CompariPSSM: a PSSM–PSSM comparison tool for motif-binding determinant analysis
File(s)
Published version
Author(s)
Tsitsa, Ifigenia
Krystkowiak, Izabella
Davey, Norman E
Type
Journal Article
Abstract
Motivation
Short linear motifs (SLiMs) are compact functional modules that mediate low-affinity protein–protein interactions. SLiMs direct the function of many dynamic signalling and regulatory complexes playing a central role in most biological processes of the cell. Motif-binding determinants describe the contribution of each residue in a motif-containing peptide to the affinity and specificity of binding to the motif-binding partner. Motif-binding determinants are generally defined as a motif consensus pattern or a position-specific scoring matrix (PSSM) encoding quantitative preferences. Motif-binding determinant comparison is an important motif analysis task and can be applied to motif annotation, classification, clustering, discovery and benchmarking. Currently, binding determinant comparison is generally performed by analysing consensus similarity; however, this ignores important quantitative information in both the consensus and non-consensus positions.
Results
We have created a new tool, CompariPSSM, that quantifies the similarity between motif-binding determinants using sliding window PSSM–PSSM comparison and scores PSSM similarity using a randomisation-based probabilistic framework. The tool has been benchmarked on curated data from the eukaryotic linear motif database and experimental data from proteomic peptidephage display. CompariPSSM can be used for peptide classification to validate motif classes, peptide clustering to group functionally related conserved disordered regions, and benchmarking experimental motif discovery methods.
Availability and implementation
CompariPSSM is available at https://slim.icr.ac.uk/projects/comparipssm.
Short linear motifs (SLiMs) are compact functional modules that mediate low-affinity protein–protein interactions. SLiMs direct the function of many dynamic signalling and regulatory complexes playing a central role in most biological processes of the cell. Motif-binding determinants describe the contribution of each residue in a motif-containing peptide to the affinity and specificity of binding to the motif-binding partner. Motif-binding determinants are generally defined as a motif consensus pattern or a position-specific scoring matrix (PSSM) encoding quantitative preferences. Motif-binding determinant comparison is an important motif analysis task and can be applied to motif annotation, classification, clustering, discovery and benchmarking. Currently, binding determinant comparison is generally performed by analysing consensus similarity; however, this ignores important quantitative information in both the consensus and non-consensus positions.
Results
We have created a new tool, CompariPSSM, that quantifies the similarity between motif-binding determinants using sliding window PSSM–PSSM comparison and scores PSSM similarity using a randomisation-based probabilistic framework. The tool has been benchmarked on curated data from the eukaryotic linear motif database and experimental data from proteomic peptidephage display. CompariPSSM can be used for peptide classification to validate motif classes, peptide clustering to group functionally related conserved disordered regions, and benchmarking experimental motif discovery methods.
Availability and implementation
CompariPSSM is available at https://slim.icr.ac.uk/projects/comparipssm.
Editor(s)
Kelso, Janet
Date Issued
2024-11-01
Date Acceptance
2024-10-27
Citation
Bioinformatics, 2024, 40 (11)
ISSN
1367-4811
Publisher
Oxford University Press
Journal / Book Title
Bioinformatics
Volume
40
Issue
11
Copyright Statement
© The Author(s) 2024. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
10.1093/bioinformatics/btae644
Publication Status
Published
Article Number
btae644
Date Publish Online
2024-10-29