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Heavy metals accumulating in the human body produce physiological toxicity by interfering with the transport of human proteins and enzymes. Heavy metals detection is significant for food safety ass...

Using RNA antisense purification and mass spectrometry, we identified more than 100 human proteins that directly and specifically bind SARS-CoV-2 RNA in infected cells. To gain insights into the functions of selected RNA interactors, we applied genetic perturbation and pharmacological inhibition experiments, and mapped the contact sites on the viral RNA. This led to the identification of host dependency factors and defense strategies, which can guide the design of novel therapeutics against SARS-CoV-2.

Minimal absent words (MAWs) are minimal-length oligomers absent from a genome or proteome. Although some artificially synthesized MAWs have deleterious effects, there is still a lack of a strategy for the classification of non-occurring sequences as potentially malicious or benign. In this work, by using Markovian models with multiple-testing correction, we reveal significant absent oligomers, which are statistically expected to exist. This suggests that their absence is due to negative selection. We survey genomes and proteomes covering the diversity of life and find thousands of significant absent sequences. Common significant MAWs are often mono- or dinucleotide tracts, or palindromic. Significant viral MAWs are often restriction sites and may indicate unknown restriction motifs. Surprisingly, significant mammal genome MAWs are often present, but rare, in other mammals, suggesting that they are suppressed but not completely forbidden. Significant human MAWs are frequently present in prokaryotes, suggesting immune function, but rarely present in human viruses, indicating viral mimicry of the host. More than one-fourth of human proteins are one substitution away from containing a significant MAW, with the majority of replacements being predicted harmful. We provide a web-based, interactive database of significant MAWs across genomes and proteomes.

Changes in the glycome of human proteins and cells are associated with the progression of multiple diseases such as Alzheimer's, diabetes mellitus, many types of cancer, and those caused by viruses. Consequently, several studies have shown essential modifications to the isomeric glycan moieties for diseases in different stages. However, the elucidation of extensive isomeric glycan profiles remains challenging because of the lack of analytical techniques with sufficient resolution power to separate all glycan and glycopeptide iso-forms. Therefore, the development of sensitive and accurate approaches for the characterization of all the isomeric forms of glycans and glycopeptides is essential to tracking the progression of pathology in glycoprotein-related diseases. This review describes the isomeric separation achievements reported in glycomics and glycoproteomics in the last decade. It focuses on the mass spectrometry-based analytical strategies, stationary phases, and derivatization techniques that have been developed to enhance the separation mechanisms in liquid chromatography systems and the detection capabilities of mass spectrometry systems.

Introns are excised from pre-messenger RNAs by the spliceosome, which produces mRNAs with continuous protein-coding information. In humans, most pre-mRNAs undergo alternative splicing to expand proteomic diversity. Cryo-electron microscopy (cryo-EM) structures of the yeast spliceosome elucidated how proteins stabilize and remodel an RNA-based active site to effect splicing catalysis. More recent cryo-EM snapshots of the human spliceosome reveal a complex protein scaffold and provide insights into the role of specific human proteins in modulating spliceosome activation, splice site positioning, and the ATPase-mediated dynamics of the active site. The emerging molecular picture highlights how, compared to its yeast counterpart, the human spliceosome has coopted additional protein factors to allow increased plasticity of splice site recognition and remodeling, and potentially to regulate alternative splicing.

This issue of the Biomedical Journal acquaints us with the compelling hypothesis that the vascular glycocalyx lies at the intersection of severe COVID-19 risk factors and damages, and the ways used by artificial intelligence to predict interactions between SARS-CoV-2 and human proteins. Furthermore, we explore the antiviral potential of valinomycin and the long list of COVID-19-related clinical trials, and learn how (not) to fix a broken femoral head. Last but not least, we get to enjoy the tale of the cellular oxygen-sensing system as well as the role of the host complement system during Leptospira infection, and learn that SARS-CoV-2 can sometimes come with a pathogenic plus one.

