Venue | Field |
|---|---|---|
2013 | PROCEEDINGS IWBBIO 2013: INTERNATIONAL WORK-CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING | Docking (molecular),Binding site,Search algorithm,Biological system,Ligand (biochemistry),Computational chemistry,Chemistry,Accessible surface area,Virtual screening,Absorbing Markov chain,Coreset |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
1 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcin Pacholczyk | 1 | 1 | 1.64 |
| Damian Borys | 2 | 0 | 1.35 |
| Marek Kimmel | 3 | 145 | 20.47 |