Flexible Docking Problems Research Articles

A Collective Variable for the Rapid Exploration of Protein Druggability.

An efficient molecular simulation methodology has been developed for the evaluation of the druggability (ligandability) of a protein. Previously proposed techniques were designed to assess the druggability of crystallographic structures and cannot be tightly coupled to molecular dynamics (MD) simulations. By contrast, the present approach, JEDI (Just Exploring Druggability at protein Interfaces), features a druggability potential made of a combination of empirical descriptors that can be collected "on-the-fly" during MD simulations. Extensive validation studies indicate that JEDI analyses discriminate druggable and nondruggable protein binding site conformations with accuracy similar to alternative methodologies, and at a fraction of the computational cost. Since the JEDI function is continuous and differentiable, the druggability potential can be used as collective variable to rapidly detect cryptic druggable binding sites in proteins with a variety of MD free energy methods. Protocols for applications to flexible docking problems are outlined.

Solving molecular flexible docking problems with metaheuristics: A comparative study

The main objective of the molecular docking problem is to find a conformation between a small molecule (ligand) and a receptor molecule with minimum binding energy. The quality of the docking score depends on two factors: the scoring function and the search method being used to find the lowest binding energy solution. In this context, AutoDock 4.2 is a popular C++ software package in the bioinformatics community providing both elements, including two genetic algorithms, one of them endowed with a local search strategy. This paper principally focuses on the search techniques for solving the docking problem. In using the AutoDock 4.2 scoring function, the approach in this study is twofold. On the one hand, a number of four metaheuristic techniques are analyzed within an extensive set of docking problems, looking for the best technique according to the quality of the binding energy solutions. These techniques are thoroughly evaluated and also compared with popular well-known docking algorithms in AutoDock 4.2. The metaheuristics selected are: generational and a steady-state Genetic Algorithm, Differential Evolution, and Particle Swarm Optimization. On the other hand, a C++ version of the jMetal optimization framework has been integrated inside AutoDock 4.2, so that all the algorithms included in jMetal are readily available to solve docking problems. The experiments reveal that Differential Evolution obtains the best overall results, even outperforming other existing algorithms specifically designed for molecular docking.

FIPSDock: A new molecular docking technique driven by fully informed swarm optimization algorithm

The accurate prediction of protein-ligand binding is of great importance for rational drug design. We present herein a novel docking algorithm called as FIPSDock, which implements a variant of the Fully Informed Particle Swarm (FIPS) optimization method and adopts the newly developed energy function of AutoDock 4.20 suite for solving flexible protein-ligand docking problems. The search ability and docking accuracy of FIPSDock were first evaluated by multiple cognate docking experiments. In a benchmarking test for 77 protein/ligand complex structures derived from GOLD benchmark set, FIPSDock has obtained a successful predicting rate of 93.5% and outperformed a few docking programs including particle swarm optimization (PSO)@AutoDock, SODOCK, AutoDock, DOCK, Glide, GOLD, FlexX, Surflex, and MolDock. More importantly, FIPSDock was evaluated against PSO@AutoDock, SODOCK, and AutoDock 4.20 suite by cross-docking experiments of 74 protein-ligand complexes among eight protein targets (CDK2, ESR1, F2, MAPK14, MMP8, MMP13, PDE4B, and PDE5A) derived from Sutherland-crossdock-set. Remarkably, FIPSDock is superior to PSO@AutoDock, SODOCK, and AutoDock in seven out of eight cross-docking experiments. The results reveal that FIPS algorithm might be more suitable than the conventional genetic algorithm-based algorithms in dealing with highly flexible docking problems.

Single- and Multi-Objective Cooperation for the Flexible Docking Problem

In this article, the impact of single-objective methods as intensification factors in a multi-objective approach is presented for the flexible docking problem. Based on a novel tri-objective model, a parallel multi-objective genetic algorithm has been designed. However, due to the high variability of the energy objective, intensification methods focused on this objective have been also included in order to improve the convergence speed of the genetic algorithm and the quality of the results. The corresponding approach, combining single- and multi-objective methods, has been proved efficient according to the tested instances and the quality criterion used.

On the Characterization and Selection of Diverse Conformational Ensembles with Applications to Flexible Docking

To address challenging flexible docking problems, a number of docking algorithms pregenerate large collections of candidate conformers. To remove the redundancy from such ensembles, a central problem in this context is to report a selection of conformers maximizing some geometric diversity criterion. We make three contributions to this problem. First, we resort to geometric optimization so as to report selections maximizing the molecular volume or molecular surface area (MSA) of the selection. Greedy strategies are developed, together with approximation bounds. Second, to assess the efficacy of our algorithms, we investigate two conformer ensembles corresponding to a flexible loop of four protein complexes. By focusing on the MSA of the selection, we show that our strategy matches the MSA of standard selection methods, but resorting to a number of conformers between one and two orders of magnitude smaller. This observation is qualitatively explained using the Betti numbers of the union of balls of the selection. Finally, we replace the conformer selection problem in the context of multiple-copy flexible docking. On the aforementioned systems, we show that using the loops selected by our strategy can improve the result of the docking process.

SODOCK: Swarm optimization for highly flexible protein–ligand docking

Protein-ligand docking can be formulated as a parameter optimization problem associated with an accurate scoring function, which aims to identify the translation, orientation, and conformation of a docked ligand with the lowest energy. The parameter optimization problem for highly flexible ligands with many rotatable bonds is more difficult than that for less flexible ligands using genetic algorithm (GA)-based approaches, due to the large numbers of parameters and high correlations among these parameters. This investigation presents a novel optimization algorithm SODOCK based on particle swarm optimization (PSO) for solving flexible protein-ligand docking problems. To improve efficiency and robustness of PSO, an efficient local search strategy is incorporated into SODOCK. The implementation of SODOCK adopts the environment and energy function of AutoDock 3.05. Computer simulation results reveal that SODOCK is superior to the Lamarckian genetic algorithm (LGA) of AutoDock, in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands. The results also reveal that PSO is more suitable than the conventional GA in dealing with flexible docking problems with high correlations among parameters. This investigation also compared SODOCK with four state-of-the-art docking methods, namely GOLD 1.2, DOCK 4.0, FlexX 1.8, and LGA of AutoDock 3.05. SODOCK obtained the smallest RMSD in 19 of 37 cases. The average 2.29 A of the 37 RMSD values of SODOCK was better than those of other docking programs, which were all above 3.0 A.

Application of a Molecular Dynamics Simulation Method with a Generalized Effective Potential to the Flexible Molecular Docking Problems

We present a new molecular dynamics method using Tsallis effective potential for the flexible docking problems of streptavidin/biotin and protein kinase C/phorbol-13-acetate. With a full flexibility of the ligands and a partial flexibility of the receptor active sites included, the new MD scheme accelerates the docking process significantly, by way of infrequent q-jumping and q-relaxation procedures between a normal potential energy surface and its transformed one by the Tsallis scheme. In the transformation, only the nonbonding interaction terms of an empirical potential energy function were employed. It has been found that the current method can predict the correctly docked structures in quite an effective way. Current results strongly indicate that this new MD method can be a very promising strategy for flexible ligand/flexible receptor docking.