Alexander Gress1,2, Sanjay K Srikakulam1,2,3, Sebastian Keller1,2,4, Vasily Ramensky5,6, Olga V Kalinina1,7,8. 1. Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)/Helmholtz Centre for Infection Research (HZI), Saarbrücken 8: 66123, Germany. 2. Graduate School of Computer Science, Saarland University, Saarbrücken 5: 101990, Germany. 3. Interdisciplinary Graduate School of Natural Product Research, Saarland University, Saarbrücken 6: 119991, Germany. 4. Research Group Computational Biology, Max Planck Institute for Informatics, Saarbrücken 7: 66421, Germany. 5. National Medical Research Center for Therapy and Preventive Medicine of the Ministry of Healthcare of Russian Federation, Moscow, Russia. 6. Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia. 7. Medical Faculty, Saarland University, Homburg, Germany. 8. Center for Bioinformatics, Saarland Informatics Campus, Saarbrücken, Germany.
Abstract
BACKGROUND: Structural annotation of genetic variants in the context of intermolecular interactions and protein stability can shed light onto mechanisms of disease-related phenotypes. Three-dimensional structures of related proteins in complexes with other proteins, nucleic acids, or ligands enrich such functional interpretation, since intermolecular interactions are well conserved in evolution. RESULTS: We present d-StructMAn, a novel computational method that enables structural annotation of local genetic variants, such as single-nucleotide variants and in-frame indels, and implements it in a highly efficient and user-friendly tool provided as a Docker container. Using d-StructMAn, we annotated several very large sets of human genetic variants, including all variants from ClinVar and all amino acid positions in the human proteome. We were able to provide annotation for more than 46% of positions in the human proteome representing over 60% proteins. CONCLUSIONS: d-StructMAn is the first of its kind and a highly efficient tool for structural annotation of protein-coding genetic variation in the context of observed and potential intermolecular interactions. d-StructMAn is readily applicable to proteome-scale datasets and can be an instrumental building machine-learning tool for predicting genotype-to-phenotype relationships.
BACKGROUND: Structural annotation of genetic variants in the context of intermolecular interactions and protein stability can shed light onto mechanisms of disease-related phenotypes. Three-dimensional structures of related proteins in complexes with other proteins, nucleic acids, or ligands enrich such functional interpretation, since intermolecular interactions are well conserved in evolution. RESULTS: We present d-StructMAn, a novel computational method that enables structural annotation of local genetic variants, such as single-nucleotide variants and in-frame indels, and implements it in a highly efficient and user-friendly tool provided as a Docker container. Using d-StructMAn, we annotated several very large sets of human genetic variants, including all variants from ClinVar and all amino acid positions in the human proteome. We were able to provide annotation for more than 46% of positions in the human proteome representing over 60% proteins. CONCLUSIONS: d-StructMAn is the first of its kind and a highly efficient tool for structural annotation of protein-coding genetic variation in the context of observed and potential intermolecular interactions. d-StructMAn is readily applicable to proteome-scale datasets and can be an instrumental building machine-learning tool for predicting genotype-to-phenotype relationships.
Authors: Matthew J Betts; Qianhao Lu; YingYing Jiang; Armin Drusko; Oliver Wichmann; Mathias Utz; Ilse A Valtierra-Gutiérrez; Matthias Schlesner; Natalie Jaeger; David T Jones; Stefan Pfister; Peter Lichter; Roland Eils; Reiner Siebert; Peer Bork; Gordana Apic; Anne-Claude Gavin; Robert B Russell Journal: Nucleic Acids Res Date: 2014-11-11 Impact factor: 16.971
Authors: Jessica X Chong; Kati J Buckingham; Shalini N Jhangiani; Corinne Boehm; Nara Sobreira; Joshua D Smith; Tanya M Harrell; Margaret J McMillin; Wojciech Wiszniewski; Tomasz Gambin; Zeynep H Coban Akdemir; Kimberly Doheny; Alan F Scott; Dimitri Avramopoulos; Aravinda Chakravarti; Julie Hoover-Fong; Debra Mathews; P Dane Witmer; Hua Ling; Kurt Hetrick; Lee Watkins; Karynne E Patterson; Frederic Reinier; Elizabeth Blue; Donna Muzny; Martin Kircher; Kaya Bilguvar; Francesc López-Giráldez; V Reid Sutton; Holly K Tabor; Suzanne M Leal; Murat Gunel; Shrikant Mane; Richard A Gibbs; Eric Boerwinkle; Ada Hamosh; Jay Shendure; James R Lupski; Richard P Lifton; David Valle; Deborah A Nickerson; Michael J Bamshad Journal: Am J Hum Genet Date: 2015-07-09 Impact factor: 11.025
Authors: Melissa J Landrum; Jennifer M Lee; Mark Benson; Garth Brown; Chen Chao; Shanmuga Chitipiralla; Baoshan Gu; Jennifer Hart; Douglas Hoffman; Jeffrey Hoover; Wonhee Jang; Kenneth Katz; Michael Ovetsky; George Riley; Amanjeev Sethi; Ray Tully; Ricardo Villamarin-Salomon; Wendy Rubinstein; Donna R Maglott Journal: Nucleic Acids Res Date: 2015-11-17 Impact factor: 16.971