Proteome-level assessment of origin, prevalence and function of Leucine-Aspartic Acid (LD) motifs.

Tanvir Alam, Meshari Alazmi, Rayan Mohammad Mahmoud Naser, Franceline Huser, Afaque Ahmad Imtiyaz Momin, Veronica Astro, Seungbeom Hong, Katarzyna Wiktoria Walkiewicz, Christian G Canlas, Raphaël Huser, Amal J. Ali, Jasmeen Merzaban, Antonio Adamo, Mariusz Jaremko, Lukasz Jaremko, Vladimir B. Bajic, Xin Gao, Stefan T. Arold

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

MOTIVATION:Leucine-aspartic acid (LD) motifs are short linear interaction motifs (SLiMs) that link paxillin family proteins to factors controlling cell adhesion, motility and survival. The existence and importance of LD motifs beyond the paxillin family is poorly understood. RESULTS:To enable a proteome-wide assessment of LD motifs, we developed an active-learning based framework (LDmotif finder; LDMF) that iteratively integrates computational predictions with experimental validation. Our analysis of the human proteome revealed a dozen new proteins containing LD motifs. We found that LD motif signalling evolved in unicellular eukaryotes more than 800 Myr ago, with paxillin and vinculin as core constituents, and nuclear export signal (NES) as a likely source of de novo LD motifs. We show that LD motif proteins form a functionally homogenous group, all being involved in cell morphogenesis and adhesion. This functional focus is recapitulated in cells by GFP-fused LD motifs, suggesting that it is intrinsic to the LD motif sequence, possibly through their effect on binding partners. Our approach elucidated the origin and dynamic adaptations of an ancestral SLiM, and can serve as a guide for the identification of other SLiMs for which only few representatives are known. AVAILABILITY:LDMF is freely available online at www.cbrc.kaust.edu.sa/ldmf; Source code is available at https://github.com/tanviralambd/LD/. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
Original languageEnglish (US)
Pages (from-to)1121-1128
Number of pages8
JournalBioinformatics (Oxford, England)
Volume36
Issue number4
DOIs
StatePublished - Oct 4 2019

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