Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes

Macromolecular protein complexes carry out many of the essential functions of cells, and many genetic diseases arise from disrupting the functions of such complexes. Currently, there is great interest in defining the complete set of human protein complexes, but recent published maps lack comprehensive coverage. Here, through the synthesis of over 9,000 published mass spectrometry experiments, we present hu.MAP, the most comprehensive and accurate human protein complex map to date, containing > 4,600 total complexes, > 7,700 proteins, and > 56,000 unique interactions, including thousands of confident protein interactions not identified by the original publications. hu.MAP accurately recapitulates known complexes withheld from the learning procedure, which was optimized with the aid of a new quantitative metric (k‐cliques) for comparing sets of sets. The vast majority of complexes in our map are significantly enriched with literature annotations, and the map overall shows improved coverage of many disease‐associated proteins, as we describe in detail for ciliopathies. Using hu.MAP, we predicted and experimentally validated candidate ciliopathy disease genes in vivo in a model vertebrate, discovering CCDC138, WDR90, and KIAA1328 to be new cilia basal body/centriolar satellite proteins, and identifying ANKRD55 as a novel member of the intraflagellar transport machinery. By offering significant improvements to the accuracy and coverage of human protein complexes, hu.MAP ( http://proteincomplexes.org ) serves as a valuable resource for better understanding the core cellular functions of human proteins and helping to determine mechanistic foundations of human disease.     

Dynamic human liver proteome atlas reveals functional insights into disease pathways

Deeper understanding of liver pathophysiology would benefit from a comprehensive quantitative proteome resource at cell type resolution to predict outcome and design therapy. Here, we quantify more than 150,000 sequence‐unique peptides aggregated into 10,000 proteins across total liver, the major liver cell types, time course of primary cell cultures, and liver disease states. Bioinformatic analysis reveals that half of hepatocyte protein mass is comprised of enzymes and 23% of mitochondrial proteins, twice the proportion of other liver cell types. Using primary cell cultures, we capture dynamic proteome remodeling from tissue states to cell line states, providing useful information for biological or pharmaceutical research. Our extensive data serve as spectral library to characterize a human cohort of non‐alcoholic steatohepatitis and cirrhosis. Dramatic proteome changes in liver tissue include signatures of hepatic stellate cell activation resembling liver cirrhosis and providing functional insights. We built a web‐based dashboard application for the interactive exploration of our resource (www.liverproteome.org).     

Large‐scale survey and database of high affinity ligands for peptide recognition modules

Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners. Here, we used large‐scale peptide‐phage display methods to derive optimal ligands for 163 unique PRMs representing 79 distinct structural families. We combined the new data with previous data that we collected for the large SH3, PDZ, and WW domain families to assemble a database containing 7,984 unique peptide ligands for 500 PRMs representing 82 structural families. For 74 PRMs, we acquired enough new data to map the specificity profiles in detail and derived position weight matrices and binding specificity logos based on multiple peptide ligands. These analyses showed that optimal peptide ligands resembled peptides observed in existing structures of PRM‐ligand complexes, indicating that a large majority of the phage‐derived peptides are likely to target natural peptide‐binding sites and could thus act as inhibitors of natural protein–protein interactions. The complete dataset has been assembled in an online database (http://www.prm‐db.org) that will enable many structural, functional, and biological studies of PRMs and SLiMs.     

Membranome 3.0: Database of single‐pass membrane proteins with AlphaFold models

The Membranome database provides comprehensive structural information on single‐pass (i.e., bitopic) membrane proteins from six evolutionarily distant organisms, including protein–protein interactions, complexes, mutations, experimental structures, and models of transmembrane α‐helical dimers. We present a new version of this database, Membranome 3.0, which was significantly updated by revising the set of 5,758 bitopic proteins and incorporating models generated by AlphaFold 2 in the database. The AlphaFold models were parsed into structural domains located at the different membrane sides, modified to exclude low‐confidence unstructured terminal regions and signal sequences, validated through comparison with available experimental structures, and positioned with respect to membrane boundaries. Membranome 3.0 was re‐developed to facilitate visualization and comparative analysis of multiple 3D structures of proteins that belong to a specified family, complex, biological pathway, or membrane type. New tools for advanced search and analysis of proteins, their interactions, complexes, and mutations were included. The database is freely accessible at https://membranome.org.     

