ArticleComputers in biology and medicine2024
Preventing future zoonosis: SARS-CoV-2 mutations enhance human-animal cross-transmission.
Article in Computers in biology and medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- AlphaFold 3: an unprecedent opportunity for fundamental research and drug development.Precision clinical medicine · 2025Review
- Current Advances and Applications of Animal Models in SARS-CoV-2 Pathogenesis and Vaccine Development.Microorganisms · 2025Review
- Viral Transmission in Sea Food Systems: Strategies for Control and Emerging Challenges.Foods (Basel, Switzerland) · 2025Review
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning.Virus evolution · 2025Article
- Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning.ArXiv · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The COVID-19 pandemic has driven substantial evolution of the SARS-CoV-2 virus, yielding subvariants that exhibit enhanced infectiousness in humans. However, this adaptive advantage may not universally extend to zoonotic transmission. In this work, we hypothesize that viral adaptations favoring animal hosts do not necessarily correlate with increased human infectivity. In addition, we consider the potential for gain-of-function mutations that could facilitate the virus's rapid evolution in humans following adaptation in animal hosts. Specifically, we identify the SARS-CoV-2 receptor-binding domain (RBD) mutations that enhance human-animal cross-transmission. To this end, we construct a multitask deep learning model, MT-TopLap trained on multiple deep mutational scanning datasets, to accurately predict the binding free energy changes upon mutation for the RBD to ACE2 of various species, including humans, cats, bats, deer, and hamsters. By analyzing these changes, we identified key RBD mutations such as Q498H in SARS-CoV-2 and R493K in the BA.2 variant that are likely to increase the potential for human-animal cross-transmission.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.