The DARPin® drug platform was established with a vision to expand the medical use of biologics beyond what was possible with monoclonal antibodies. It is based on naturally occurring ankyrin repeat domains that are typically building blocks of multifunctional human proteins. The platform allows for the generation of diverse, well-behaved, multifunctional drug candidates. Recent clinical data illustrate the favorable safety profile of the first DARPin® molecules tested in patients. With the positive phase III results of the most advanced DARPin® drug candidate, abicipar, the DARPin® drug platform is potentially about to achieve its first marketing approval. This review highlights some of the key milestones and decisions encountered when transforming the DARPin® platform from an academic concept to a biotech drug pipeline engine.

Aim: HPV is an important cause of cervical cancer, but Chlamydia trachomatis (CT) is suspiciously involved in this disease ranging from direct to its involvement as a cofactor with HPV. We performed this study to understand the interaction of HPV and C. trachomatis with humans and its contribution to cervical cancer. Materials & methods: Host–pathogen and pathogen–pathogen protein–protein interaction maps of HPV/CT/human were prepared and compared to analyze interactions during single/coinfection of C. trachomatis and HPV. The interacting human proteins were detected by their involvement in cervical cancer. Results: C. trachomatis may interact with several cancer associated proteins while HPV and C. trachomatis largely interact with different human proteins, suggesting different pathogenesis. Conclusion: C. trachomatis coinfection with HPV may modulate cervical cancer development.

The functional and optimal expression of genes is crucial for survival of all living organisms. Numerous experiments and efforts have been performed to reveal the mechanisms required for the functional and optimal expression of human genes. The yeast Saccharomyces cerevisiae has evolved independently of humans for billions of years. Nevertheless, S. cerevisiae has many conserved genes and expression mechanisms that are similar to those in humans. Yeast is the most commonly used model organism for studying the function and expression mechanisms of human genes because it has a relatively simple genome structure, which is easy to manipulate. Many previous studies have focused on understanding the functions and mechanisms of human proteins using orthologous genes and biological systems of yeast. In this review, we mainly introduce two recent studies that replaced human genes and nucleosomes with those of yeast. Here, we suggest that, although yeast is a relatively small eukaryotic cell, its humanization is useful for the direct study of human proteins. In addition, yeast can be used as a model organism in a broader range of studies, including drug screening.

Relevance. The genetic recombination between viruses and men is known long ago. It can be divided on relict and ontogenic ones. For the host the recombination may display different consequences the nature of which is not exposed explicitly.Aim is to analyze (on the base of computer comparison of the primary structure of viral and human proteins ) the occurrence of twodirectional recombination by small genome fragments between viruses and men and describe its possible after-effects.Materials and methods. For this computer study human and virus protein sequences were used from data bases available in INTERNET.Results. It was indicated that recombination (cryptical and explicit) by small genome fragments between viruses and men occurred many times in the past and many viruses pathogenic for men were involved in it.Conclusion. The bioinformatics approach allows to look at the past of viruses and men and find the traces of genetic information changes between them that may predetermine the effects of vaccines and diagnostic immune tests.

Recently, the life of human beings around the entire world has been endangering by the spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is indispensable. In 2019, a predictor called “pLoc_bal-mHum” was developed for identifying the subcellular localization of human proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mHum was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporate the “deep-learning” technique and develop a new predictor called “pLoc_Deep-mHum”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web-server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mHum/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

A cell contains numerous protein molecules. One of the fundamental goals in molecular cell biology is to determine their subcellular locations since this information is extremely important to both basic research and drug development. In this paper, we report a novel and very powerful predictor called "pLoc_bal-mHum" for predicting the subcellular localization of human proteins based on their sequence information alone. Cross-validation tests on exactly the same experiment-confirmed dataset have indicated that the new predictor is remarkably superior to the existing state-of-the-art predictor in identifying the subcellular localization of human proteins. To maximize the convenience for the majority of experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mHum/, by which users can easily get their desired results without the need to go through the detailed mathematics.

Using a IIIA radiation skin burn model in CD1 mice, we demonstrated that transdermal application of liposomes containing rhAFP; liposomes containing rhGCSF; and liposomes containing rhAFP and rhGCSF on the affected area of the skin within two weeks after irradiation led to a significant reduction in the healing time of the burn wound according to clinical indices. The most effective was the use of liposomes containing rhAFP: the duration of the latent period increased 70%, the duration of the moist epidermitis was reduced by 60%, the duration of the reparative phase was reduced by 32%, and the total healing time of the burn was reduced by 44%, compared to the values for mice with untreated radiation burns. Immunohistochemical studies have shown that application of the studied drugs led to an increase in the survival of stem cells in the hair follicles of the skin: the most pronounced effect on histological indices was noted in the group treated with liposomes containing rhAFP and rhGCSF.

