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Molecular modeling in the age of clinical genomics, the enterprise of the next generation

Overview of attention for article published in Journal of Molecular Modeling, February 2017
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Title
Molecular modeling in the age of clinical genomics, the enterprise of the next generation
Published in
Journal of Molecular Modeling, February 2017
DOI 10.1007/s00894-017-3258-3
Pubmed ID
Authors

Jeremy W. Prokop, Jozef Lazar, Gabrielle Crapitto, D. Casey Smith, Elizabeth A. Worthey, Howard J. Jacob

Abstract

Protein modeling and molecular dynamics hold a unique toolset to aide in the characterization of clinical variants that may result in disease. Not only do these techniques offer the ability to study under characterized proteins, but they do this with the speed that is needed for time-sensitive clinical cases. In this paper we retrospectively study a clinical variant in the XIAP protein, C203Y, while addressing additional variants seen in patients with similar gastrointestinal phenotypes as the C203Y mutation. In agreement with the clinical tests performed on the C203Y patient, protein modeling and molecular dynamics suggest that direct interactions with RIPK2 and Caspase3 are altered by the C203Y mutation and subsequent loss of Zn coordination in the second BIR domain of XIAP. Interestingly, the variant does not appear to alter interactions with SMAC, resulting in further damage to the caspase and NOD2 pathways. To expand the computational strategy designed when studying XIAP, we have applied the molecular modeling tools to a list of 140 variants seen in CFTR associated with cystic fibrosis, and a list of undiagnosed variants in 17 different genes. This paper shows the exciting applications of molecular modeling in the classification and characterization of genetic variants identified in next generation sequencing. Graphical abstract XIAP in Caspase 3 and NOD2 signaling pathways.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 20%
Student > Master 4 20%
Student > Ph. D. Student 3 15%
Student > Bachelor 2 10%
Professor 2 10%
Other 3 15%
Unknown 2 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 30%
Biochemistry, Genetics and Molecular Biology 5 25%
Medicine and Dentistry 2 10%
Pharmacology, Toxicology and Pharmaceutical Science 2 10%
Unspecified 1 5%
Other 2 10%
Unknown 2 10%