FPDock: Protein–protein docking using flower pollination algorithm. (August 2021)

• Record Type: Journal Article
• Authors: Sunny, Sharon; Jayaraj, P.B.
• Is Part Of: Computational biology and chemistry. Volume 93(2021)
• Publication Date: 2021-08
• Subjects: Protein–protein docking -- Flower pollination algorithm -- Protein–protein interactions -- Nature inspired algorithms; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2021.107518
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Protein-protein docking can be formulated as an optimization problem where the system tries to minimize the energy function. Exhaustive searching in docking algorithms can be avoided by cleverly employing randomized algorithms. Applicability of Flower Pollination Algorithm in protein-protein docking is analyzed. An open source docking tool, FPDock, is developed. Abstract: Proteins play their vital role in biological systems through interaction and complex formation with other biological molecules. Indeed, abnormalities in the interaction patterns affect the proteins' structure and have detrimental effects on living organisms. Research in structure prediction gains its gravity as the functions of proteins depend on their structures. Protein–protein docking is one of the computational methods devised to understand the interaction between proteins. Metaheuristic algorithms are promising to use owing to the hardness of the structure prediction problem. In this paper, a variant of the Flower Pollination Algorithm (FPA) is applied to get an accurate protein–protein complex structure. The algorithm begins execution from a randomly generated initial population, which gets flourished in different isolated islands, trying to find their local optimum. The abiotic and biotic pollination applied in different generations brings diversity and intensity to the solutions. Each round of pollination applies an energy-based scoring function whose value influences the choice to accept a new solution. Analysis of final predictions based on CAPRI quality criteria shows that the proposed method has a success rate of 58% in top10 ranks, which in comparison with other methods like SwarmDock, pyDock, ZDOCK is better. Source code of the work is available at: https://github.com/Sharon1989Sunny/_FPDock_ .