ArticleBMC genomics2013
The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations.
Article in BMC genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 56 citations in OpenAlex.
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- Enhancing missense variant pathogenicity prediction with protein language models using VariPred.Scientific reports · 2024Article
- Missense3D-PPI: A Web Resource to Predict the Impact of Missense Variants at Protein Interfaces Using 3D Structural Data.Journal of molecular biology · 2023Article
- Disruptor: Computational identification of oncogenic mutants disrupting protein-protein and protein-DNA interactions.Communications biology · 2023Article
- The DBSAV Database: Predicting Deleteriousness of Single Amino Acid Variations in the Human Proteome.Journal of molecular biology · 2021Article
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- Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated?Journal of molecular biology · 2019Article
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- IntPred: a structure-based predictor of protein-protein interaction sites.Bioinformatics (Oxford, England) · 2018Article
- Assigning function to natural allelic variation via dynamic modeling of gene network induction.Molecular systems biology · 2018Article
- Twenty novel mutations inMolecular genetics and metabolism reports · 2017Article
- The druggable genome and support for target identification and validation in drug development.Science translational medicine · 2017Article
- Identification and analysis of mutational hotspots in oncogenes and tumour suppressors.Oncotarget · 2017Article
- Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols.Methods in molecular biology (Clifton, N.J.) · 2017Article
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- Chemoprevention of Colonic Aberrant Crypt Foci by Novel Schiff Based Dichlorido(4-Methoxy-2-{[2-(Piperazin-4-Ium-1-Yl)Ethyl]Iminomethyl}Phenolate)Cd Complex in Azoxymethane-Induced Colorectal Cancer in Rats.Scientific reports · 2015Article
- Anatomy of protein disorder, flexibility and disease-related mutations.Frontiers in molecular biosciences · 2015Article
- Chemopreventive evaluation of a Schiff base derived copper (II) complex against azoxymethane-induced colorectal cancer in rats.PloS one · 2014Article
- Thoughts from SNP-SIG 2012: future challenges in the annotation of genetic variations.BMC genomics · 2013Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUnderstanding and predicting the effects of mutations on protein structure and phenotype is an increasingly important area. Genes for many genetically linked diseases are now routinely sequenced in the clinic. Previously we focused on understanding the structural effects of mutations, creating the SAAPdb resource.
resultsWe have updated SAAPdb to include 41% more SNPs and 36% more PDs. Introducing a hydrophobic residue on the surface, or a hydrophilic residue in the core, no longer shows significant differences between SNPs and PDs. We have improved some of the analyses significantly enhancing the analysis of clashes and of mutations to-proline and from-glycine. A new web interface has been developed allowing users to analyze their own mutations. Finally we have developed a machine learning method which gives a cross-validated accuracy of 0.846, considerably out-performing well known methods including SIFT and PolyPhen2 which give accuracies between 0.690 and 0.785.
conclusionsWe have updated SAAPdb and improved its analyses, but with the increasing rate with which mutation data are generated, we have created a new analysis pipeline and web interface. Results of machine learning using the structural analysis results to predict pathogenicity considerably outperform other methods.
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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.