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1. Identifying antimicrobial peptides using word embedding with deep recurrent neural networks

   https://doi.org/10.1093/bioinformatics/bty937  

   Hamid, Md-Nafiz ; Friedberg, Iddo ; Hancock, ed., John ( November 2018 , Bioinformatics)

   Antibiotic resistance constitutes a major public health crisis, and finding new sources of antimicrobial drugs is crucial to solving it. Bacteriocins, which are bacterially produced antimicrobial peptide products, are candidates for broadening the available choices of antimicrobials. However, the discovery of new bacteriocins by genomic mining is hampered by their sequences’ low complexity and high variance, which frustrates sequence similarity-based searches.

   Here we use word embeddings of protein sequences to represent bacteriocins, and apply a word embedding method that accounts for amino acid order in protein sequences, to predict novel bacteriocins from protein sequences without using sequence similarity. Our method predicts, with a high probability, six yet unknown putative bacteriocins in Lactobacillus. Generalized, the representation of sequences with word embeddings preserving sequence order information can be applied to peptide and protein classification problems for which sequence similarity cannot be used.

   Data and source code for this project are freely available at: https://github.com/nafizh/NeuBI.

   Supplementary data are available at Bioinformatics online.

    

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2. Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN

   https://doi.org/10.1038/s41592-024-02191-z  

   Rosen, Yanay ; Brbić, Maria ; Roohani, Yusuf ; Swanson, Kyle ; Li, Ziang ; Leskovec, Jure ( February 2024 , Nature Methods)

   Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, interspecies genomic differences limit the joint analysis of cross-species datasets to homologous genes. Here we present SATURN, a deep learning method for learning universal cell embeddings that encodes genes’ biological properties using protein language models. By coupling protein embeddings from language models with RNA expression, SATURN integrates datasets profiled from different species regardless of their genomic similarity. SATURN can detect functionally related genes coexpressed across species, redefining differential expression for cross-species analysis. Applying SATURN to three species whole-organism atlases and frog and zebrafish embryogenesis datasets, we show that SATURN can effectively transfer annotations across species, even when they are evolutionarily remote. We also demonstrate that SATURN can be used to find potentially divergent gene functions between glaucoma-associated genes in humans and four other species.

    

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3. HFSP: high speed homology-driven function annotation of proteins

   https://doi.org/10.1093/bioinformatics/bty262  

   Mahlich, Yannick ; Steinegger, Martin ; Rost, Burkhard ; Bromberg, Yana ( June 2018 , Bioinformatics)

   The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins. The increase in the number of available sequences, however, has drastically increased the search space, thus significantly slowing down alignment methods.

   Here we describe homology-derived functional similarity of proteins (HFSP), a novel computational method that uses results of a high-speed alignment algorithm, MMseqs2, to infer functional similarity of proteins on the basis of their alignment length and sequence identity. We show that our method is accurate (85% precision) and fast (more than 40-fold speed increase over state-of-the-art). HFSP can help correct at least a 16% error in legacy curations, even for a resource of as high quality as Swiss-Prot. These findings suggest HFSP as an ideal resource for large-scale functional annotation efforts.

   Supplementary data are available at Bioinformatics online.

    

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4. GLIDER: function prediction from GLIDE-based neighborhoods

   https://doi.org/10.1093/bioinformatics/btac322  

   Devkota, Kapil ; Schmidt, Henri ; Werenski, Matt ; Murphy, James M. ; Erden, Mert ; Arsenescu, Victor ; Cowen, Lenore J. ; Valencia, ed., Alfonso ( May 2022 , Bioinformatics)

   Protein function prediction, based on the patterns of connection in a protein–protein interaction (or association) network, is perhaps the most studied of the classical, fundamental inference problems for biological networks. A highly successful set of recent approaches use random walk-based low-dimensional embeddings that tend to place functionally similar proteins into coherent spatial regions. However, these approaches lose valuable local graph structure from the network when considering only the embedding. We introduce GLIDER, a method that replaces a protein–protein interaction or association network with a new graph-based similarity network. GLIDER is based on a variant of our previous GLIDE method, which was designed to predict missing links in protein–protein association networks, capturing implicit local and global (i.e. embedding-based) graph properties.

   GLIDER outperforms competing methods on the task of predicting GO functional labels in cross-validation on a heterogeneous collection of four human protein–protein association networks derived from the 2016 DREAM Disease Module Identification Challenge, and also on three different protein–protein association networks built from the STRING database. We show that this is due to the strong functional enrichment that is present in the local GLIDER neighborhood in multiple different types of protein–protein association networks. Furthermore, we introduce the GLIDER graph neighborhood as a way for biologists to visualize the local neighborhood of a disease gene. As an application, we look at the local GLIDER neighborhoods of a set of known Parkinson’s Disease GWAS genes, rediscover many genes which have known involvement in Parkinson’s disease pathways, plus suggest some new genes to study.

   All code is publicly available and can be accessed here: https://github.com/kap-devkota/GLIDER.

   Supplementary data are available at Bioinformatics online.

    

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5. ModuleAlign: module-based global alignment of protein–protein interaction networks

   https://doi.org/10.1093/bioinformatics/btw447  

   Hashemifar, Somaye ; Ma, Jianzhu ; Naveed, Hammad ; Canzar, Stefan ; Xu, Jinbo ( August 2016 , Bioinformatics)

   As an increasing amount of protein–protein interaction (PPI) data becomes available, their computational interpretation has become an important problem in bioinformatics. The alignment of PPI networks from different species provides valuable information about conserved subnetworks, evolutionary pathways and functional orthologs. Although several methods have been proposed for global network alignment, there is a pressing need for methods that produce more accurate alignments in terms of both topological and functional consistency.

   In this work, we present a novel global network alignment algorithm, named ModuleAlign, which makes use of local topology information to define a module-based homology score. Based on a hierarchical clustering of functionally coherent proteins involved in the same module, ModuleAlign employs a novel iterative scheme to find the alignment between two networks. Evaluated on a diverse set of benchmarks, ModuleAlign outperforms state-of-the-art methods in producing functionally consistent alignments. By aligning Pathogen–Human PPI networks, ModuleAlign also detects a novel set of conserved human genes that pathogens preferentially target to cause pathogenesis.

   http://ttic.uchicago.edu/∼hashemifar/ModuleAlign.html

   canzar@ttic.edu or j3xu.ttic.edu

   Supplementary data are available at Bioinformatics online.

    

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