Abstract
BackgroundProtein-ligand docking programs are routinely used in structure-based drug design to find the optimal binding pose of a ligand in the protein’s active site. These programs are also used to identify potential drug candidates by ranking large sets of compounds. As more accurate and efficient docking programs are always desirable, constant efforts focus on developing better docking algorithms or improving the scoring function. Recently, chaotic maps have emerged as a promising approach to improve the search behavior of optimization algorithms in terms of search diversity and convergence speed. However, their effectiveness on docking applications has not been explored. Herein, we integrated five popular chaotic maps—logistic, Singer, sinusoidal, tent, and Zaslavskii maps—into PSOVina^{{mathrm{2LS}}}, a recent variant of the popular AutoDock Vina program with enhanced global and local search capabilities, and evaluated their performances in ligand pose prediction and virtual screening using four docking benchmark datasets and two virtual screening datasets.ResultsPose prediction experiments indicate that chaos-embedded algorithms outperform AutoDock Vina and PSOVina in ligand pose RMSD, success rate, and run time. In virtual screening experiments, Singer map-embedded PSOVina^{{mathrm{2LS}}} achieved a very significant five- to sixfold speedup with comparable screening performances to AutoDock Vina in terms of area under the receiver operating characteristic curve and enrichment factor. Therefore, our results suggest that chaos-embedded PSOVina methods might be a better option than AutoDock Vina for docking and virtual screening tasks. The success of chaotic maps in protein-ligand docking reveals their potential for improving optimization algorithms in other search problems, such as protein structure prediction and folding. The Singer map-embedded PSOVina^{{mathrm{2LS}}} which is named PSOVina-2.0 and all testing datasets are publicly available on https://cbbio.cis.umac.mo/software/psovina.
Highlights
Small-molecule drugs exert their pharmacological effects through binding to their biological targets and subsequently modulating the activities that are associated with diseases to be treated
Some metaheuristic docking methods have been implemented, such as SODOCK [6], particle swarm optimization (PSO)@AutoDock [7], FIPSDock [8], PSOVina [9] based on the PSO algorithm and variants, PLANTS [10] based on ant colony optimization (ACO) and FlABCps [11] based on artificial bee colony (ABC)
In this work, we explored the use of chaotic maps to enhance the search capability and speed in docking applications
Summary
Small-molecule drugs exert their pharmacological effects through binding to their biological targets and subsequently modulating the activities that are associated with diseases to be treated. Swarm-intelligence-based approaches using particle swarm optimization (PSO) and other nature-inspired methods, such as artificial bee colony (ABC) and ant colony optimization (ACO), have become very popular for solving nonlinear and complex optimization problems The advantages of these metaheuristic algorithms are that they tend to find good solutions quickly, they are easy to implement, and there are many variants to allow easy customization of the algorithm fitting the domain of interest. Some metaheuristic docking methods have been implemented, such as SODOCK [6], PSO@AutoDock [7], FIPSDock [8], PSOVina [9] based on the PSO algorithm and variants, PLANTS [10] based on ACO and FlABCps [11] based on ABC All of these docking methods have been shown to improve the pose prediction accuracy and docking efficiency compared to traditional optimization methods. We integrated five popular chaotic maps—logistic, Singer, sinusoidal, tent, and Zaslavskii maps—into PSOVina2LS , a recent variant of the popular AutoDock Vina program with enhanced global and local search capabilities, and evaluated their performances in ligand pose prediction and virtual screening using four docking benchmark datasets and two virtual screening datasets
Sign-in/Register to view highlights & summary 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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
