Computational Pipelines for Protein Ubiquitylation Analysis and Prediction.

Anastasiya Potapenko, Lyndal Henden, Ghasem Azemi, Antonio Di Ieva, Angela S Laird, Roger Chung, Albert Lee, Jennilee M Davidson

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Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Anastasiya PotapenkoMotor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.

Lyndal HendenMotor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.

Ghasem AzemiComputational Neurosurgery Lab (CNS), Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.

Antonio Di IevaComputational Neurosurgery Lab (CNS), Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.

Angela S LairdMotor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.

Roger ChungMotor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.

Albert LeeMotor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.

Jennilee M DavidsonMotor Neuron Disease Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia. jennilee.davidson@mq.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ubiquitylation is a crucial posttranslational modification that regulates cellular homeostasis and has been linked to a range of diseases. Identification and analysis of ubiquitylated proteins and ubiquitylation sites have historically been successfully performed by mass spectrometry from in vitro and in vivo samples. However, to address some of the challenges and improve the efficiency of ubiquitylation analysis by mass spectrometry, computational tools have been developed and are becoming increasingly popular. In this chapter, we summarize the available computational tools for predicting ubiquitylation in silico and the common approaches used to enrich ubiquitylation in samples for mass spectrometry and computational analysis. We subsequently provide simple steps for molecular biologists to follow for predicting ubiquitylation sites on proteins in silico, and we provide a computational approach to identify average ubiquitin branch sites across samples from ubiquitin-enriched mass spectrometry data.

Indexed as

Computational BiologyProteinsUbiquitinationAnimalsComputer SimulationHumansMass SpectrometryProtein Processing, Post-TranslationalSoftwareUbiquitinProteinsUbiquitinComputational analysisIn silicoIn vitroMass spectrometryUbiquitylationUbiquitylation predictionUbiquitylation sites

Identifiers

PMID42542522

DOI10.1016/j.cbpa.2012.12.009

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.