250 related articles for article (PubMed ID: 32096919)
1. Analysis of protein missense alterations by combining sequence- and structure-based methods.
Gyulkhandanyan A; Rezaie AR; Roumenina L; Lagarde N; Fremeaux-Bacchi V; Miteva MA; Villoutreix BO
Mol Genet Genomic Med; 2020 Apr; 8(4):e1166. PubMed ID: 32096919
[TBL] [Abstract][Full Text] [Related]
2. CoDP: predicting the impact of unclassified genetic variants in MSH6 by the combination of different properties of the protein.
Terui H; Akagi K; Kawame H; Yura K
J Biomed Sci; 2013 Apr; 20(1):25. PubMed ID: 23621914
[TBL] [Abstract][Full Text] [Related]
3. Missense3D-PPI: A Web Resource to Predict the Impact of Missense Variants at Protein Interfaces Using 3D Structural Data.
Pennica C; Hanna G; Islam SA; Sternberg MJE; David A
J Mol Biol; 2023 Jul; 435(14):168060. PubMed ID: 37356905
[TBL] [Abstract][Full Text] [Related]
4. In silico analyses of missense mutations in coagulation factor VIII: identification of severity determinants of haemophilia A.
Sengupta M; Sarkar D; Ganguly K; Sengupta D; Bhaskar S; Ray K
Haemophilia; 2015 Sep; 21(5):662-9. PubMed ID: 25854144
[TBL] [Abstract][Full Text] [Related]
5. Insight into the structural and functional analysis of the impact of missense mutation on cytochrome P450 oxidoreductase.
Kumar R; Jayaraman M; Ramadas K; Chandrasekaran A
J Mol Graph Model; 2020 Nov; 100():107708. PubMed ID: 32805558
[TBL] [Abstract][Full Text] [Related]
6. Unveiling the influence of factor VIII physicochemical properties on hemophilia A phenotype through an in silico methodology.
Meireles MR; Stelmach LH; Bandinelli E; Vieira GF
Comput Methods Programs Biomed; 2022 Jun; 219():106768. PubMed ID: 35367915
[TBL] [Abstract][Full Text] [Related]
7. In silico enhancement of the stability and activity of keratinocyte growth factor.
Poorebrahim M; Sadeghi S; Ghorbani R; Asghari M; Abazari MF; Kalhor H; Rahimi H
J Theor Biol; 2017 Apr; 418():111-121. PubMed ID: 28093295
[TBL] [Abstract][Full Text] [Related]
8. Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated?
Ittisoponpisan S; Islam SA; Khanna T; Alhuzimi E; David A; Sternberg MJE
J Mol Biol; 2019 May; 431(11):2197-2212. PubMed ID: 30995449
[TBL] [Abstract][Full Text] [Related]
9. Characterizing the combined effects of cytochrome P450 missense variation within star allele definitions.
Khoza N; Twesigomwe D; Othman H
Pharmacogenomics; 2023 Jul; 24(10):561-578. PubMed ID: 37503750
[No Abstract] [Full Text] [Related]
10. Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics.
Ernst C; Hahnen E; Engel C; Nothnagel M; Weber J; Schmutzler RK; Hauke J
BMC Med Genomics; 2018 Mar; 11(1):35. PubMed ID: 29580235
[TBL] [Abstract][Full Text] [Related]
11. meta-PPISP: a meta web server for protein-protein interaction site prediction.
Qin S; Zhou HX
Bioinformatics; 2007 Dec; 23(24):3386-7. PubMed ID: 17895276
[TBL] [Abstract][Full Text] [Related]
12. Curated multiple sequence alignment for the Adenomatous Polyposis Coli (APC) gene and accuracy of in silico pathogenicity predictions.
Karabachev AD; Martini DJ; Hermel DJ; Solcz D; Richardson ME; Pesaran T; Sarkar IN; Greenblatt MS
PLoS One; 2020; 15(8):e0233673. PubMed ID: 32750050
[TBL] [Abstract][Full Text] [Related]
13. Computational Analysis of Missense Variants of G Protein-Coupled Receptors Involved in the Neuroendocrine Regulation of Reproduction.
Min L; Nie M; Zhang A; Wen J; Noel SD; Lee V; Carroll RS; Kaiser UB
Neuroendocrinology; 2016; 103(3-4):230-9. PubMed ID: 26088945
[TBL] [Abstract][Full Text] [Related]
14. Exploring the impact of CYP11A1's missense SNPs on the interaction between CYP11A1 and cholesterol: A comprehensive structural analysis and MD simulation study.
Redouane S; Harmak H; Elkarhat Z; Charoute H; Malki A; Barakat A; Rouba H
Comput Biol Chem; 2023 Oct; 106():107937. PubMed ID: 37552904
[TBL] [Abstract][Full Text] [Related]
15. Missense3D-DB web catalogue: an atom-based analysis and repository of 4M human protein-coding genetic variants.
Khanna T; Hanna G; Sternberg MJE; David A
Hum Genet; 2021 May; 140(5):805-812. PubMed ID: 33502607
[TBL] [Abstract][Full Text] [Related]
16. Using SIFT and PolyPhen to predict loss-of-function and gain-of-function mutations.
Flanagan SE; Patch AM; Ellard S
Genet Test Mol Biomarkers; 2010 Aug; 14(4):533-7. PubMed ID: 20642364
[TBL] [Abstract][Full Text] [Related]
17. Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods.
Mathe E; Olivier M; Kato S; Ishioka C; Hainaut P; Tavtigian SV
Nucleic Acids Res; 2006; 34(5):1317-25. PubMed ID: 16522644
[TBL] [Abstract][Full Text] [Related]
18. Interpreting missense variants: comparing computational methods in human disease genes CDKN2A, MLH1, MSH2, MECP2, and tyrosinase (TYR).
Chan PA; Duraisamy S; Miller PJ; Newell JA; McBride C; Bond JP; Raevaara T; Ollila S; Nyström M; Grimm AJ; Christodoulou J; Oetting WS; Greenblatt MS
Hum Mutat; 2007 Jul; 28(7):683-93. PubMed ID: 17370310
[TBL] [Abstract][Full Text] [Related]
19. DARVIC: Dihedral angle-reliant variant impact classifier for functional prediction of missense VUS.
Lagniton PNP; Tam B; Wang SM
Comput Methods Programs Biomed; 2023 Aug; 238():107596. PubMed ID: 37201251
[TBL] [Abstract][Full Text] [Related]
20. CoagMDB: a database analysis of missense mutations within four conserved domains in five vitamin K-dependent coagulation serine proteases using a text-mining tool.
Saunders RE; Perkins SJ
Hum Mutat; 2008 Mar; 29(3):333-44. PubMed ID: 18058827
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
