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

Varsha D Badal1, Petras J Kundrotas1, Ilya A Vakser1,2

  • 1Computational Biology Program.

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|September 22, 2020
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Summary

Machine learning models filter irrelevant residues from protein-protein interaction studies. Deep Recursive Neural Network (DRNN) outperforms Support Vector Machine (SVM) when training on full-text and testing on abstracts for residue classification.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Protein-protein complex modeling requires scoring and analysis of generated models.
  • Constraints like essential amino acids can improve scoring accuracy.
  • Text mining of PubMed abstracts can identify potential interaction residues, but often includes irrelevant ones.

Purpose of the Study:

  • To develop and compare machine learning approaches for filtering irrelevant residues identified through text mining.
  • To enhance the usability of text-mined constraints for protein-protein complex structural modeling.

Main Methods:

  • Explored two machine learning models: Deep Recursive Neural Network (DRNN) and Support Vector Machine (SVM).
  • Investigated different training and testing schemes using PubMed abstracts and PMC Open Access (PMC-OA) full-text articles.
  • Classified residues as interface or non-interface.

Main Results:

  • DRNN model demonstrated superior performance over SVM when trained on full-text articles and tested on abstracts.
  • Model performance was similar when both training and testing were conducted on abstracts or full-text articles.
  • The computational cost of DRNN is significantly higher, especially during training.

Conclusions:

  • DRNN is advantageous for classifying residues when training and testing datasets have dissimilar text patterns (e.g., full-text vs. abstracts).
  • SVM is a viable and computationally efficient alternative when training and testing datasets share similar patterns.
  • Optimized residue filtering improves the reliability of constraints for protein docking and structural analysis.