Open AccessRecent Trends in Machine Learning-based Protein Fold Recognition Methods
Publication year - 2020
Publication title -
biointerface research in applied chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.216
H-Index - 11
ISSN - 2069-5837
DOI - 10.33263/briac114.1123311243
Subject(s) - threading (protein sequence) , protein function prediction , protein structure prediction , protein structure , fold (higher order function) , protein sequencing , computer science , artificial intelligence , sequence (biology) , structural classification of proteins database , protein tertiary structure , peptide sequence , computational biology , machine learning , protein function , biology , biochemistry , gene , programming language
Proteins are macromolecules that enable life. Protein function is due to its three-dimensional structure and shape. It is challenging to understand how a linear sequence of amino acid residues folds into a three-dimensional structure. Machine learning-based methods may help significantly in reducing the gap present between known protein sequence and structure. Identifying protein folds from a sequence can help predict protein tertiary structure, determine protein function, and give insights into protein-protein interactions. This work focuses on the following aspects. The kind of features such as sequential, structural, functional, and evolutionary extracted for representing protein sequence and different methods of extracting these features. This work also includes details of machine learning algorithms used with respective settings and protein fold recognition structures. Detailed performance comparison of well-known works is also given.