The Human Proteome Organization’s (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems, and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well, and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted ∼20 000 human proteins encoded by the human genome., This work was funded in part by the National Institutes of Health grants R01GM087221 (EWD/RLM), R24GM127667 (EWD), U54EB020406 (EWD), R01HL133135 (RLM), U19AG02312 (RLM), U54ES017885 (GSO), U24CA210967-01 (GSO), R01LM013115 (NB) and P41GM103484 (NB); National Science Foundation grants ABI-1759980 (NB), DBI-1933311 (EWD), and IOS-1922871 (EWD); Canadian Institutes of Health Research 148408 (CMO); Korean Ministry of Health and Welfare HI13C2098 (YKP); French Ministry of Higher Education, Research and Innovation, ProFI project, ANR-10-INBS-08 (YV); also in part by the National Eye Institute (NEI), National Human Genome Research Institute (NHGRI), National Heart, Lung, and Blood Institute (NHLBI), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of General Medical Sciences (NIGMS), and National Institute of Mental Health (NIMH) of the National Institutes of Health under Award Number U24HG007822 (SO) (the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health).

Summary QuartataWeb is a user-friendly server developed for polypharmacological and chemogenomics analyses. Users can easily obtain information on experimentally verified (known) and computationally predicted (new) interactions between 5494 drugs and 2807 human proteins in DrugBank, and between 315 514 chemicals and 9457 human proteins in the STITCH database. In addition, QuartataWeb links targets to KEGG pathways and GO annotations, completing the bridge from drugs/chemicals to function via protein targets and cellular pathways. It allows users to query a series of chemicals, drug combinations or multiple targets, to enable multi-drug, multi-target, multi-pathway analyses, toward facilitating the design of polypharmacological treatments for complex diseases. Availability and implementation QuartataWeb is freely accessible at http://quartata.csb.pitt.edu. Supplementary information Supplementary data are available at Bioinformatics online.

Proteins with homo-repeats of more than 4 amino acid residues in length were examined to understand whether some splicing sites in pre-mRNA may be attributed to homo-repeats in human proteins. The human proteome was found to contain a total of 404 proteins with homo-repeats that account for at least one splicing site in pre-mRNA. Pre-mRNA splicing sites were more often found in the C-terminal part (67%) than in the middle or N-terminal part of a homo-repeat. Ten homo-repeats were identified to have two splicing sites per repeat. The repeats were lysine homo-repeats in all but one case.

Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug’s therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment’s efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for., Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins; how drugs restore these functions, however, is often unknown. Here, the authors develop the multiscale interactome, a powerful approach to explain disease treatment.

About 10% of human proteins have no annotated function in protein knowledge bases. A workflow to generate hypotheses for the function of these uncharacterized proteins has been developed, based on predicted and experimental information on protein properties, interactions, tissular expression, subcellular localization, conservation in other organisms, as well as phenotypic data in mutant model organisms. This workflow has been applied to seven uncharacterized human proteins (C6orf118, C7orf25, CXorf58, RSRP1, SMLR1, TMEM53 and TMEM232) in the frame of a course-based undergraduate research experience named Functionathon organized at the University of Geneva to teach undergraduate students how to use biological databases and bioinformatics tools and interpret the results. C6orf118, CXorf58 and TMEM232 were proposed to be involved in cilia-related functions; TMEM53 and SMLR1 were proposed to be involved in lipid metabolism and C7orf25 and RSRP1 were proposed to be involved in RNA metabolism and gene expression. Experimental strategies to test these hypotheses were also discussed. The results of this manual data mining study may contribute to the project recently launched by the Human Proteome Organization (HUPO) Human Proteome Project aiming to fill gaps in the functional annotation of human proteins. Database URL: http://www.nextprot.org

According to the amino acid composition of natural proteins, it could be expected that all possible sequences of three or four amino acids will occur at least once in large protein datasets purely by chance. However, in some species or cellular context, specific short amino acid motifs are missing due to unknown reasons. We describe these as Avoided Motifs, short amino acid combinations missing from biological sequences. Here we identify 209 human and 154 bacterial Avoided Motifs of length four amino acids, and discuss their possible functionality according to their presence in other species. Furthermore, we determine two Avoided Motifs of length three amino acids in human proteins specifically located in the cytoplasm, and two more in secreted proteins. Our results support the hypothesis that the characterization of Avoided Motifs in particular contexts can provide us with information about functional motifs, pointing to a new approach in the use of molecular sequences for the discovery of protein function.

