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Computational Prediction of Short Linear Motifs from Protein Sequences

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Computational Peptidology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1268))

Abstract

Short Linear Motifs (SLiMs) are functional protein microdomains that typically mediate interactions between a short linear region in one protein and a globular domain in another. SLiMs usually occur in structurally disordered regions and mediate low affinity interactions. Most SLiMs are 3–15 amino acids in length and have 2–5 defined positions, making them highly likely to occur by chance and extremely difficult to identify. Nevertheless, our knowledge of SLiMs and capacity to predict them from protein sequence data using computational methods has advanced dramatically over the past decade. By considering the biological, structural, and evolutionary context of SLiM occurrences, it is possible to differentiate functional instances from chance matches in many cases and to identify new regions of proteins that have the features consistent with a SLiM-mediated interaction. Their simplicity also makes SLiMs evolutionarily labile and prone to independent origins on different sequence backgrounds through convergent evolution, which can be exploited for predicting novel SLiMs in proteins that share a function or interaction partner.

In this review, we explore our current knowledge of SLiMs and how it can be applied to the task of predicting them computationally from protein sequences. Rather than focusing on specific SLiM prediction tools, we provide an overview of the methods available and concentrate on principles that should continue to be paramount even in the light of future developments. We consider the relative merits of using regular expressions or profiles for SLiM discovery and discuss the main considerations for both predicting new instances of known SLiMs, and de novo prediction of novel SLiMs. In particular, we highlight the importance of correctly modelling evolutionary relationships and the probability of false positive predictions.

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Abbreviations

DMI:

Domain-motif interaction

ELM:

Eukaryotic linear motif

FPR:

False positive rate

GO:

Gene ontology

HMM:

Hidden Markov model

IDP:

Intrinsically disordered protein

IDR:

Intrinsically disordered region

LDMS:

(l, d) motif search

MnM:

Minimotif miner

MoRF:

Molecular recognition feature

MST:

Minimum spanning tree

PPI:

Protein-protein interaction

PSSM:

Position-specific scoring matrix

PTM:

Posttranslational modification

Regex:

Regular expression

SLiM:

Short linear motif

TPR:

True positive rate

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Author information

Authors and Affiliations

  1. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Room 263B, Biological Sciences Building, Building D26, Sydney, NSW, 2052, Australia

    Richard J. Edwards

  2. Centre for Biological Sciences, University of Southampton, Southampton, UK

    Richard J. Edwards & Nicolas Palopoli

  3. Institute for Life Sciences, University of Southampton, Southampton, UK

    Richard J. Edwards

Authors
  1. Richard J. Edwards
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  2. Nicolas Palopoli
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Correspondence to Richard J. Edwards .

Editor information

Editors and Affiliations

  1. Center of Bioinformatics (COBI), School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China

    Peng Zhou

  2. Center of Bioinformatics (COBI), School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China

    Jian Huang

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Edwards, R.J., Palopoli, N. (2015). Computational Prediction of Short Linear Motifs from Protein Sequences. In: Zhou, P., Huang, J. (eds) Computational Peptidology. Methods in Molecular Biology, vol 1268. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2285-7_6

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  • DOI: https://doi.org/10.1007/978-1-4939-2285-7_6

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2284-0

  • Online ISBN: 978-1-4939-2285-7

  • eBook Packages: Springer Protocols

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