Computational Approaches for Peroxisomal Protein Localization

Marco Anteghini*, Vitor A.P. Martins dos Santos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review


Computational approaches are practical when investigating putative peroxisomal proteins and for sub-peroxisomal protein localization in unknown protein sequences. Nowadays, advancements in computational methods and Machine Learning (ML) can be used to hasten the discovery of novel peroxisomal proteins and can be combined with more established computational methodologies. Here, we explain and list some of the most used tools and methodologies for novel peroxisomal protein detection and localization.

Original languageEnglish
Title of host publicationPeroxisomes
Subtitle of host publicationMethods and protocols
EditorsMichael Schrader
PublisherHumana Press
Number of pages7
ISBN (Electronic)9781071630488
ISBN (Print) 9781071630471
Publication statusPublished - 24 Mar 2023

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029


  • Cellular compartments
  • Machine learning
  • Peroxisome targeting signal
  • Sub-organelle localization
  • Subcellular localization


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