Syn Wiki: Functional annotation of the first artificial organism Mycoplasma mycoides JCVI‐syn3A

The new field of synthetic biology aims at the creation of artificially designed organisms. A major breakthrough in the field was the generation of the artificial synthetic organism Mycoplasma mycoides JCVI‐syn3A. This bacterium possesses only 452 protein‐coding genes, the smallest number for any organism that is viable independent of a host cell. However, about one third of the proteins have no known function indicating major gaps in our understanding of simple living cells. To facilitate the investigation of the components of this minimal bacterium, we have generated the database SynWiki (http://synwiki.uni-goettingen.de/). SynWiki is based on a relational database and gives access to published information about the genes and proteins of M. mycoides JCVI‐syn3A. To gain a better understanding of the functions of the genes and proteins of the artificial bacteria, protein–protein interactions that may provide clues for the protein functions are included in an interactive manner. SynWiki is an important tool for the synthetic biology community that will support the comprehensive understanding of a minimal cell as well as the functional annotation of so far uncharacterized proteins.     

Analysis of a photosynthetic cyanobacterium rich in internal membrane systems via gradient profiling by sequencing (Grad-seq)

Although regulatory small RNAs have been reported in photosynthetic cyanobacteria, the lack of clear RNA chaperones involved in their regulation poses a conundrum. Here, we analyzed the full complement of cellular RNAs and proteins using gradient profiling by sequencing (Grad-seq) in Synechocystis 6803. Complexes with overlapping subunits such as the CpcG1-type versus the CpcL-type phycobilisomes or the PsaK1 versus PsaK2 photosystem I pre(complexes) could be distinguished, supporting the high quality of this approach. Clustering of the in-gradient distribution profiles followed by several additional criteria yielded a short list of potential RNA chaperones that include an YlxR homolog and a cyanobacterial homolog of the KhpA/B complex. The data suggest previously undetected complexes between accessory proteins and CRISPR-Cas systems, such as a Csx1-Csm6 ribonucleolytic defense complex. Moreover, the exclusive association of either RpoZ or 6S RNA with the core RNA polymerase complex and the existence of a reservoir of inactive sigma–antisigma complexes is suggested. The Synechocystis Grad-seq resource is available online at https://sunshine.biologie.uni-freiburg.de/GradSeqExplorer/ providing a comprehensive resource for the functional assignment of RNA–protein complexes and multisubunit protein complexes in a photosynthetic organism.

We analyze a cyanobacterium using Grad-seq, providing a comprehensive resource for the in-depth analysis of the complexome in a photosynthetic organism.     

The Overlap of Small Molecule and Protein Binding Sites within Families of Protein Structures

Protein–protein interactions are challenging targets for modulation by small molecules. Here, we propose an approach that harnesses the increasing structural coverage of protein complexes to identify small molecules that may target protein interactions. Specifically, we identify ligand and protein binding sites that overlap upon alignment of homologous proteins. Of the 2,619 protein structure families observed to bind proteins, 1,028 also bind small molecules (250–1000 Da), and 197 exhibit a statistically significant (p<0.01) overlap between ligand and protein binding positions. These “bi-functional positions”, which bind both ligands and proteins, are particularly enriched in tyrosine and tryptophan residues, similar to “energetic hotspots” described previously, and are significantly less conserved than mono-functional and solvent exposed positions. Homology transfer identifies ligands whose binding sites overlap at least 20% of the protein interface for 35% of domain–domain and 45% of domain–peptide mediated interactions. The analysis recovered known small-molecule modulators of protein interactions as well as predicted new interaction targets based on the sequence similarity of ligand binding sites. We illustrate the predictive utility of the method by suggesting structural mechanisms for the effects of sanglifehrin A on HIV virion production, bepridil on the cellular entry of anthrax edema factor, and fusicoccin on vertebrate developmental pathways. The results, available at http://pibase.janelia.org, represent a comprehensive collection of structurally characterized modulators of protein interactions, and suggest that homologous structures are a useful resource for the rational design of interaction modulators.     

Up-to-date catalogues of yeast protein complexes

Gold standard datasets on protein complexes are key to inferring and validating protein–protein interactions. Despite much progress in characterizing protein complexes in the yeast Saccharomyces cerevisiae, numerous researchers still use as reference the manually curated complexes catalogued by the Munich Information Center of Protein Sequences database. Although this catalogue has served the community extremely well, it no longer reflects the current state of knowledge. Here, we report two catalogues of yeast protein complexes as results of systematic curation efforts. The first one, denoted as CYC2008, is a comprehensive catalogue of 408 manually curated heteromeric protein complexes reliably backed by small-scale experiments reported in the current literature. This catalogue represents an up-to-date reference set for biologists interested in discovering protein interactions and protein complexes. The second catalogue, denoted as YHTP2008, comprises 400 high-throughput complexes annotated with current literature evidence. Among them, 262 correspond, at least partially, to CYC2008 complexes. Evidence for interacting subunits is collected for 68 complexes that have only partial or no overlap with CYC2008 complexes, whereas no literature evidence was found for 100 complexes. Some of these partially supported and as yet unsupported complexes may be interesting candidates for experimental follow up. Both catalogues are freely available at: http://wodaklab.org/cyc2008/.     

