ArticleACS omega2020
Toward Developing Intuitive Rules for Protein Variant Effect Prediction Using Deep Mutational Scanning Data.
Article in ACS omega, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Machine learning framework for cost effective deep mutational scanning through targeted substitution profiling.BMC bioinformatics · 2026Article
- Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning.Nature communications · 2024Article
- Simultaneous enhancement of multiple functional properties using evolution-informed protein design.Nature communications · 2024Article
- Phenotype Design Space Provides a Mechanistic Framework Relating Molecular Parameters to Phenotype Diversity Available for Selection.Journal of molecular evolution · 2023Article
- Simultaneous enhancement of multiple functional properties using evolution-informed protein design.bioRxiv : the preprint server for biology · 2023Article
- Automated optimisation of solubility and conformational stability of antibodies and proteins.Nature communications · 2023Article
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- Embeddings from protein language models predict conservation and variant effects.Human genetics · 2022Article
- Mutation-Specific Differences in Kv7.1 (International journal of molecular sciences · 2022Review
- Facilitating Machine Learning-Guided Protein Engineering with Smart Library Design and Massively Parallel Assays.Advanced genetics (Hoboken, N.J.) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein structure and function can be severely altered by even a single amino acid mutation. Predictions of mutational effects using extensive artificial intelligence (AI)-based models, although accurate, remain as enigmatic as the experimental observations in terms of improving intuitions about the contributions of various factors. Inspired by Lipinski's rules for drug-likeness, we devise simple thresholding criteria on five different descriptors such as conservation, which have so far been limited to qualitative interpretations such as high conservation implies high mutational effect. We analyze systematic deep mutational scanning data of all possible single amino acid substitutions on seven proteins (25153 mutations) to first define these thresholds and then to evaluate the scope and limits of the predictions. At this stage, the approach allows us to comment easily and with a low error rate on the subset of mutations classified as neutral or deleterious by all of the descriptors. We hope that complementary to the accurate AI predictions, these thresholding rules or their subsequent modifications will serve the purpose of codifying the knowledge about the effects of mutations.
Identifiers
What OpenQuestion holds
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.