PRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effects
Open accessPublished version of a preprint
Abstract
Predicting the functional impact of point mutations is a critical challenge in genomics. PRESCOTT reconstructs complete mutational landscapes, identifies mutation-sensitive regions, and categorizes missense variants as benign, pathogenic, or variants of uncertain significance. Leveraging protein sequences, structural models, and population-specific allele frequencies, PRESCOTT surpasses existing methods in classifying ClinVar variants, the ACMG dataset, and over 1800 proteins from the Human Protein Dataset. Its online server facilitates mutation effect predictions for any protein and variant, and includes a database of over 19,000 human proteins, ready for population-specific analyses. Open access to residue-specific scores offers transparency and valuable insights for genomic medicine.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W. W., Hegde, M., Lyon, E., Spector, E., et al. (2015) · Genetics in Medicine · doi:10.1038/gim.2015.30 · Cited by 32,594 · cited 1× · see citing works
“The gnomAD dataset is used in the majority of rare disease analysis pipe-lines in both diagnostic and research settings around the world. gnomAD follows the rigorous guidance for the evaluation and aggregation of variant evidence determined by ACMG and Association for Molecular Pathology (AMP) [75].”
UniProt: the Universal Protein Knowledgebase in 2023
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1052 · Cited by 7,210 · see citing works
A genomic mutational constraint map using variation in 76,156 human genomes
Chen, S., Francioli, L. C., Goodrich, J. K., Collins, R. L., Kanai, M., Wang, Q., Alföldi, J., Watts, N. A., Vittal, C., Gauthier, L. D., et al. (2024) · Nature · doi:10.1038/s41586-023-06045-0 · Cited by 1,727 · see citing works
Deep mutational scanning: a new style of protein science
Fowler, D. M., & Fields, S. (2014) · Nature Methods · doi:10.1038/nmeth.3027 · Cited by 1,287 · see citing works
Unified rational protein engineering with sequence-based deep representation learning
Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131 · see citing works
Disease variant prediction with deep generative models of evolutionary data
Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J. K., Brock, K., Gal, Y., & Marks, D. S. (2021) · Nature · doi:10.1038/s41586-021-04043-8 · Cited by 791 · see citing works
Mutation effects predicted from sequence co-variation
Hopf, T. A., Ingraham, J. B., Poelwijk, F. J., Schärfe, C. P. I., Springer, M., Sander, C., & Marks, D. S. (2017) · Nature Biotechnology · doi:10.1038/nbt.3769 · Cited by 775 · see citing works
ProGen2: Exploring the boundaries of protein language models
Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N., & Madani, A. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.002 · Cited by 480 · see citing works
Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold
Steinegger, M., Mirdita, M., & Söding, J. (2019) · Nature Methods · doi:10.1038/s41592-019-0437-4 · Cited by 445 · cited 1× · see citing works
“Since Alphafold implementation uses several databases such as Uniref90 [68], BFD [69, 70], and Mgnify [71], due to their large sizes, Alphafold computational time becomes prohibitively high for thousands of proteins and, for this reason, we used Colabfold, based on highly clustered databases, to…”
Unexplored therapeutic opportunities in the human genome
Oprea, T. I., Bologa, C. G., Brunak, S., Campbell, A., Gan, G. N., Gaulton, A., Gomez, S. M., Guha, R., Hersey, A., Holmes, J., et al. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2018.14 · Cited by 378 · cited 1× · see citing works
“For the functional characterization of the human proteome, for instance, this bias is massive since there are only 5000 particularly well-studied human proteins [6] over the ~ 20,000 in the full proteome [3, 7, 8].”
Regional missense constraint improves variant deleteriousness prediction
Samocha, K. E., Kosmicki, J. A., Karczewski, K. J., O’Donnell-Luria, A. H., Pierce-Hoffman, E., MacArthur, D. G., Neale, B. M., & Daly, M. J. (2017) · doi:10.1101/148353 · Cited by 351 · see citing works
Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J. F., Abbeel, P., Sercu, T., & Rives, A. (2021) · doi:10.1101/2021.02.12.430858 · Cited by 350 · see citing works
Transformer protein language models are unsupervised structure learners
Rao, R., Meier, J., Sercu, T., Ovchinnikov, S., & Rives, A. (2020) · doi:10.1101/2020.12.15.422761 · Cited by 341 · see citing works
Large-scale investigation of the reasons why potentially important genes are ignored
Stoeger, T., Gerlach, M., Morimoto, R. I., & Nunes Amaral, L. A. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2006643 · Cited by 261 · see citing works
Predicting functional effect of missense variants using graph attention neural networks
Zhang, H., Xu, M. S., Fan, X., Chung, W. K., & Shen, Y. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00561-w · Cited by 98 · see citing works
Sinha, S., Eisenhaber, B., Jensen, L. J., Kalbuaji, B., & Eisenhaber, F. (2018) · PROTEOMICS · doi:10.1002/pmic.201800093 · Cited by 60 · see citing works
Flagged first, then by citations
- Authors
- Tekpinar, M., David, L., Henry, T., & Carbone, A.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Lyon 1 Université (FR)
- Corresponding author
- Carbone, Alessandra
- Venue
- Genome Biology
- Published
- 2025-05-06
- Volume
- 26
- Issue
- 1
- Pages
- 113-113
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC12054230
- OpenAlex
- W4410130203
- Cited by
- 18
- References
- 17
- Citations by year
- 2026: 9 · 2025: 6 · 2024: 2 · 2012: 1
- FWCI
- 8.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Agence Nationale de la Recherche (ANR-21-CE17-0046)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- point mutations, protein structure, epistasis, population allele frequencies, ClinVar variants, ACMG criteria, variants of uncertain significance, protein sequence analysis, online prediction server
- MeSH terms
- Epistasis, Genetic, Gene Frequency, Humans, Proteins, Software, Mutation, Missense
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