SECISearch3 and Seblastian: new tools for prediction of SECIS elements and selenoproteins

Selenoproteins are proteins containing an uncommon amino acid selenocysteine (Sec). Sec is inserted by a specific translational machinery that recognizes a stem-loop structure, the SECIS element, at the 3′ UTR of selenoprotein genes and recodes a UGA codon within the coding sequence. As UGA is normally a translational stop signal, selenoproteins are generally misannotated and designated tools have to be developed for this class of proteins. Here, we present two new computational methods for selenoprotein identification and analysis, which we provide publicly through the web servers at http://gladyshevlab.org/SelenoproteinPredictionServer or http://seblastian.crg.es. SECISearch3 replaces its predecessor SECISearch as a tool for prediction of eukaryotic SECIS elements. Seblastian is a new method for selenoprotein gene detection that uses SECISearch3 and then predicts selenoprotein sequences encoded upstream of SECIS elements. Seblastian is able to both identify known selenoproteins and predict new selenoproteins. By applying these tools to diverse eukaryotic genomes, we provide a ranked list of newly predicted selenoproteins together with their annotated cysteine-containing homologues. An analysis of a representative candidate belonging to the AhpC family shows how the use of Sec in this protein evolved in bacterial and eukaryotic lineages.     

New Regulators of Clathrin-Mediated Endocytosis Identified in Saccharomyces cerevisiae by Systematic Quantitative Fluorescence Microscopy

Despite the importance of clathrin-mediated endocytosis (CME) for cell biology, it is unclear if all components of the machinery have been discovered and many regulatory aspects remain poorly understood. Here, using Saccharomyces cerevisiae and a fluorescence microscopy screening approach we identify previously unknown regulatory factors of the endocytic machinery. We further studied the top scoring protein identified in the screen, Ubx3, a member of the conserved ubiquitin regulatory X (UBX) protein family. In vivo and in vitro approaches demonstrate that Ubx3 is a new coat component. Ubx3-GFP has typical endocytic coat protein dynamics with a patch lifetime of 45 ± 3 sec. Ubx3 contains a W-box that mediates physical interaction with clathrin and Ubx3-GFP patch lifetime depends on clathrin. Deletion of the UBX3 gene caused defects in the uptake of Lucifer Yellow and the methionine transporter Mup1 demonstrating that Ubx3 is needed for efficient endocytosis. Further, the UBX domain is required both for localization and function of Ubx3 at endocytic sites. Mechanistically, Ubx3 regulates dynamics and patch lifetime of the early arriving protein Ede1 but not later arriving coat proteins or actin assembly. Conversely, Ede1 regulates the patch lifetime of Ubx3. Ubx3 likely regulates CME via the AAA-ATPase Cdc48, a ubiquitin-editing complex. Our results uncovered new components of the CME machinery that regulate this fundamental process.     

Large-Scale Event Extraction from Literature with Multi-Level Gene Normalization

Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons – Attribution – Share Alike (CC BY-SA) license.     

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups). Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB) with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be considered as pre-training for various applications of deep learning in bioinformatics. The related data is available at Life Language Processing Website: http://llp.berkeley.edu and Harvard Dataverse: http://dx.doi.org/10.7910/DVN/JMFHTN.     

CentiServer: A Comprehensive Resource,Web-Based Application and R Package for Centrality Analysis

Various disciplines are trying to solve one of the most noteworthy queries and broadly used concepts in biology, essentiality. Centrality is a primary index and a promising method for identifying essential nodes, particularly in biological networks. The newly created CentiServer is a comprehensive online resource that provides over 110 definitions of different centrality indices, their computational methods, and algorithms in the form of an encyclopedia. In addition, CentiServer allows users to calculate 55 centralities with the help of an interactive web-based application tool and provides a numerical result as a comma separated value (csv) file format or a mapped graphical format as a graph modeling language (GML) file. The standalone version of this application has been developed in the form of an R package. The web-based application (CentiServer) and R package (centiserve) are freely available at http://www.centiserver.org/     