Structural genomics groups have identified the need to generate multiple truncated versions of each target to improve their success in producing a well-expressed, soluble, and stable protein and one that crystallizes and diffracts to a sufficient resolution for structural determination. At the Structural Genomics Consortium, we opted for the ligation-independent cloning (LIC) method which provides the throughput we desire to produce and screen many proteins in a parallel process. Here, we describe our LIC protocol for generating constructs in 96-well format and provide a choice of vectors suitable for expressing proteins in both E. coli and the baculovirus expression vector system (BEVS).

In Chapter 3 , we described the Structural Genomics Consortium (SGC) process for generating multiple constructs of truncated versions of each protein using LIC. In this chapter we provide a step-by-step procedure of our E. coli system for test expressing intracellular (soluble) proteins in a 96-well format that enables us to identify which proteins or truncated versions are expressed in a soluble and stable form suitable for structural studies. In addition, we detail the process for scaling up cultures for large-scale protein purification. This level of production is required to obtain sufficient quantities (i.e., milligram amounts) of protein for further characterization and/or structural studies (e.g., crystallization or cryo-EM experiments). Our standard process is purification by immobilized metal affinity chromatography (IMAC) using nickel resin followed by size exclusion chromatography (SEC), with additional procedures arising from the complexity of the protein itself.

Metaproteomics is as a promising technique for studying the human gut microbiota, because it can reveal the taxonomic profile and also shed light on the functional role of the microbial community. Nevertheless, methods for extracting proteins from stool samples continue to evolve, in the pursuit of optimal protocols for moistening and dispersing the stool sample and for disrupting microbial cells which are two critical steps for ensuring good protein recovery. Here, we evaluated different stool sample processing and microbial cell disruption methods for metaproteomic analyses of human gut microbiota. An unsupervised principal component analysis showed that different methods produced similar human gut microbial taxonomic profiles. An unsupervised two-way hierarchical clustering analysis identified the microbial taxonomic signatures associated with each method. Proteobacteria and Bacteroidetes identification was favored by moistening the stool samples during processing and by disrupting cells with medium-sized glass beads. Ascomycota identification was enhanced by using large-sized glass beads during sample processing for stool dispersion. Euryarchaeota identification was improved with a combination of small and medium-sized glass beads for cell disruption. Assessments of the relative abundance of Firmicutes, Actinobacteria and Spirochaetes improved when ultrasonication was performed before cell disruption with glass beads. The latter method also increased the overall number of identified proteins. Taxonomic and protein functional analyses of metaproteomic data derived from stool samples from six healthy individuals showed common taxonomic profiles. We also detected certain proteins involved in microbial functions relevant to the host and related mostly to particular taxa, such as B12 biosynthesis and short chain fatty acid production carried out mainly by members in the Prevotella genus and the Firmicutes phylum, respectively. Finally, in this metaproteomic study we identified several human proteins, mostly related to the anti-microbial response, which could contribute to determining the beneficial and detrimental relationships between gut microbiota and human cells in particular human diseases.

Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome–DME interaction (MICBIO), xenobiotics–DME interaction (XEOTIC) and host protein–DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3359 MICBIOs between 225 microbial species and 185 DMEs; 47 778 XEOTICs between 4150 xenobiotics and 501 DMEs; 7849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/, Graphical Abstract Graphical AbstractThe Interactome Database of Drug-Metabolizing Enzyme (INTEDE) provides the comprehensive interactome data from three perspectives for both human and microbial drug-metabolizing enzymes.

