2023
Liu, Dan; Young, Francesca; Robertson, David L; Yuan, Ke
Prediction of virus-host association using protein language models and multiple instance learning Journal Article
In: bioRxiv, 2023.
Abstract | Links | BibTeX | Tags: Project 10, WP 1.2 Host prediction, WP 1.3 Virus-host interactions
@article{nokey,
title = {Prediction of virus-host association using protein language models and multiple instance learning},
author = {Dan Liu and Francesca Young and David L Robertson and Ke Yuan},
doi = {10.1101/2023.04.07.536023},
year = {2023},
date = {2023-04-08},
journal = {bioRxiv},
abstract = {Predicting virus-host association is essential to understand how viruses interact with host species, and discovering new therapeutics for viral diseases across humans and animals. Currently, the host of the majority of viruses is unknown. Here, we introduce EvoMIL, a deep learning method that predicts virus-host association at the species level from viral sequence only. The method combines a pre-trained large protein language model and attention-based multiple instance learning to allow protein-orientated predictions. Our results show that protein embeddings capture stronger predictive signals than traditional handcrafted features, including amino acids and DNA k-mers, and physio-chemical properties. EvoMIL binary classifiers achieve AUC values of over 0.95 for all prokaryotic and nearly 0.8 for almost all eukaryotic hosts. In multi-host prediction tasks, EvoMIL achieved median performance improvements of 8.6% in prokaryotic hosts and 1.8% in eukaryotic hosts. Furthermore, EvoMIL estimates the importance of single proteins in the prediction and maps them to an embedding landscape of all viral proteins, where proteins with similar functions are distinctly clustered together.},
keywords = {Project 10, WP 1.2 Host prediction, WP 1.3 Virus-host interactions},
pubstate = {published},
tppubtype = {article}
}
Predicting virus-host association is essential to understand how viruses interact with host species, and discovering new therapeutics for viral diseases across humans and animals. Currently, the host of the majority of viruses is unknown. Here, we introduce EvoMIL, a deep learning method that predicts virus-host association at the species level from viral sequence only. The method combines a pre-trained large protein language model and attention-based multiple instance learning to allow protein-orientated predictions. Our results show that protein embeddings capture stronger predictive signals than traditional handcrafted features, including amino acids and DNA k-mers, and physio-chemical properties. EvoMIL binary classifiers achieve AUC values of over 0.95 for all prokaryotic and nearly 0.8 for almost all eukaryotic hosts. In multi-host prediction tasks, EvoMIL achieved median performance improvements of 8.6% in prokaryotic hosts and 1.8% in eukaryotic hosts. Furthermore, EvoMIL estimates the importance of single proteins in the prediction and maps them to an embedding landscape of all viral proteins, where proteins with similar functions are distinctly clustered together.
2021
Goettsch, Winfried; Beerenwinkel, Niko; Deng, Li; Dölken, Lars; Dutilh, Bas E.; Erhard, Florian; Kaderali, Lars; von Kleist, Max; Marquet, Roland; Matthijnssens, Jelle; McCallin, Shawna; McMahon, Dino; Rattei, Thomas; van Rij, Ronald P.; Robertson, David L.; Schwemmle, Martin; Stern-Ginossar, Noam; Marz, Manja
ITN -- VIROINF: Understanding (Harmful) Virus-Host Interactions by Linking Virology and Bioinformatics Journal Article
In: Viruses, vol. 13, no. 5, pp. 766, 2021.
Abstract | Links | BibTeX | Tags: Project 01, Project 02, Project 03, Project 04, Project 05, Project 06, Project 07, Project 08, Project 09, Project 10, Project 11, Project 12, Project 13, Project 14, Project 15, WP 1.1 Virus identification, WP 1.2 Host prediction, WP 1.3 Virus-host interactions, WP 1.4 Virus regulation, WP 1.5 Virus products, WP 2.1 Microevolution: Virus quasispecies, WP 2.2 Macroevolution: Natural selection of viruses
@article{nokey,
title = {ITN -- VIROINF: Understanding (Harmful) Virus-Host Interactions by Linking Virology and Bioinformatics},
author = {Winfried Goettsch and Niko Beerenwinkel and Li Deng and Lars Dölken and Bas E. Dutilh and Florian Erhard and Lars Kaderali and Max von Kleist and Roland Marquet and Jelle Matthijnssens and Shawna McCallin and Dino McMahon and Thomas Rattei and Ronald P. {van Rij} and David L. Robertson and Martin Schwemmle and Noam Stern-Ginossar and Manja Marz},
doi = {10.3390/v13050766},
year = {2021},
date = {2021-04-27},
urldate = {2021-04-27},
journal = {Viruses},
volume = {13},
number = {5},
pages = {766},
abstract = {Many recent studies highlight the fundamental importance of viruses. Besides their important role as human and animal pathogens, their beneficial, commensal or harmful functions are poorly understood. By developing and applying tailored bioinformatical tools in important virological models, the Marie Skłodowska-Curie Initiative International Training Network VIROINF will provide a better understanding of viruses and the interaction with their hosts. This will open the door to validate methods of improving viral growth, morphogenesis and development, as well as to control strategies against unwanted microorganisms. The key feature of VIROINF is its interdisciplinary nature, which brings together virologists and bioinformaticians to achieve common goals.},
keywords = {Project 01, Project 02, Project 03, Project 04, Project 05, Project 06, Project 07, Project 08, Project 09, Project 10, Project 11, Project 12, Project 13, Project 14, Project 15, WP 1.1 Virus identification, WP 1.2 Host prediction, WP 1.3 Virus-host interactions, WP 1.4 Virus regulation, WP 1.5 Virus products, WP 2.1 Microevolution: Virus quasispecies, WP 2.2 Macroevolution: Natural selection of viruses},
pubstate = {published},
tppubtype = {article}
}
Many recent studies highlight the fundamental importance of viruses. Besides their important role as human and animal pathogens, their beneficial, commensal or harmful functions are poorly understood. By developing and applying tailored bioinformatical tools in important virological models, the Marie Skłodowska-Curie Initiative International Training Network VIROINF will provide a better understanding of viruses and the interaction with their hosts. This will open the door to validate methods of improving viral growth, morphogenesis and development, as well as to control strategies against unwanted microorganisms. The key feature of VIROINF is its interdisciplinary nature, which brings together virologists and bioinformaticians to achieve common goals.