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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Acknowledgements
We thank the Bioinformatics Infrastructure Facility, Department of Biotechnology, and the Indian Institute of Technology Madras for computational facilities and the Ministry of Human Resource and Development (MHRD) for HTRA scholarship to PR. We thank WALTZ developers for sharing the executable.
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Prabakaran, R., Rawat, P., Thangakani, A.M. et al. Protein aggregation: in silico algorithms and applications. Biophys Rev 13, 71–89 (2021). https://doi.org/10.1007/s12551-021-00778-w
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DOI: https://doi.org/10.1007/s12551-021-00778-w