Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

PeSA: A Software Tool for Peptide Specificity Analysis

Emine Topcu, Kyle K. Biggar
doi: https://doi.org/10.1101/760140
Emine Topcu
Institute of Biochemistry and Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa Ontario, K1N 5B6 Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kyle K. Biggar
Institute of Biochemistry and Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa Ontario, K1N 5B6 Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: kyle_biggar@carleton.ca
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

ABSTRACT

The discovery of molecular interactions is crucial towards a better understanding of complex biological functions. Particularly protein-protein interactions (i.e., PPIs), which are responsible for a variety of cellular functions from epigenetic modifications to enzyme-substrate specificity, have been studied extensively over the past decades. Position-specific scoring matrices (PSSM) in particular are used extensively to help determine interaction specificity or candidate interaction motifs. However, not all studies successfully report their results as a candidate interaction motif. In many cases, this is the result of a lack of analysis tools for simple analysis and motif generation. Peptide Specificity Analyst (PeSA) is developed with the goal of filling this gap and providing an analysis software to aid peptide array analysis and subsequent motif generation. PeSA utilizes two models of motif creation: (1) frequency-based using a peptide list, and (2) weight-based using a quantified matrix. The ability to generate motifs effortlessly will make analyzing, interpreting and sharing peptide specificity study results in a simple and straightforward process.

Figure
  • Download figure
  • Open in new tab

HIGHLIGHTS

  • Biological motifs are widely used representations for peptide specificity analysis.

  • PeSA populates a list of peptides matching a set threshold from a quantified matrix.

  • Frequency-based motif using a peptide list to spot residue patterns.

  • Use of quantified matrices to create weight-based motifs using residue positions.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted September 08, 2019.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
PeSA: A Software Tool for Peptide Specificity Analysis
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
PeSA: A Software Tool for Peptide Specificity Analysis
Emine Topcu, Kyle K. Biggar
bioRxiv 760140; doi: https://doi.org/10.1101/760140
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
PeSA: A Software Tool for Peptide Specificity Analysis
Emine Topcu, Kyle K. Biggar
bioRxiv 760140; doi: https://doi.org/10.1101/760140

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3505)
  • Biochemistry (7346)
  • Bioengineering (5323)
  • Bioinformatics (20263)
  • Biophysics (10016)
  • Cancer Biology (7743)
  • Cell Biology (11300)
  • Clinical Trials (138)
  • Developmental Biology (6437)
  • Ecology (9951)
  • Epidemiology (2065)
  • Evolutionary Biology (13322)
  • Genetics (9361)
  • Genomics (12583)
  • Immunology (7701)
  • Microbiology (19021)
  • Molecular Biology (7441)
  • Neuroscience (41036)
  • Paleontology (300)
  • Pathology (1229)
  • Pharmacology and Toxicology (2137)
  • Physiology (3160)
  • Plant Biology (6860)
  • Scientific Communication and Education (1272)
  • Synthetic Biology (1896)
  • Systems Biology (5311)
  • Zoology (1089)