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Abstract Motivationmetal-binding proteins have a central role in maintaining life processes. Nearly one-third of known protein structures contain metal ions that are used for a variety of needs, such as catalysis, DNA/RNA binding, protein structure stability, etc. Identifying metal-binding proteins is thus crucial for understanding the mechanisms of cellular activity. However, experimental annotation of protein metal-binding potential is severely lacking, while computational techniques are often imprecise and of limited applicability. Resultswe developed a novel machine learning-based method, mebipred, for identifying metal-binding proteins from sequence-derived features. This method is over 80% accurate in recognizing proteins that bind metal ion-containing ligands; the specific identity of 11 ubiquitously present metal ions can also be annotated. mebipred is reference-free, i.e. no sequence alignments are involved, and is thus faster than alignment-based methods; it is also more accurate than other sequence-based prediction methods. Additionally, mebipred can identify protein metal-binding capabilities from short sequence stretches, e.g. translated sequencing reads, and, thus, may be useful for the annotation of metal requirements of metagenomic samples. We performed an analysis of available microbiome data and found that ocean, hot spring sediments and soil microbiomes use a more diverse set of metals than human host-related ones. For human microbiomes, physiological conditions explain the observed metal preferences. Similarly, subtle changes in ocean sample ion concentration affect the abundance of relevant metal-binding proteins. These results highlight mebipred’s utility in analyzing microbiome metal requirements. Availability and implementationmebipred is available as a web server at services.bromberglab.org/mebipred and as a standalone package at https://pypi.org/project/mymetal/. Supplementary informationSupplementary data are available at Bioinformatics online.
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An in silico proteomics screen to predict and prioritize protein–protein interactions dependent on post-translationally modified motifs
Abstract MotivationThe development of proteomic methods for the characterization of domain/motif interactions has greatly expanded our understanding of signal transduction. However, proteomics-based binding screens have limitations including that the queried tissue or cell type may not harbor all potential interacting partners or post-translational modifications (PTMs) required for the interaction. Therefore, we sought a generalizable, complementary in silico approach to identify potentially novel motif and PTM-dependent binding partners of high priority. ResultsWe used as an initial example the interaction between the Src homology 2 (SH2) domains of the adaptor proteins CT10 regulator of kinase (CRK) and CRK-like (CRKL) and phosphorylated-YXXP motifs. Employing well-curated, publicly-available resources, we scored and prioritized potential CRK/CRKL–SH2 interactors possessing signature characteristics of known interacting partners. Our approach gave high priority scores to 102 of the >9000 YXXP motif-containing proteins. Within this 102 were 21 of the 25 curated CRK/CRKL–SH2-binding partners showing a more than 80-fold enrichment. Several predicted interactors were validated biochemically. To demonstrate generalized applicability, we used our workflow to predict protein–protein interactions dependent upon motif-specific arginine methylation. Our data demonstrate the applicability of our approach to, conceivably, any modular binding domain that recognizes a specific post-translationally modified motif. Supplementary informationSupplementary data are available at Bioinformatics online.
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- Award ID(s):
- 1656510
- PAR ID:
- 10393404
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 34
- Issue:
- 22
- ISSN:
- 1367-4803
- Page Range / eLocation ID:
- p. 3898-3906
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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