Presentation Title

Using SCA to identify functionally related residues in eRF1

Faculty Mentor

André Cavalcanti

Start Date

18-11-2017 2:15 PM

End Date

18-11-2017 3:15 PM

Location

BSC-Ursa Minor 123

Session

Poster 3

Type of Presentation

Poster

Subject Area

interdisciplinary

Abstract

Statistical Coupling Analysis (SCA) is a method originally developed by Ranganathan et al. (2009) based on the idea that amino acid residues within a protein function cooperatively and its amino acid sequence should be reflective of this interaction. Through the calculation of covariance between positions weighted by measures of positional conservation in a multiple sequence alignment, SCA identifies groups of correlated residues, termed “sectors”.

We have performed substantial modifications to the published versions of SCA, aiming for precise and automated analysis that incorporates high-quality and intuitive result visualizations.

A Python implementation of the method was developed this summer and applied to the identification of functionally related residues in the eukaryotic releasing factor 1 family (eRF1). SCA was able to identify three sectors of residues spatially grouped in the crystal structure of eRF1. Several residues cluster around previously identified essential motifs, indicating their biochemical significance. Inter-covariance between sectors was also observed, suggesting cooperation between sectors.

Future directions of the project include incorporating contact mapping and protein dynamics studies to evaluate sector validity and identify possible allosteric communication pathways. In combination with other bioinformatics programs, SCA will hopefully help elucidate the mechanism through which the domains in eRF1 coordinate and the interdependent characters of eRF1 and eRF3.

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Nov 18th, 2:15 PM Nov 18th, 3:15 PM

Using SCA to identify functionally related residues in eRF1

BSC-Ursa Minor 123

Statistical Coupling Analysis (SCA) is a method originally developed by Ranganathan et al. (2009) based on the idea that amino acid residues within a protein function cooperatively and its amino acid sequence should be reflective of this interaction. Through the calculation of covariance between positions weighted by measures of positional conservation in a multiple sequence alignment, SCA identifies groups of correlated residues, termed “sectors”.

We have performed substantial modifications to the published versions of SCA, aiming for precise and automated analysis that incorporates high-quality and intuitive result visualizations.

A Python implementation of the method was developed this summer and applied to the identification of functionally related residues in the eukaryotic releasing factor 1 family (eRF1). SCA was able to identify three sectors of residues spatially grouped in the crystal structure of eRF1. Several residues cluster around previously identified essential motifs, indicating their biochemical significance. Inter-covariance between sectors was also observed, suggesting cooperation between sectors.

Future directions of the project include incorporating contact mapping and protein dynamics studies to evaluate sector validity and identify possible allosteric communication pathways. In combination with other bioinformatics programs, SCA will hopefully help elucidate the mechanism through which the domains in eRF1 coordinate and the interdependent characters of eRF1 and eRF3.