New Frontiers for Machine Learning in Protein Science. Issue 20 (1st October 2021)

• Record Type: Journal Article
• Authors: Morgunov, Alexey S.; Saar, Kadi L.; Vendruscolo, Michele; Knowles, Tuomas P.J.
• Is Part Of: Journal of molecular biology. Volume 433:Issue 20(2021)
• Publication Date: 2021-10-01
• Subjects: protein folding -- machine learning -- liquid-liquid phase separation -- protein aggregation -- protein-protein interactions -- data science; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2021.167232
• Languages: English
• ISSNs: 0022-2836

Abstract

Graphical abstract: Highlights: Protein function is fundamentally reliant on inter-molecular interactions. Protein interactions involve ensembles of conformational states. We describe how machine learning methods open new frontiers in biomolecular sciences. We discuss the challenges in the study of the interactions and condensation of proteins. Abstract: Protein function is fundamentally reliant on inter-molecular interactions that underpin the ability of proteins to form complexes driving biological processes in living cells. Increasingly, such interactions are recognised as being formed between proteins that exist on a broad spectrum of dynamic conformational states and levels of intrinsic disorder. Additionally, the sizes of the structures formed can range from simple binary complexes to large dynamic biomolecular condensates measuring 100 nm or more. Understanding the parameters that govern such interactions, how they form, how they lead to function and what happens when they take place in unintended manners and lead to disease, represent some of the core questions for molecular biosciences. In light of recent advances made in solving the protein folding problem by machine learning methods, we discuss here the challenges and opportunities brought by these new data-driven approaches for the next frontiers of biomolecular science.