ArticleComputers in biology and medicine2022
D3AI-Spike: A deep learning platform for predicting binding affinity between SARS-CoV-2 spike receptor binding domain with multiple amino acid mutations and human angiotensin-converting enzyme 2.
Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 18 citations in OpenAlex.
- Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments.Journal of chemical information and modeling · 2026Article
- Generative prediction of real-world prevalent SARS-CoV-2 mutation with in silico virus evolution.Briefings in bioinformatics · 2025Article
- Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model.Bioresources and bioprocessing · 2025Article
- Biophysical principles predict fitness of SARS-CoV-2 variants.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Review
- Biophysical principles predict fitness of SARS-CoV-2 variants.bioRxiv : the preprint server for biology · 2024Article
- CellSTAR: a comprehensive resource for single-cell transcriptomic annotation.Nucleic acids research · 2024Article
- PandoGen: Generating complete instances of future SARS-CoV-2 sequences using Deep Learning.PLoS computational biology · 2024Article
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Authors and funding
10 authors at 2 institutions in 1 country.
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Abstract
The number of SARS-CoV-2 spike Receptor Binding Domain (RBD) with multiple amino acid mutations is huge due to random mutations and combinatorial explosions, making it almost impossible to experimentally determine their binding affinities to human angiotensin-converting enzyme 2 (hACE2). Although computational prediction is an alternative way, there is still no online platform to predict the mutation effect of RBD on the hACE2 binding affinity until now. In this study, we developed a free online platform based on deep learning models, namely D3AI-Spike, for quickly predicting binding affinity between spike RBD mutants and hACE2. The models based on CNN and CNN-RNN methods have the concordance index of around 0.8. Overall, the test results of the models are in agreement with the experimental data. To further evaluate the prediction power of D3AI-Spike, we predicted and experimentally determined the binding affinity of a VUM (variants under monitoring) variant IHU (B.1.640.2), which has fourteen amino acid substitutions, including N501Y and E484K, and 9 deletions located in the spike protein. The predicted average affinity score for wild-type RBD and IHU to hACE2 are 0.483 and 0.438, while the determined K
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