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Prediction of protein interactions is essential for studying biomolecular mechanisms
  • Ilya Vakser
Ilya Vakser
University of Kansas

Corresponding Author:[email protected]

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Structural characterization of protein interactions is essential for our ability to understand and modulate physiological processes. Computational approaches to modeling of protein complexes provide structural information that far exceeds capabilities of the existing experimental techniques. Protein structure prediction in general, and prediction of protein interactions in particular, has been revolutionized by the rapid progress in Deep Learning techniques. The work of Schweke et al. presents a community-wide study of an important problem of distinguishing physiological protein-protein complexes/interfaces (experimentally determined or modeled) from non-physiological ones. The authors designed and generated a large benchmark set of physiological and non-physiological homodimeric complexes, and evaluated a large set of scoring functions, as well as AlphaFold predictions, on their ability to discriminate the non-physiological interfaces. The problem of separating physiological interfaces from non-physiological ones is very difficult, largely due to the lack of a clear distinction between the two categories in a crowded environment inside a living cell. Still, the ability to identify key physiologically significant interfaces in the variety of possible configurations of a protein-protein complex is important. The study presents a major data resource and methodological development in this important direction for molecular and cellular biology.
27 Jun 2023Submitted to PROTEOMICS
30 Jun 2023Assigned to Editor
30 Jun 2023Submission Checks Completed
30 Jun 2023Review(s) Completed, Editorial Evaluation Pending
05 Jul 2023Editorial Decision: Accept