Application of machine learning on understanding biomolecule interactions in cellular machinery

Rewati Dixit, Khushal Khambhati, Kolli Venkata Supraja, Vijai Singh, Franziska Lederer, Pau Loke Show, Mukesh Kumar Awasthi, Abhinav Sharma, Rohan Jain

Research output: Contribution to journalReview articlepeer-review

11 Scopus citations

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.

Original languageBritish English
Article number128522
JournalBioresource Technology
Volume370
DOIs
StatePublished - Feb 2023

Keywords

  • Aptamer design
  • Nanomedicine
  • Protein mutation
  • Protein-carbohydrate interaction
  • Protein-solid interaction

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