Year: 2026
Type: article
Abstract: Effective diagnosis and treatment of rare genetic disorders requires the interpretation of a patient's genetic variants of unknown significance (VUSs). Today, clinical decision-making is primarily guided by gene-phenotype association databases and DNA-based scoring methods. Our web-accessible variant analysis pipeline, VUStruct, supplements these established approaches by deeply analyzing the downstream molecular impact of variation in context of 3D protein structure. VUStruct's growing impact is fueled by the co-proliferation of protein 3D structural models, gene sequencing, compute power, and artificial intelligence. Contextualizing VUSs in protein 3D structural models also illuminates longitudinal genomics studies and biochemical bench research focused on VUS, and we created VUStruct for clinicians and researchers alike. We now introduce VUStruct to the broad scientific community as a mature, web-facing, extensible, High-Performance Computing (HPC) software pipeline. VUStruct maps missense variants onto automatically selected protein structures and launches a broad range of analyses. These include energy-based assessments of protein folding and stability, pathogenicity prediction through spatial clustering analysis, and machine learning (ML) predictors of binding surface disruptions and nearby post-translational modification sites. The pipeline also considers the entire input set of VUS and identifies genes potentially involved in digenic disease. VUStruct's utility in clinical rare disease genome interpretation has been demonstrated through its analysis of over 175 Undiagnosed Disease Network (UDN) Patient cases. VUStruct-leveraged hypotheses have often informed clinicians in their consideration of additional patient testing, and we report here details from two cases where VUStruct was key to their solution. We also note successes with academic research collaborators, for whom VUStruct has informed research directions in both computational genomics and wet lab studies.
Source: PLoS Computational Biology
Authors: Christopher W. Moth, Jonathan H. Sheehan, Abdullah Al Mamun, R. Michael Sivley, Alican Gulsevin, David C. Rinker, Zenab F. Mchaourab, Undiagnosed Diseases Network, John A. Capra, Jens Meiler
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Funders: Alexander von Humboldt-Stiftung, Vanderbilt University, Deutscher Akademischer Austauschdienst, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, German Network for Bioinformatics Infrastructure, National Institutes of Health, National Institute on Aging, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Human Genome Research Institute, NIH Office of the Director, National Cancer Institute, National Institute of Allergy and Infectious Diseases, National Institute of Neurological Disorders and Stroke, U.S. National Library of Medicine
Awards: Personalized Structural Biology: Enabling Exome Interpretation in Undiagnosed Diseases, G1455670351, SFB 1052: Mechanismen der Adipositas, G1633987393, G1867085847, G1980014556, GPU-Accelerated Parallel Computer for Drug Discovery Applications, Structure based design of trimer interface epitope focused universal influenza vaccines, G2833659745, G3759311550, G4097572831, G4712284355, G482723523, G485242556, SFB 1423: Strukturelle Dynamik der GPCRAktivierung und Signaltransduktion, GPU-Accelerated Parallel Computer for Life Sciences Research, Vanderbilt Center for Undiagnosed Diseases (VCUD), G6029886166, Parallel Computer with High Memory Nodes, Washington University School of Medicine Undiagnosed Diseases Network Clinical Site, Mechanistic insights into Variants of Uncertain Significance (VUS) using novel EGFR variants as a paradigm, G8072767560, Structural Determinants of Allosteric Modulation of Brain GPCRs, SPP 2363: Nutzung und Entwicklung des maschinellen Lernens für molekulare Anwendungen Molekulares maschinelles Lernen, Washington University in St. Louis UDN Diagnostic Center of Excellence, Cerebrovascular involvement in Alzheimer's Disease: PAR4 Antagonism, Vanderbilt Center of Excellence for Undiagnosed Disease (VCEUD), Decrypting Variants of Uncertain Significance in Long-QT Syndrome
OpenAlex ID: W4401435285