Publication detail

Correlated Mutation Analysis Improvement by Proteomic Signal Processing

KUPKOVÁ, K. SEDLÁŘ, K. PROVAZNÍK, I.

Original Title

Correlated Mutation Analysis Improvement by Proteomic Signal Processing

English Title

Correlated Mutation Analysis Improvement by Proteomic Signal Processing

Type

conference paper

Language

en

Original Abstract

Correlated mutation analysis has become a powerful tool in protein contact prediction based on multiple sequence alignments of homologous proteins. Here, we introduce a novel method for the computation of correlated mutations based on prior transformation of protein sequences into their numerical form, which provides important biological properties about the amino acids. The method is then incorporated into a compact technique replacing the previously proposed Jaccard index. This results in the significant improvement of all of the statistical parameters used in this study.

English abstract

Correlated mutation analysis has become a powerful tool in protein contact prediction based on multiple sequence alignments of homologous proteins. Here, we introduce a novel method for the computation of correlated mutations based on prior transformation of protein sequences into their numerical form, which provides important biological properties about the amino acids. The method is then incorporated into a compact technique replacing the previously proposed Jaccard index. This results in the significant improvement of all of the statistical parameters used in this study.

Keywords

correlated mutations; protein contact map; numerical representation

Released

29.08.2016

Location

Brno

ISBN

978-80-214-5389-0

Book

Sborník příspěvků studentské konference Blansko 2016

Edition number

1

Pages from

47

Pages to

50

Pages count

105

URL

BibTex


@inproceedings{BUT127645,
  author="Kristýna {Kupková} and Karel {Sedlář} and Ivo {Provazník}",
  title="Correlated Mutation Analysis Improvement by
Proteomic Signal Processing",
  annote="Correlated mutation analysis has become a
powerful tool in protein contact prediction based on multiple
sequence alignments of homologous proteins. Here, we introduce
a novel method for the computation of correlated mutations
based on prior transformation of protein sequences into their
numerical form, which provides important biological properties
about the amino acids. The method is then incorporated into a
compact technique replacing the previously proposed Jaccard
index. This results in the significant improvement of all of the
statistical parameters used in this study.",
  booktitle="Sborník příspěvků studentské konference Blansko 2016",
  chapter="127645",
  howpublished="online",
  year="2016",
  month="august",
  pages="47--50",
  type="conference paper"
}