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Research Summary Blogs from UVA Qdata team

ACM BCB - Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences

2 minute read

Title: Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences

  • authors: Jack Lanchantin, Tom Weingarten, Arshdeep Sekhon, Clint Miller, Yanjun Qi
  • 2021 ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB)

PDF @ BioArxiv

Talk: Slide

Abstract

Viruses such as SARS-CoV-2 infect the human body by forming interactions between virus proteins and human proteins. However, experimental methods to find protein interactions are inadequate: large scale experiments are noisy, and small scale experiments are slow and expensive. Inspired by the recent successes of deep neural networks, we hypothesize that deep learning methods are well-positioned to aid and augment biological experiments, hoping to help identify more accurate virus-host protein interaction maps. Moreover, computational methods can quickly adapt to predict how virus mutations change protein interactions with the host proteins.

We propose DeepVHPPI, a novel deep learning framework combining a self-attention-based transformer architecture and a transfer learning training strategy to predict interactions between human proteins and virus proteins that have novel sequence patterns. We show that our approach outperforms the state-of-the-art methods significantly in predicting Virus–Human protein interactions for SARS-CoV-2, H1N1, and Ebola. In addition, we demonstrate how our framework can be used to predict and interpret the interactions of mutated SARS-CoV-2 Spike protein sequences.

We make all of our data and code available on GitHub https://github.com/QData/DeepVHPPI.

demo1 demo1 demo1

Citations

@article {Lanchantin2020.12.14.422772,
	author = {Lanchantin, Jack and Weingarten, Tom and Sekhon, Arshdeep and Miller, Clint and Qi, Yanjun},
	title = {Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences},
	elocation-id = {2020.12.14.422772},
	year = {2021},
	doi = {10.1101/2020.12.14.422772},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Viruses such as SARS-CoV-2 infect the human body by forming interactions between virus proteins and human proteins. However, experimental methods to find protein interactions are inadequate: large scale experiments are noisy, and small scale experiments are slow and expensive. Inspired by the recent successes of deep neural networks, we hypothesize that deep learning methods are well-positioned to aid and augment biological experiments, hoping to help identify more accurate virus-host protein interaction maps. Moreover, computational methods can quickly adapt to predict how virus mutations change protein interactions with the host proteins.We propose DeepVHPPI, a novel deep learning framework combining a self-attention-based transformer architecture and a transfer learning training strategy to predict interactions between human proteins and virus proteins that have novel sequence patterns. We show that our approach outperforms the state-of-the-art methods significantly in predicting Virus{\textendash}Human protein interactions for SARS-CoV-2, H1N1, and Ebola. In addition, we demonstrate how our framework can be used to predict and interpret the interactions of mutated SARS-CoV-2 Spike protein sequences.Availability We make all of our data and code available on GitHub https://github.com/QData/DeepVHPPI.ACM Reference Format Jack Lanchantin, Tom Weingarten, Arshdeep Sekhon, Clint Miller, and Yanjun Qi. 2021. Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences. In Proceedings of ACM Conference (ACM-BCB). ACM, New York, NY, USA, 10 pages. https://doi.org/??Competing Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2021/06/08/2020.12.14.422772},
	eprint = {https://www.biorxiv.org/content/early/2021/06/08/2020.12.14.422772.full.pdf},
	journal = {bioRxiv}
}

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