| Literature DB >> 33747245 |
R Prabakaran1, Puneet Rawat1, A Mary Thangakani1, Sandeep Kumar2, M Michael Gromiha1,3.
Abstract
Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications. © International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature 2021.Entities:
Keywords: Aggregation kinetics; Aggregation propensity; Algorithm; Molecular dynamics; Peptide assembly; Prediction; Protein aggregation
Year: 2021 PMID: 33747245 PMCID: PMC7930180 DOI: 10.1007/s12551-021-00778-w
Source DB: PubMed Journal: Biophys Rev ISSN: 1867-2450