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bioRxiv · 10.1101/760140

PeSA: A Software Tool for Peptide Specificity Analysis

Topcu, E.·Biggar, K. K.
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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.\n\nGRAPHICAL ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC=\"FIGDIR/small/760140v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (20K):\norg.highwire.dtl.DTLVardef@1668389org.highwire.dtl.DTLVardef@145bbfeorg.highwire.dtl.DTLVardef@1392eaeorg.highwire.dtl.DTLVardef@127e2c3_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIBiological motifs are widely used representations for peptide specificity analysis.\nC_LIO_LIPeSA populates a list of peptides matching a set threshold from a quantified matrix.\nC_LIO_LIFrequency-based motif using a peptide list to spot residue patterns.\nC_LIO_LIUse of quantified matrices to create weight-based motifs using residue positions.\nC_LI

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Topcu, E., Biggar, K. K.. 2019-09-08. PeSA: A Software Tool for Peptide Specificity Analysis. https://doi.org/10.1101/760140

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