Evidence for a Nonendosomal Function of the Saccharomyces cerevisiae ESCRT-III-Like Protein Chm7

Endosomal sorting complex required for transport (ESCRT) proteins are involved in a number of cellular processes, such as endosomal protein sorting, HIV budding, cytokinesis, plasma membrane repair, and resealing of the nuclear envelope during mitosis. Here we explored the function of a noncanonical member of the ESCRT-III protein family, the Saccharomyces cerevisiae ortholog of human CHMP7. Very little is known about this protein. In silico analysis predicted that Chm7 (yeast ORF YJL049w) is a fusion of an ESCRT-II and ESCRT-III-like domain, which would suggest a role in endosomal protein sorting. However, our data argue against a role of Chm7 in endosomal protein sorting. The turnover of the endocytic cargo protein Ste6 and the vacuolar protein sorting of carboxypeptidase S (CPS) were not affected by CHM7 deletion, and Chm7 also responded very differently to a loss in Vps4 function compared to a canonical ESCRT-III protein. Our data indicate that the Chm7 function could be connected to the endoplasmic reticulum (ER). In line with a function at the ER, we observed a strong negative genetic interaction between the deletion of a gene function (APQ12) implicated in nuclear pore complex assembly and messenger RNA (mRNA) export and the CHM7 deletion. The patterns of genetic interactions between the APQ12 deletion and deletions of ESCRT-III genes, two-hybrid interactions, and the specific localization of mCherry fusion proteins are consistent with the notion that Chm7 performs a novel function at the ER as part of an alternative ESCRT-III complex.     

PharmDB-K: Integrated Bio-Pharmacological Network Database for Traditional Korean Medicine

Despite the growing attention given to Traditional Medicine (TM) worldwide, there is no well-known, publicly available, integrated bio-pharmacological Traditional Korean Medicine (TKM) database for researchers in drug discovery. In this study, we have constructed PharmDB-K, which offers comprehensive information relating to TKM-associated drugs (compound), disease indication, and protein relationships. To explore the underlying molecular interaction of TKM, we integrated fourteen different databases, six Pharmacopoeias, and literature, and established a massive bio-pharmacological network for TKM and experimentally validated some cases predicted from the PharmDB-K analyses. Currently, PharmDB-K contains information about 262 TKMs, 7,815 drugs, 3,721 diseases, 32,373 proteins, and 1,887 side effects. One of the unique sets of information in PharmDB-K includes 400 indicator compounds used for standardization of herbal medicine. Furthermore, we are operating PharmDB-K via phExplorer (a network visualization software) and BioMart (a data federation framework) for convenient search and analysis of the TKM network. Database URL: http://pharmdb-k.org, http://biomart.i-pharm.org.     

EDock‐ML: A web server for using ensemble docking with machine learning to aid drug discovery

EDock‐ML is a web server that facilitates the use of ensemble docking with machine learning to help decide whether a compound is worthwhile to be considered further in a drug discovery process. Ensemble docking provides an economical way to account for receptor flexibility in molecular docking. Machine learning improves the use of the resulting docking scores to evaluate whether a compound is likely to be useful. EDock‐ML takes a bottom‐up approach in which machine‐learning models are developed one protein at a time to improve predictions for the proteins included in its database. Because the machine‐learning models are intended to be used without changing the docking and model parameters with which the models were trained, novice users can use it directly without worrying about what parameters to choose. A user simply submits a compound specified by an ID from the ZINC database (Sterling, T.; Irwin, J. J., J Chem Inf Model 2015, 55[11], 2,324–2,337.) or upload a file prepared by a chemical drawing program and receives an output helping the user decide the likelihood of the compound to be active or inactive for a drug target. EDock‐ML can be accessed freely at edock‐ml.umsl.edu     

PIPE-CLIP: a comprehensive online tool for CLIP-seq data analysis

CLIP-seq is widely used to study genome-wide interactions between RNA-binding proteins and RNAs. However, there are few tools available to analyze CLIP-seq data, thus creating a bottleneck to the implementation of this methodology. Here, we present PIPE-CLIP, a Galaxy framework-based comprehensive online pipeline for reliable analysis of data generated by three types of CLIP-seq protocol: HITS-CLIP, PAR-CLIP and iCLIP. PIPE-CLIP provides both data processing and statistical analysis to determine candidate cross-linking regions, which are comparable to those regions identified from the original studies or using existing computational tools. PIPE-CLIP is available at http://pipeclip.qbrc.org/.