Fast and scalable querying of eukaryotic linear motifs with gget elm
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1.
California Institute of Technology
Abstract
Motivation
Eukaryotic linear motifs (ELMs), or Short Linear Motifs, are protein interaction modules that play an essential role in cellular processes and signaling networks and are often involved in diseases like cancer. The ELM database is a collection of manually curated motif knowledge from scientific papers. It has become a crucial resource for investigating motif biology and recognizing candidate ELMs in novel amino acid sequences. Users can search amino acid sequences or UniProt Accessions on the ELM resource web interface. However, as with many web services, there are limitations in the swift processing of large-scale queries through the ELM web interface or API calls, and, therefore, integration into protein function analysis pipelines is limited.
Results
To allow swift, large-scale motif analyses on protein sequences using ELMs curated in the ELM database, we have extended the gget suite of Python and command line tools with a new module, gget elm, which does not rely on the ELM server for efficiently finding candidate ELMs in user-submitted amino acid sequences and UniProt Accessions. gget elm increases accessibility to the information stored in the ELM database and allows scalable searches for motif-mediated interaction sites in the amino acid sequences.
Availability and implementation
The manual and source code are available at https://github.com/pachterlab/gget.
Copyright and License
Acknowledgement
We thank the expert curators of the ELM database for providing an excellent resource. We thank Dr Toby Gibson for the valuable feedback on the manuscript. We also thank Candace Rypisi and the rest of the Summer Undergraduate Research Fellowships (SURF) program staff for facilitating valuable research opportunities for undergraduate students and mentorship opportunities for graduate students at Caltech. Illustrations in Fig. 2 were created with BioRender.com.
Contributions
L.L. and L.P. conceived the project after listening to a lecture by Prof. Amy E. Keating. L.L., C.H., and M.K. designed the gget elm approach. L.L. and C.H. wrote the gget elm software, with C.H. being the primary developer under the supervision of L.L. L.L. is the primary developer of the gget software, and M.K. is the primary developer of the ELM resource. L.L. wrote the initial draft of the manuscript. C.H., M.K., and L.P. provided feedback on the manuscript. All authors reviewed and approved the manuscript.
Funding
This work was supported by funding from the Biology and Bioengineering Division at the California Institute of Technology and the Chen Graduate Innovator Grant [CHEN.SYS3.CGIAFY21 to L.L.]. C.H. was supported by the Citadel Global Fixed Income SURF Fellowship. gget was supported by Pachter lab start-up funds.
Data Availability
Supplementary data are available at Bioinformatics online.
Conflict of Interest
None declared.
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Additional details
Identifiers
- PMCID
- PMC10927331
Funding
- California Institute of Technology
- Division of Biology and Biological Engineering
- California Institute of Technology
- Tianqiao and Chrissy Chen Institute for Neuroscience CHEN.SYS3.CGIAFY21
- California Institute of Technology
- Summer Undergraduate Research Fellowship