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1. 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)

   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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2. Text mining for modeling of protein complexes enhanced by machine learning

   https://doi.org/10.1093/bioinformatics/btaa823  

   Badal, Varsha D; Kundrotas, Petras J; Vakser, Ilya A (September 2020, Bioinformatics)

   Elofsson, Arne (Ed.)

   Abstract Motivation Procedures for structural modeling of protein–protein complexes (protein docking) produce a number of models which need to be further analyzed and scored. Scoring can be based on independently determined constraints on the structure of the complex, such as knowledge of amino acids essential for the protein interaction. Previously, we showed that text mining of residues in freely available PubMed abstracts of papers on studies of protein–protein interactions may generate such constraints. However, absence of post-processing of the spotted residues reduced usability of the constraints, as a significant number of the residues were not relevant for the binding of the specific proteins. Results We explored filtering of the irrelevant residues by two machine learning approaches, Deep Recursive Neural Network (DRNN) and Support Vector Machine (SVM) models with different training/testing schemes. The results showed that the DRNN model is superior to the SVM model when training is performed on the PMC-OA full-text articles and applied to classification (interface or non-interface) of the residues spotted in the PubMed abstracts. When both training and testing is performed on full-text articles or on abstracts, the performance of these models is similar. Thus, in such cases, there is no need to utilize computationally demanding DRNN approach, which is computationally expensive especially at the training stage. The reason is that SVM success is often determined by the similarity in data/text patterns in the training and the testing sets, whereas the sentence structures in the abstracts are, in general, different from those in the full text articles. Availabilityand implementation The code and the datasets generated in this study are available at https://gitlab.ku.edu/vakser-lab-public/text-mining/-/tree/2020-09-04. Supplementary information Supplementary data are available at Bioinformatics online. 

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3. PIQLE: protein–protein interface quality estimation by deep graph learning of multimeric interaction geometries

   https://doi.org/10.1093/bioadv/vbad070  

   Shuvo, Md Hossain; Karim, Mohimenul; Roche, Rahmatullah; Bhattacharya, Debswapna; Gromiha, ed., Michael (June 2023, Bioinformatics Advances)

   Abstract MotivationAccurate modeling of protein–protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. ResultsHere, we present PIQLE, a deep graph learning method for protein–protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of individual interactions between the interfacial residues using a multi-head graph attention network and then probabilistically combines the estimated quality for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods including DProQA, TRScore, GNN-DOVE and DOVE on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study and comparison with the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring reveal that the performance gains are connected to the effectiveness of the multi-head graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE. Availability and implementationAn open-source software implementation of PIQLE is freely available at https://github.com/Bhattacharya-Lab/PIQLE. Supplementary informationSupplementary data are available at Bioinformatics Advances online. 

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4. An in silico proteomics screen to predict and prioritize protein–protein interactions dependent on post-translationally modified motifs

   https://doi.org/10.1093/bioinformatics/bty434  

   Schmoker, Anna M.; Driscoll, Heather E.; Geiger, Stefanie R.; Vincent, James J.; Ebert, Alicia M.; Ballif, Bryan A.; Wren, ed., Jonathan (June 2018, Bioinformatics)

   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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5. PhyloWGA : chromosome-aware phylogenetic interrogation of whole genome alignments

   https://doi.org/10.1093/bioinformatics/btaa884  

   Adams, Richard H; Castoe, Todd A; DeGiorgio, Michael (December 2020, Bioinformatics)

   Ponty, Yann (Ed.)

   Abstract Summary Here, we present PhyloWGA, an open source R package for conducting phylogenetic analysis and investigation of whole genome data. Availabilityand implementation Available at Github (https://github.com/radamsRHA/PhyloWGA). Supplementary information Supplementary data are available at Bioinformatics online. 

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