Computational approaches to predict protein functional families and functional sites.

Autor: Rauer C; Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK., Sen N; Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK., Waman VP; Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK., Abbasian M; Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK., Orengo CA; Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK. Electronic address: c.orengo@ucl.ac.uk.
Jazyk: angličtina
Zdroj: Current opinion in structural biology [Curr Opin Struct Biol] 2021 Oct; Vol. 70, pp. 108-122. Date of Electronic Publication: 2021 Jul 02.
DOI: 10.1016/j.sbi.2021.05.012
Abstrakt: Understanding the mechanisms of protein function is indispensable for many biological applications, such as protein engineering and drug design. However, experimental annotations are sparse, and therefore, theoretical strategies are needed to fill the gap. Here, we present the latest developments in building functional subclassifications of protein superfamilies and using evolutionary conservation to detect functional determinants, for example, catalytic-, binding- and specificity-determining residues important for delineating the functional families. We also briefly review other features exploited for functional site detection and new machine learning strategies for combining multiple features.
Competing Interests: Conflict of interest statement The authors declare no conflict of interest. Given her role as Editorial Board Member, Christine Orengo had no involvement in the peer review process of this article and had no access to information regarding its peer review.
(Copyright © 2021. Published by Elsevier Ltd.)
Databáze: MEDLINE
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