Using machine‐learning‐driven approaches to boost hot‐spot's knowledge

Understanding protein–protein interactions (PPIs) is fundamental to describe and to characterize the formation of biomolecular assemblies, and to establish the energetic principles underlying biological networks. One key aspect of these interfaces is the existence and prevalence of hot‐spots (HS) residues that, upon mutation to alanine, negatively impact the formation of such protein–protein complexes. HS have been widely considered in research, both in case studies and in a few large‐scale predictive approaches. This review aims to present the current knowledge on PPIs, providing a detailed understanding of the microspecifications of the residues involved in those interactions and the characteristics of those defined as HS through a thorough assessment of related field‐specific methodologies. We explore recent accurate artificial intelligence‐based techniques, which are progressively replacing well‐established classical energy‐based methodologies.

• Publication year

  2022

• Venue

  WIREs Computational Molecular Science

• Publication date

  2022-02-09

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1002/wcms.1602
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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