Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

scholarly journals Learning RMSD to Improve Protein-Ligand Scoring and Pose Selection

2020 ◽  
Author(s):  
Rishal Aggarwal ◽  
David R. Koes
Keyword(s):  
Scoring Function ◽  
Target Site ◽  
Scoring Functions ◽  
Autodock Vina ◽  
Binding Ability ◽  
Structure Based Drug Design ◽  
Chemical Structures ◽  
Novel Approach ◽  
Experimental Values ◽  
Ligand Conformation

Docking algorithms are an essential part of the Structure Based Drug Design (SBDD) process as they aim to effectively identify the binding poses of chemical structures at the target site. These algorithms are reliant on scoring functions that evaluate the binding ability of a ligand conformation. Typically, scoring functions are designed to predict the binding affinity of various poses at the target site. In this work, we design a novel approach where the scoring function attempts to predict the Root Mean Square Deviation (RMSD) of a pose to the true binding pose. We show that a Convolutional Neural Network (CNN) can be trained to learn these RMSD values with high correlation between predicted and experimental values. Furthermore we show that this scoring function can improve pose selection performance when used in combination with orthogonal scoring functions like Autodock Vina.

scholarly journals Learning RMSD to Improve Protein-Ligand Scoring and Pose Selection

2020 ◽  
Author(s):  
Rishal Aggarwal ◽  
David R. Koes
Keyword(s):  
Scoring Function ◽  
Target Site ◽  
Scoring Functions ◽  
Autodock Vina ◽  
Binding Ability ◽  
Structure Based Drug Design ◽  
Chemical Structures ◽  
Novel Approach ◽  
Experimental Values ◽  
Ligand Conformation

Docking algorithms are an essential part of the Structure Based Drug Design (SBDD) process as they aim to effectively identify the binding poses of chemical structures at the target site. These algorithms are reliant on scoring functions that evaluate the binding ability of a ligand conformation. Typically, scoring functions are designed to predict the binding affinity of various poses at the target site. In this work, we design a novel approach where the scoring function attempts to predict the Root Mean Square Deviation (RMSD) of a pose to the true binding pose. We show that a Convolutional Neural Network (CNN) can be trained to learn these RMSD values with high correlation between predicted and experimental values. Furthermore we show that this scoring function can improve pose selection performance when used in combination with orthogonal scoring functions like Autodock Vina.

scholarly journals Learning RMSD to Improve Protein-Ligand Scoring and Pose Selection

2020 ◽  
Author(s):  
Rishal Aggarwal ◽  
David R. Koes
Keyword(s):  
Scoring Function ◽  
Target Site ◽  
Scoring Functions ◽  
Autodock Vina ◽  
Binding Ability ◽  
Structure Based Drug Design ◽  
Chemical Structures ◽  
Novel Approach ◽  
Experimental Values ◽  
Ligand Conformation

Docking algorithms are an essential part of the Structure Based Drug Design (SBDD) process as they aim to effectively identify the binding poses of chemical structures at the target site. These algorithms are reliant on scoring functions that evaluate the binding ability of a ligand conformation. Typically, scoring functions are designed to predict the binding affinity of various poses at the target site. In this work, we design a novel approach where the scoring function attempts to predict the Root Mean Square Deviation (RMSD) of a pose to the true binding pose. We show that a Convolutional Neural Network (CNN) can be trained to learn these RMSD values with high correlation between predicted and experimental values. Furthermore we show that this scoring function can improve pose selection performance when used in combination with orthogonal scoring functions like Autodock Vina.

scholarly journals Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses

Molecules ◽  
2019 ◽  
Vol 24 (14) ◽  
pp. 2610 ◽  
Author(s):  
Célien Jacquemard ◽  
Viet-Khoa Tran-Nguyen ◽  
Malgorzata N. Drwal ◽  
Didier Rognan ◽  
Esther Kellenberger
Keyword(s):  
Virtual Screening ◽  
Protein Complexes ◽  
Local Interaction ◽  
Ligand Docking ◽  
Pose Prediction ◽  
Binding Modes ◽  
Individual Comparison ◽  
Docking Program ◽  
The Individual ◽  
Interaction Density

Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.

scholarly journals Predicting the structure and energetics of protein-ligand interaction

