ArticleiScience2022
Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 4 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
- Dengue fever mapping in Bangladesh: A spatial modeling approach.Health science reports · 2024Article
- Deep-learning-enabled protein-protein interaction analysis for prediction of SARS-CoV-2 infectivity and variant evolution.Nature medicine · 2023Article
- Towards mechanistic models of mutational effects: Deep learning on Alzheimer's Aβ peptide.Computational and structural biotechnology journal · 2023Article
- Neural networks to learn protein sequence-function relationships from deep mutational scanning data.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Deep mutational scanning (DMS) experiments have been performed on SARS-CoV-2's spike receptor-binding domain (RBD) and human angiotensin-converting enzyme 2 (ACE2) zinc-binding peptidase domain-both central players in viral infection and evolution and antibody evasion-quantifying how mutations impact biochemical phenotypes. We modeled biochemical phenotypes from massively parallel assays, using neural networks trained on protein sequence mutations in the virus and human host. Neural networks were significantly predictive of binding affinity, protein expression, and antibody escape, learning complex interactions and higher-order features that are difficult to capture with conventional methods from structural biology. Integrating the physicochemical properties of amino acids, such as hydrophobicity and long-range non-bonded energy per atom, significantly improved prediction (empirical p < 0.01). We observed concordance of the neural network predictions with molecular dynamics (multiple 500 ns or 1 μs all-atom) simulations of the spike protein-ACE2 interface, with critical implications for the use of deep learning to dissect molecular mechanisms.
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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.