Application of machine learning on understanding biomolecule interactions in cellular machinery. (February 2023)

• Record Type: Journal Article
• Authors: Dixit, Rewati; Khambhati, Khushal; Supraja, Kolli Venkata; Singh, Vijai; Lederer, Franziska; Show, Pau-Loke; Awasthi, Mukesh Kumar; Sharma, Abhinav; Jain, Rohan
• Is Part Of: Bioresource technology. Volume 370(2023)
• Publication Date: 2023-02
• Subjects: Protein mutation -- Protein-solid interaction -- Protein-carbohydrate interaction -- Aptamer design -- Nanomedicine; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.128522
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: ML applications in therapeutics can enhance drug delivery. Protein modifications through ML can improve their targeted bioactivity. Detection of protein-solid interactions through ML can aid in nanomedicine. ML can predict the interactions between carbohydrate and protein. Abstract: Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.