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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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Training host-pathogen protein-protein interaction predictors.

Abdul Hannan Basit1,2, Wajid Arshad Abbasi1, Amina Asif1

  • 1* Department of Computer and Information Sciences, Biomedical Informatics Research Laboratory, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad 44000, Pakistan.

Journal of Bioinformatics and Computational Biology
|August 1, 2018
PubMed
Summary
This summary is machine-generated.

Identifying host-pathogen interactions (HPIs) aids drug discovery for infectious diseases. This study optimizes machine learning models for HPI prediction, improving accuracy with weighted negative sampling and introducing the HOPITOR predictor.

Keywords:
Host–pathogen interactionsinteraction predictornegative samplingprotein–protein interactions

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in life sciences

Background:

  • Protein-protein interactions (PPIs) are crucial in molecular biology, with host-pathogen interactions (HPIs) driving infectious diseases.
  • Traditional wet lab methods for PPI detection are costly and not scalable.
  • Computational approaches are essential for predicting PPIs, especially HPIs, to accelerate drug discovery.

Purpose of the Study:

  • To develop and optimize machine learning models for predicting inter-species PPIs, with a focus on HPIs.
  • To address key challenges in HPI predictor development, including negative sample selection, sample weighting strategies, and sample size ratios.
  • To introduce HOPITOR, a novel computational tool for predicting human-viral protein interactions.

Main Methods:

  • Comparison of random versus DeNovo negative sampling strategies for HPI prediction.
  • Implementation and evaluation of a soft DeNovo approach, weighting negative samples inversely by similarity to positive examples.
  • Development and validation of the HOPITOR (Host-Pathogen Interaction Predictor) model.

Main Results:

  • DeNovo sampling demonstrated superior accuracy compared to random sampling for HPI prediction.
  • The soft DeNovo approach significantly improved generalization performance by assigning weights to negative samples.
  • HOPITOR was developed as an effective predictor for human-viral protein interactions.

Conclusions:

  • Optimized negative sampling strategies, particularly soft DeNovo weighting, enhance the accuracy and generalization of HPI prediction models.
  • The HOPITOR predictor provides a valuable computational resource for identifying human-viral protein interactions.
  • This work contributes to the advancement of computational methods for understanding infectious diseases and developing targeted therapeutics.