ArticleBriefings in bioinformatics2023
Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 11 citations in OpenAlex.
- StructMAn 2.0 Web: a web server for structural annotation of protein sequences and mutations.Nucleic acids research · 2025Article
- Accurate identification and mechanistic evaluation of pathogenic missense variants withProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Variant effect predictor correlation with functional assays is reflective of clinical classification performance.Genome biology · 2025Article
- Dynamics-based protein network features accurately discriminate neutral and rheostat positions.Biophysical journal · 2024Article
- CASTpFold: Computed Atlas of Surface Topography of the universe of protein Folds.Nucleic acids research · 2024Article
- CASTpFold: Computed Atlas of Surface Topography of the universe of protein Folds.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
We report the structure-based pathogenicity relationship identifier (SPRI), a novel computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutational clusters. SPRI can effectively extract properties determining pathogenicity encoded in protein structures, and can identify deleterious missense mutations of germ line origin associated with Mendelian diseases, as well as mutations of somatic origin associated with cancer drivers. It compares favorably to other methods in predicting deleterious mutations. Furthermore, SPRI can discover spatially organized pathogenic higher-order spatial clusters (patHOS) of deleterious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and driver mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole human proteome.
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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.