Statistical analysis of protein structural features: Relationships and PCA grouping

E. Del Prete, S. Dotolo, A. Marabotti, A. Facchiano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Subtle structural differences among homologous proteins may be responsible of the modulation of their functional properties. Therefore, we are exploring novel and strengthened methods to investigate in deep protein structure, and to analyze conformational features, in order to highlight relationships to functional properties. We selected some protein families based on their different structural class from CATH database, and studied in detail many structural parameters for these proteins. Some valuable results from Pearson’s correlation matrix have been validated with a Student’s t‐distribution test at a significance level of 5% (p‐value). We investigated in detail the best relationships among parameters, by using partial correlation. Moreover, PCA technique has been used for both single family and all families, in order to demonstrate how to find outliers for a family and extract new combined features. The correctness of this approach was borne out by the agreement of our results with geometric and structural properties, known or expected. In addition, we found unknown relationships, which will be object of further studies, in order to consider them as putative markers related to the peculiar structure‐function relationships for each family.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages33-43
Number of pages11
Volume8623
ISBN (Print)9783319244617
DOIs
Publication statusPublished - 2015
Event11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2014 - Cambridge, United Kingdom
Duration: Jun 26 2014Jun 28 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8623
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2014
CountryUnited Kingdom
CityCambridge
Period6/26/146/28/14

Keywords

  • Correlation
  • Global features
  • PCA
  • Protein structure

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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