ArticlePloS one2017
Molecular modeling and molecular dynamic simulation of the effects of variants in the TGFBR2 kinase domain as a paradigm for interpretation of variants obtained by next generation sequencing.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Interpreting human genetic variation at atomic resolution.Nature genetics · 2026Review
- Multi-State Structural Genomics Enables Large-Scale, Mechanistic, and Context-Specific Classification of ABCC6 Genetic Variants Implicated in Calcification Diseases.International journal of molecular sciences · 2026Article
- Computational Analysis ofACS chemical neuroscience · 2025Article
- Protein Catalysis Through Structural Dynamics: A Comprehensive Analysis of Energy Conversion in Enzymatic Systems and Its Computational Limitations.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Assessing Protein Surface-Based Scoring for Interpreting Genomic Variants.International journal of molecular sciences · 2024Article
- MARK2 variants cause autism spectrum disorder via the downregulation of WNT/β-catenin signaling pathway.American journal of human genetics · 2024Article
- Computational structural genomics and clinical evidence suggest BCKDK gain-of-function may cause a potentially asymptomatic maple syrup urine disease phenotype.JIMD reports · 2024Article
- Structural and Dynamic Analyses of Pathogenic Variants inLife (Basel, Switzerland) · 2024Article
- Structural analysis ofPeerJ · 2022Article
- Interpreting Sequence Variation in PDAC-Predisposing Genes Using a Multi-Tier Annotation Approach Performed at the Gene, Patient, and Cohort Level.Frontiers in oncology · 2021Article
- Molecular modeling of LDLR aids interpretation of genomic variants.Journal of molecular medicine (Berlin, Germany) · 2019Article
- Benefits and Challenges of Rare Genetic Variation in Alzheimer's Disease.Current genetic medicine reports · 2019Article
- Structure and function prediction of arsenate reductase from Deinococcus indicus DR1.Journal of molecular modeling · 2019Article
- Kinact: a computational approach for predicting activating missense mutations in protein kinases.Nucleic acids research · 2018Article
- A profound computational study to prioritize the disease-causing mutations in PRPS1 gene.Metabolic brain disease · 2018Article
- The Importance of Biologic Knowledge and Gene Expression Context for Genomic Data Interpretation.Frontiers in genetics · 2018Article
- Assessing Human Genetic Variations in Glucose Transporter SLC2A10 and Their Role in Altering Structural and Functional Properties.Frontiers in genetics · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Variants in the TGFBR2 kinase domain cause several human diseases and can increase propensity for cancer. The widespread application of next generation sequencing within the setting of Individualized Medicine (IM) is increasing the rate at which TGFBR2 kinase domain variants are being identified. However, their clinical relevance is often uncertain. Consequently, we sought to evaluate the use of molecular modeling and molecular dynamics (MD) simulations for assessing the potential impact of variants within this domain. We documented the structural differences revealed by these models across 57 variants using independent MD simulations for each. Our simulations revealed various mechanisms by which variants may lead to functional alteration; some are revealed energetically, while others structurally or dynamically. We found that the ATP binding site and activation loop dynamics may be affected by variants at positions throughout the structure. This prediction cannot be made from the linear sequence alone. We present our structure-based analyses alongside those obtained using several commonly used genomics-based predictive algorithms. We believe the further mechanistic information revealed by molecular modeling will be useful in guiding the examination of clinically observed variants throughout the exome, as well as those likely to be discovered in the near future by clinical tests leveraging next-generation sequencing through IM efforts.
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