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Computational Models for the Study of Protein Aggregation. | LitMetric

Computational Models for the Study of Protein Aggregation.

Methods Mol Biol

Institute of Physics, Polish Academy of Sciences, Warsaw, Poland.

Published: February 2022

AI Article Synopsis

  • Protein aggregation is linked to neurodegenerative diseases and its understanding is crucial since these diseases currently have no effective treatments.
  • Researchers use both experimental and computational methods to study protein structures and their self-organization processes, despite the challenges involved.
  • This text discusses various computational approaches, focusing on four prominent all-atom force fields (AMBER, CHARMM, GROMOS, and OPLS) and the use of coarse-grained models to investigate aggregation kinetics.

Article Abstract

Protein aggregation has been studied by many groups around the world for many years because it can be the cause of a number of neurodegenerative diseases that have no effective treatment. Obtaining the structure of related fibrils and toxic oligomers, as well as describing the pathways and main factors that govern the self-organization process, is of paramount importance, but it is also very difficult. To solve this problem, experimental and computational methods are often combined to get the most out of each method. The effectiveness of the computational approach largely depends on the construction of a reasonable molecular model. Here we discussed different versions of the four most popular all-atom force fields AMBER, CHARMM, GROMOS, and OPLS, which have been developed for folded and intrinsically disordered proteins, or both. Continuous and discrete coarse-grained models, which were mainly used to study the kinetics of aggregation, are also summarized.

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Source
http://dx.doi.org/10.1007/978-1-0716-1546-1_4DOI Listing

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