Cited by 14Protein Structure and DynamicsEnzyme Structure and FunctionComputational Drug Discovery Methods

PICKLUSTER: a protein-interface clustering and analysis plug-in for UCSF ChimeraX

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Genz, Luca R, Mulvaney, Thomas, Nair, Sanjana, Topf, Maya·2023·Bioinformatics

Published version of a preprint

Abstract

Summary: The identification and characterization of interfaces in protein complexes is crucial for understanding the mechanisms of molecular recognition. These interfaces are also attractive targets for protein inhibition. However, targeting protein interfaces can be challenging for large interfaces that consist of multiple interacting regions. We present PICKLUSTER [Protein Interface C(K)luster]—a program for identifying “sub-interfaces” in protein–protein complexes using distance clustering. The division of the interface into smaller “sub-interfaces” offers a more focused approach for targeting protein–protein interfaces. Availability and implementation: PICKLUSTER is implemented as a plug-in for the molecular visualization program UCSF ChimeraX 1.4 and subsequent versions. It is freely available for download in the ChimeraX Toolshed and https://gitlab.com/topf-lab/pickluster.git.

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Authors
Genz, L. R., Mulvaney, T., Nair, S., & Topf, M.
Affiliations
Leibniz Institute of Virology (LIV) (DE)
Corresponding author
Topf, Maya
Venue
Bioinformatics
Published
2023-10-16
Volume
39
Issue
11
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
OpenAlex
W4387691791
Cited by
14
References
2
Citations by year
2026: 5 · 2025: 9
FWCI
1.25
Type
journal article
Language
en
Open access
Locations
  • Bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
cooperation of Leibniz Institute of Virology
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords
protein-protein interfaces, distance clustering, protein-protein complex, molecular recognition, molecular visualization
MeSH terms
Proteins, Software, Cluster Analysis

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