2019 ◽  
Author(s):  
◽  
Zhiwei Ma
Keyword(s):  
Molecular Docking ◽  
Drug Design ◽  
Large Scale ◽  
Kinase Inhibitors ◽  
Ligand Docking ◽  
Pose Prediction ◽  
Computer Algorithms ◽  
Ligand Interaction ◽  
Structure Based Drug Design ◽  
Ligand Selectivity

Molecular docking has been a crucial component and remains a highly active area in computer-aided drug design (CADD). In simple terms, molecular docking uses computer algorithms to identify the "best" match between two molecules, a process analogous to solving three-dimensional jigsaw puzzles. In more rigorous terms, the molecular docking problem can be defined as predicting the "correct" bound association state for the given atomic coordinates of two molecules. Docking is an important tool for structure and affinity predictions of molecular association, which would lead to the mechanistic understanding of the physicochemical interactions at the atomic level. Protein-small molecule (referred to as "ligand") docking, in particular, has broad application to structure-based drug design, as drug compounds are usually small molecules. In this dissertation, I present my studies on protein-ligand docking. In the background introduction, I reviewed the docking methodology and the key recent developments in the field. Next, I applied an ensemble docking algorithm onto 14 protein kinases to study ligand selectivity, a major issue for the development of kinase inhibitors as anticancer drugs. In Chapter 3, I developed a web server for automated, in silico screening of multiple targets for a given ligand query. Finally, I integrated the new methods for protein-ligand binding mode prediction and applied the integrated method to a large-scale, blind prediction competition named Continuous Evaluation of Ligand Pose Prediction (CELPP).

scholarly journals A Hybrid Cuckoo Search and Differential Evolution Approach to Protein–Ligand Docking

2018 ◽  
Vol 19 (10) ◽  
pp. 3181 ◽  
Author(s):  
Hang Lin ◽  
Shirley Siu
Keyword(s):  
Differential Evolution ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Optimization Methods ◽  
Binding Pocket ◽  
Ligand Docking ◽  
Conformational Search ◽  
Success Rates ◽  
Autodock Vina ◽  
Benchmark Datasets

Protein–ligand docking is a molecular modeling technique that is used to predict the conformation of a small molecular ligand at the binding pocket of a protein receptor. There are many protein–ligand docking tools, among which AutoDock Vina is the most popular open-source docking software. In recent years, there have been numerous attempts to optimize the search process in AutoDock Vina by means of heuristic optimization methods, such as genetic and particle swarm optimization algorithms. This study, for the first time, explores the use of cuckoo search (CS) to solve the protein–ligand docking problem. The result of this study is CuckooVina, an enhanced conformational search algorithm that hybridizes cuckoo search with differential evolution (DE). Extensive tests using two benchmark datasets, PDBbind 2012 and Astex Diverse set, show that CuckooVina improves the docking performances in terms of RMSD, binding affinity, and success rate compared to Vina though it requires about 9–15% more time to complete a run than Vina. CuckooVina predicts more accurate docking poses with higher binding affinities than PSOVina with similar success rates. CuckooVina’s slower convergence but higher accuracy suggest that it is better able to escape from local energy minima and improves the problem of premature convergence. As a summary, our results assure that the hybrid CS–DE process to continuously generate diverse solutions is a good strategy to maintain the proper balance between global and local exploitation required for the ligand conformational search.

scholarly journals Protein flexibility in docking and surface mapping

2012 ◽  
Vol 45 (3) ◽  
pp. 301-343 ◽  
Author(s):  
Katrina W. Lexa ◽  
Heather A. Carlson
Keyword(s):  
Structural Information ◽  
Protein Flexibility ◽  
Ligand Docking ◽  
Pose Prediction ◽  
Lead Discovery ◽  
Surface Mapping ◽  
Structure Based Drug Design ◽  
Potential Binding ◽  
Flexible Protein ◽  
Rigid Docking

AbstractStructure-based drug design has become an essential tool for rapid lead discovery and optimization. As available structural information has increased, researchers have become increasingly aware of the importance of protein flexibility for accurate description of the native state. Typical protein–ligand docking efforts still rely on a single rigid receptor, which is an incomplete representation of potential binding conformations of the protein. These rigid docking efforts typically show the best performance rates between 50 and 75%, while fully flexible docking methods can enhance pose prediction up to 80–95%. This review examines the current toolbox for flexible protein–ligand docking and receptor surface mapping. Present limitations and possibilities for future development are discussed.

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