Text mining for modeling of protein complexes enhanced by machine learning.

Journal: Bioinformatics (Oxford, England)
Published Date: May 1, 2021

Abstract

MOTIVATION: Procedures for structural modeling of protein-protein complexes (protein docking) produce a number of models which need to be further analyzed and scored. Scoring can be based on independently determined constraints on the structure of the complex, such as knowledge of amino acids essential for the protein interaction. Previously, we showed that text mining of residues in freely available PubMed abstracts of papers on studies of protein-protein interactions may generate such constraints. However, absence of post-processing of the spotted residues reduced usability of the constraints, as a significant number of the residues were not relevant for the binding of the specific proteins.

Authors

  • Varsha D Badal
    Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047, USA.
  • Petras J Kundrotas
    Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047, USA. pkundro@ku.edu.
  • Ilya A Vakser
    Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047, USA. vakser@ku.edu.

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