Research Paper on Analysis of Protein Function Using Machine Learning

Description

Title: Analysis of Protein Function Using Machine Learning

Abstract: Machine learning (ML) has long been a crucial tool in computational biology for understanding protein function. Recent developments in novel machine learning techniques and applications, new machine learning techniques have been incorporated into many computational biology fields that deal with protein function. We look at how machine learning (ML) has been incorporated into a variety of computational models to increase prediction accuracy and comprehend protein function. Protein structure prediction, protein engineering using sequence changes to achieve stability and druggability characteristics, molecular docking in terms of protein-ligand binding, including allosteric effects, protein-rotein interactions, and protein-centric drug discovery are some of the applications that are covered. Structure, flexibility, stability, and dynamics must all be taken into account in order to quantify the mechanisms underlying protein function; these factors become inseparable due to their interdependence. Conformational dynamics, which frequently takes the form of protein kinetics, is another important aspect of how proteins function. This review includes computational techniques that employ ML to produce representative conformational ensembles and quantify differences in conformational ensembles crucial to function. The potential for each of these subjects in the future is highlighted.

Keywords: machine learning; protein structure prediction; protein–protein interactions; protein dynamics; protein function; allostery; conformational sampling; force fields; molecular docking

Paper Quality: SCOPUS / Web of Science Level Research Paper

Subject: Biomolecules

Writer Experience: 20+ Years

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