Proteome-level assessment of origin, prevalence and function of leucine-aspartic acid (LD) motifs

Author:

Alam Tanvir1,Alazmi Meshari1,Naser Rayan2,Huser Franceline2,Momin Afaque A2ORCID,Astro Veronica3,Hong SeungBeom2,Walkiewicz Katarzyna W2,Canlas Christian G4,Huser Raphaël5,Ali Amal J3,Merzaban Jasmeen3,Adamo Antonio3,Jaremko Mariusz3,Jaremko Łukasz3,Bajic Vladimir B1ORCID,Gao Xin1,Arold Stefan T2

Affiliation:

1. Computational Bioscience Research Center (CBRC), Division of Computer, Electrical and Mathematical Sciences & Engineering (CEMSE)

2. Computational Bioscience Research Center (CBRC), Division of Biological and Environmental Sciences and Engineering (BESE)

3. Division of Biological and Environmental Sciences and Engineering (BESE)

4. Core Labs and King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

5. Division of Computer, Electrical and Mathematical Sciences & Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

Abstract

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 (LD motif 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 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 and implementation 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.

Funder

Supercomputing Laboratory

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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