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1. Towards region-specific propagation of protein functions

   https://doi.org/10.1093/bioinformatics/bty834  

   Koo, Da Chen Emily ; Bonneau, Richard ; Wren, ed., Jonathan ( October 2018 , Bioinformatics)

   Due to the nature of experimental annotation, most protein function prediction methods operate at the protein-level, where functions are assigned to full-length proteins based on overall similarities. However, most proteins function by interacting with other proteins or molecules, and many functional associations should be limited to specific regions rather than the entire protein length. Most domain-centric function prediction methods depend on accurate domain family assignments to infer relationships between domains and functions, with regions that are unassigned to a known domain-family left out of functional evaluation. Given the abundance of residue-level annotations currently available, we present a function prediction methodology that automatically infers function labels of specific protein regions using protein-level annotations and multiple types of region-specific features.

   We apply this method to local features obtained from InterPro, UniProtKB and amino acid sequences and show that this method improves both the accuracy and region-specificity of protein function transfer and prediction. We compare region-level predictive performance of our method against that of a whole-protein baseline method using proteins with structurally verified binding sites and also compare protein-level temporal holdout predictive performances to expand the variety and specificity of GO terms we could evaluate. Our results can also serve as a starting point to categorize GO terms into region-specific and whole-protein terms and select prediction methods for different classes of GO terms.

   The code and features are freely available at: https://github.com/ek1203/rsfp.

   Supplementary data are available at Bioinformatics online.

    

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2. mebipred : identifying metal-binding potential in protein sequence

   https://doi.org/10.1093/bioinformatics/btac358  

   Aptekmann, A. A. ; Buongiorno, J. ; Giovannelli, D. ; Glamoclija, M. ; Ferreiro, D. U. ; Bromberg, Y. ; Cowen, ed., Lenore ( May 2022 , Bioinformatics)

   metal-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.

   we 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.

   mebipred is available as a web server at services.bromberglab.org/mebipred and as a standalone package at https://pypi.org/project/mymetal/.

   Supplementary data are available at Bioinformatics online.

    

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3. Structural and dynamic studies of the human RNA binding protein RBM3 reveals the molecular basis of its oligomerization and RNA recognition

   https://doi.org/10.1111/febs.16301  

   Roy, Sayantani ; Boral, Soumendu ; Maiti, Snigdha ; Kushwaha, Tushar ; Basak, Aditya J. ; Lee, Woonghee ; Basak, Amit ; Gholap, Shivajirao L. ; Inampudi, Krishna K. ; De, Soumya ( December 2021 , The FEBS Journal)

   Human RNA‐binding motif 3 protein (RBM3) is a cold‐shock protein which functions in various aspects of global protein synthesis, cell proliferation and apoptosis by interacting with the components of basal translational machinery. RBM3 plays important roles in tumour progression and cancer metastasis, and also has been shown to be involved in neuroprotection and endoplasmic reticulum stress response. Here, we have solved the solution NMR structure of the N‐terminal 84 residue RNA recognition motif (RRM) of RBM3. The remaining residues are rich in RGG and YGG motifs and are disordered. The RRM domain adopts a βαββαβ topology, which is found in many RNA‐binding proteins. NMR‐monitored titration experiments and molecular dynamic simulations show that the beta‐sheet and two loops form the RNA‐binding interface. Hydrogen bond, pi–pi and pi–cation are the key interactions between the RNA and the RRM domain. NMR, size exclusion chromatography and chemical cross‐linking experiments show that RBM3 forms oligomers in solution, which is favoured by decrease in temperature, thus, potentially linking it to its function as a cold‐shock protein. Temperature‐dependent NMR studies revealed that oligomerization of the RRM domain occurs via nonspecific interactions. Overall, this study provides the detailed structural analysis of RRM domain of RBM3, its interaction with RNA and the molecular basis of its temperature‐dependent oligomerization.

    

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4. CRK and NCK adaptors may functionally overlap in zebrafish neurodevelopment, as indicated by common binding partners and overlapping expression patterns

   https://doi.org/10.1002/1873-3468.14781  

   Stergas, Helaina R. ; Dillon‐Martin, Maeve ; Dumas, Caroline M. ; Hansen, Nicole A. ; Carasi‐Schwartz, Francesca J. ; D'Amico, Alex R. ; Finnegan, Kylie M. ; Juch, Uatchet ; Kane, Keeley R. ; Kaplan, Isabel E. ; et al ( December 2023 , FEBS Letters)

   CRK adaptor proteins are important for signal transduction mechanisms driving cell proliferation and positioning during vertebrate central nervous system development. Zebrafish lacking both CRK family members exhibit small, disorganized retinas with 50% penetrance. The goal of this study was to determine whether another adaptor protein might functionally compensate for the loss of CRK adaptors. Expression patterns in developing zebrafish, and bioinformatic analyses of the motifs recognized by their SH2 and SH3 domains, suggest NCK adaptors are well‐positioned to compensate for loss of CRK adaptors. In support of this hypothesis, proteomic analyses found CRK and NCK adaptors share overlapping interacting partners including known regulators of cell adhesion and migration, suggesting their functional intersection in neurodevelopment.

    

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5. SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences

   https://doi.org/10.1093/bioinformatics/btz324  

   Zhang, Jian ; Kurgan, Lukasz ( July 2019 , Bioinformatics)

   Accurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein–protein interactions, helps protein–protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use.

   We propose a novel, accurate and fast sequence-based predictor of PBRs that minimizes the cross-predictions. Our SCRIBER (SeleCtive pRoteIn-Binding rEsidue pRedictor) method takes advantage of three innovations: comprehensive dataset that covers multiple types of binding residues, novel types of inputs that are relevant to the prediction of PBRs, and an architecture that is tailored to reduce the cross-predictions. The dataset includes complete protein chains and offers improved coverage of binding annotations that are transferred from multiple protein–protein complexes. We utilize innovative two-layer architecture where the first layer generates a prediction of protein-binding, RNA-binding, DNA-binding and small ligand-binding residues. The second layer re-predicts PBRs by reducing overlap between PBRs and the other types of binding residues produced in the first layer. Empirical tests on an independent test dataset reveal that SCRIBER significantly outperforms current predictors and that all three innovations contribute to its high predictive performance. SCRIBER reduces cross-predictions by between 41% and 69% and our conservative estimates show that it is at least 3 times faster. We provide putative PBRs produced by SCRIBER for the entire human proteome and use these results to hypothesize that about 14% of currently known human protein domains bind proteins.

   SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/.

   Supplementary data are available at Bioinformatics online.

    

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