Protein aggregation: in silico algorithms and applications |
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Authors: | R. Prabakaran Puneet Rawat A. Mary Thangakani Sandeep Kumar M. Michael Gromiha |
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Affiliation: | 1.Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India ;2.Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT USA ;3.School of Computing, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Kanagawa Japan |
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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. |
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Keywords: | Protein aggregation Peptide assembly Aggregation kinetics Aggregation propensity Prediction Algorithm Molecular dynamics |
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