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DELPHI: accurate deep ensemble model for protein interaction sites prediction.

Yiwei Li1, G Brian Golding2, Lucian Ilie1

  • 1Department of Computer Science, The University of Western Ontario London, ON N6A 5B7, Canada.

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|August 26, 2020
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Summary

DELPHI, a deep learning tool, accurately predicts protein-protein interaction sites using novel features and an ensemble model. This computational method outperforms existing programs, aiding in understanding protein function and evolution.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Protein-protein interactions (PPIs) are crucial for cellular functions.
  • Experimental methods for identifying PPI binding sites are time-consuming and costly.
  • Accurate computational prediction of PPI binding sites is essential for biological research.

Purpose of the Study:

  • To develop a novel, highly accurate computational method for predicting protein-protein interaction binding sites.
  • To introduce the DEep Learning Prediction of Highly probable protein Interaction sites (DELPHI) suite.
  • To leverage deep learning and novel features for improved PPI site prediction.

Main Methods:

  • Developed DELPHI, a sequence-based deep learning suite utilizing an ensemble of CNN and RNN models.
  • Incorporated three novel features (HSP, position information, ProtVec) alongside nine existing features.
  • Trained and evaluated DELPHI on five diverse datasets, comparing its performance against nine state-of-the-art programs.

Main Results:

  • DELPHI significantly outperformed all competing methods across all evaluated metrics.
  • DELPHI achieved substantial improvements in AUPRC (18.5%) and MCC (27.7%) compared to the second-best method.
  • The ensemble model and novel features were identified as key contributors to DELPHI's superior performance.
  • Predicted binding sites showed strong correlations with evolutionary conservation and matched known data from Pfam.

Conclusions:

  • DELPHI represents a significant advancement in computational prediction of protein-protein interaction binding sites.
  • The method's accuracy and novel features offer valuable insights into protein function and evolutionary conservation.
  • DELPHI is available as open-source software and a web server for broader scientific accessibility.