Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Machine Learning on the Impacts of Mutations in the SARS-CoV-2 Spike RBD on Binding Affinity to Human ACE2 Based on Deep Mutational Scanning Data.

Biochemistry - 2 Sept 2025

Xia Hui, Wei Dacong, Guo Zhihong, Chung Lung Wa

Abstract excerpt

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to accumulate mutations in the spike receptor-binding domain (RBD) region, leading to the emergence of new variants that potentially change the binding affinity for the human angiotensin converting enzyme 2 (hACE2) receptor. Deep mutational scanning (DMS) is a powerful biochemical experimental technique that can characterize the impact of...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Angiotensin-Converting Enzyme 2
Share to COVID-19

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Machine Learning

Share to Models, Molecular

Share to Neural Networks, Computer

Share to Protein Domains

Share to SARS-CoV-2

Share to Spike Glycoprotein, Coronavirus

Source

Identifiers

PMID: 40811092
DOI: 10.1021/acs.biochem.4c00587

Genes

ACE2

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Machine Learning on the Impacts of Mutations in the SARS-CoV-2 Spike RBD on Binding Affinity to Human ACE2 Based on Deep Mutational Scanning Data.PMID 40811092Read ↗

Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.Semantic proximity Read ↗

Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2Semantic proximity Read ↗

Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptorSemantic proximity Read ↗

Predictive profiling of SARS-CoV-2 variants by deep mutational learningSemantic proximity Read ↗

Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set.Semantic proximity Read ↗

Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 Deep Mutational Interaction SetSemantic proximity Read ↗

A CNN model for predicting binding affinity changes between SARS-CoV-2 spike RBD variants and ACE2 homologuesSemantic proximity Read ↗

Modeling the Mutational Effects on Biochemical Phenotypes of SARS-CoV-2 Using Molecular Fields.Semantic proximity Read ↗

Quantification of biases in predictions of protein-protein binding affinity changes upon mutationsSemantic proximity Read ↗

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2.Semantic proximity Read ↗

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2Semantic proximity Read ↗

Impact of E484Q and L452R Mutations on Structure and Binding Behavior of SARS-CoV-2 B.1.617.1 Using Deep Learning AlphaFold2, Molecular Docking and Dynamics Simulation.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.