ADDRESS: A Database of Disease-associated Human Variants Incorporating Protein Structure and Folding Stabilities. Issue 11 (28th May 2021)

• Record Type: Journal Article
• Authors: Woodard, Jaie; Zhang, Chengxin; Zhang, Yang
• Is Part Of: Journal of molecular biology. Volume 433:Issue 11(2021)
• Publication Date: 2021-05-28
• Subjects: database -- Single-nucleotide polymorphism -- disease variant -- pathogenicity prediction; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2021.166840
• Languages: English
• ISSNs: 0022-2836

Abstract

Graphical abstract: Highlights: Many missense-associated diseases stem from protein structure and stability changes. Development of a new database to associate pathogenic mutations with structures. Quantitative association of human variance with folding free energy changes. Pathogenic mutations are found to lead to lower stabilities and have more contacts. ADDRESS is useful for investigating detailed mechanisms of mutation pathogenicity. Abstract: Numerous human diseases are caused by mutations in genomic sequences. Since amino acid changes affect protein function through mechanisms often predictable from protein structure, the integration of structural and sequence data enables us to estimate with greater accuracy whether and how a given mutation will lead to disease. Publicly available annotated databases enable hypothesis assessment and benchmarking of prediction tools. However, the results are often presented as summary statistics or black box predictors, without providing full descriptive information. We developed a new semi-manually curated human variant database presenting information on the protein contact-map, sequence-to-structure mapping, amino acid identity change, and stability prediction for the popular UniProt database. We found that the profiles of pathogenic and benign missense polymorphisms can be effectively deduced using decision trees and comparative analyses based on the presented dataset. The database is made publicly available through https://zhanglab.ccmb.med.umich.edu/ADDRESS .