Fulltext

GeneSV - an Approach to Help Characterize Possible Variations in Genomic and Protein Sequences

Zemla A 1 , Kostova T 2 , Gorchakov R 3 , Volkova E 3 , Beasley DW 4 , Cardosa J 5 Show all authors , Weaver SC 3 , Vasilakis N 3 , Naraghi-Arani P 6

Affiliations 

  • 1 Computing Application and Research Department, Lawrence Livermore National Laboratory, Livermore, CA, USA
  • 2 Center for Applied Scientific Computing, Lawrence Livermore National Laboratory
  • 3 Department of Pathology and Center for Biodefense and Emerging Infectious Diseases, University of Texas Medical Branch, Galveston, TX, USA. ; Center for Tropical Diseases, University of Texas Medical Branch, Galveston, TX 77555-0609. ; Institute for Human Infections and Immunity, University of Texas Medical Branch, Galveston, TX, USA
  • 4 Department of Pathology and Center for Biodefense and Emerging Infectious Diseases, University of Texas Medical Branch, Galveston, TX, USA. ; Center for Tropical Diseases, University of Texas Medical Branch, Galveston, TX 77555-0609. ; Institute for Human Infections and Immunity, University of Texas Medical Branch, Galveston, TX, USA. ; Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston
  • 5 Sentinext Therapeutics Sdn Bhd, Penang, Malaysia
  • 6 Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA
Bioinform Biol Insights, 2014 Jan 8;8:1-16.
PMID: 24453480 DOI: 10.4137/BBI.S13076

Abstract

A computational approach for identification and assessment of genomic sequence variability (GeneSV) is described. For a given nucleotide sequence, GeneSV collects information about the permissible nucleotide variability (changes that potentially preserve function) observed in corresponding regions in genomic sequences, and combines it with conservation/variability results from protein sequence and structure-based analyses of evaluated protein coding regions. GeneSV was used to predict effects (functional vs. non-functional) of 37 amino acid substitutions on the NS5 polymerase (RdRp) of dengue virus type 2 (DENV-2), 36 of which are not observed in any publicly available DENV-2 sequence. 32 novel mutants with single amino acid substitutions in the RdRp were generated using a DENV-2 reverse genetics system. In 81% (26 of 32) of predictions tested, GeneSV correctly predicted viability of introduced mutations. In 4 of 5 (80%) mutants with double amino acid substitutions proximal in structure to one another GeneSV was also correct in its predictions. Predictive capabilities of the developed system were illustrated on dengue RNA virus, but described in the manuscript a general approach to characterize real or theoretically possible variations in genomic and protein sequences can be applied to any organism.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

MeSH terms
Similar publications