Open AccessProtein aggregation: in silico algorithms and applications
Author(s) -
R. Prabakaran,
Puneet Rawat,
A. Mary Thangakani,
Sandeep Kumar,
M. Michael Gromiha
Publication year - 2021
Publication title -
biophysical reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.766
H-Index - 39
eISSN - 1867-2469
pISSN - 1867-2450
DOI - 10.1007/s12551-021-00778-w
Subject(s) - in silico , computer science , protein aggregation , field (mathematics) , data science , computational biology , biochemical engineering , chemistry , biology , engineering , mathematics , biochemistry , gene , pure mathematics
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.