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Natural language processing in text mining for structural modeling of protein complexes.

Varsha D Badal1, Petras J Kundrotas2, Ilya A Vakser3

  • 1Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047, USA.

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|March 7, 2018
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Summary

Advanced text-mining with natural language processing improves protein docking by refining binding site residue identification from biomedical literature, increasing success rates.

Keywords:
Binding site predictionDependency parserProtein dockingProtein interactionsRule-based systemSupervised learning

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • Protein-protein interaction modeling generates numerous complex configurations.
  • Identifying accurate near-native models remains a significant challenge.
  • Biomedical research abstracts can provide crucial constraints for protein docking.

Purpose of the Study:

  • To enhance a text-mining (TM) tool for more accurate protein binding site residue extraction.
  • To improve the performance of protein docking by utilizing refined constraints.

Main Methods:

  • Extended a text-mining tool with natural language processing (NLP) for contextual analysis of residue occurrence.
  • Developed specialized dictionaries to identify relevant protein binding site keywords.
  • Employed machine learning-based NLP for efficient filtering of mined residues.
  • Tested NLP-generated constraints in docking unbound proteins using the DOCKGROUND benchmark set.

Main Results:

  • A dictionary tailored for protein binding sites significantly improved TM performance compared to generic interaction dictionaries.
  • Machine learning-based NLP filtered mined residues more effectively than rule-based NLP.
  • Using NLP-refined constraints substantially improved protein docking output quality and success rates.

Conclusions:

  • Deep parsing and NLP significantly advanced basic text-mining for protein binding site residue extraction.
  • The enhanced TM approach with NLP demonstrated a substantial increase in protein docking success rates.