The nuclear export receptor CRM1 (XPO1) recognizes and binds specific sequence motifs termed nuclear export signals (NESs) in cargo proteins. About 200 NES motifs have been identified, but over a thousand human proteins are potential CRM1 cargos, and most of their NESs remain to be identified. On the other hand, the interaction of NES peptides with the &ldquo, NES-binding groove&rdquo, of CRM1 was studied in detail using structural and biochemical analyses, but a better understanding of CRM1 function requires further investigation of how the results from these in vitro studies translate into actual NES export in a cellular context. Here we show that a simple cellular assay, based on a recently described reporter (SRVB/A), can be applied to identify novel potential NESs motifs, and to obtain relevant information on different aspects of CRM1-mediated NES export. Using cellular assays, we first map 19 new sequence motifs with nuclear export activity in 14 cancer-related proteins that are potential CRM1 cargos. Next, we investigate the effect of mutations in individual NES-binding groove residues, providing further insight into CRM1-mediated NES export. Finally, we extend the search for CRM1-dependent NESs to a recently uncovered, but potentially vast, set of small proteins called micropeptides. By doing so, we report the first NES-harboring human micropeptides.

The seven 14-3-3 isoforms are highly abundant human proteins encoded by similar yet distinct genes. 14-3-3 proteins recognize phosphorylated motifs within numerous human and viral proteins. Here, we analyze by X-ray crystallography, fluorescence polarization, mutagenesis and fusicoccin-mediated modulation the structural basis and druggability of 14-3-3 binding to four E6 oncoproteins of tumorigenic human papillomaviruses. 14-3-3 isoforms bind variant and mutated phospho-motifs of E6 and unrelated protein RSK1 with different affinities, albeit following an ordered affinity ranking with conserved relative KD ratios. Remarkably, 14-3-3 isoforms obey the same hierarchy when binding to most of their established targets, as supported by literature and a recent human complexome map. This knowledge allows predicting proportions of 14-3-3 isoforms engaged with phosphoproteins in various tissues. Notwithstanding their individual functions, cellular concentrations of 14-3-3 may be collectively adjusted to buffer the strongest phosphorylation outbursts, explaining their expression variations in different tissues and tumors., 14-3-3 proteins recognize phosphorylated motifs within numerous protein partners. Here, the authors characterize the binding of all human 14-3-3 isoforms to four E6 oncoproteins, and identify a fixed order of 14-3-3 binding affinities that is conserved in 14-3-3:phosphoprotein interactions across the proteome.

International audience; The causative agent of Huntington's disease, the poly-Q homo-repeat in the N-terminal region of huntingtin (httex1), is flanked by a 17-residue-long fragment (N17) and a proline-rich region (PRR), which promote and inhibit the aggregation propensity of the protein, respectively, by poorly understood mechanisms. Based on experimental data obtained from site-specifically labeled NMR samples, we derived an ensemble model of httex1 that identified both flanking regions as opposing poly-Q secondary structure promoters. While N17 triggers helicity through a promiscuous hydrogen bond network involving the side chains of the first glutamines in the poly-Q tract, the PRR promotes extended conformations in neighboring glutamines. Furthermore, a bioinformatics analysis of the human proteome showed that these structural traits are present in many human glutamine-rich proteins and that they are more prevalent in proteins with longer poly-Q tracts. Taken together, these observations provide the structural bases to understand previous biophysical and functional data on httex1.

Understanding the disease pathogenesis of the novel coronavirus, denoted SARS-CoV-2, is critical to the development of anti-SARS-CoV-2 therapeutics. The global propagation of the viral disease, denoted COVID-19 (“coronavirus disease 2019”), has unified the scientific community in searching for possible inhibitory small molecules or polypeptides. Given the known interaction between the human ACE2 (“Angiotensin-converting enzyme 2”) protein and the SARS-CoV virus (responsible for the coronavirus outbreak circa. 2003), considerable focus has been directed towards the putative interaction between the SARS-CoV-2 Spike protein and ACE2. However, a more holistic understanding of the SARS-CoV-2 vs. human inter-species interactome promises additional putative protein-protein interactions (PPI) that may be considered targets for the development of inhibitory therapeutics.To that end, we leverage two state-of-the-art, sequence-based PPI predictors (PIPE4 & SPRINT) capable of generating the comprehensive SARS-CoV-2 vs. human interactome, comprising approximately 285,000 pairwise predictions. Of these, we identify the high-scoring subset of human proteins predicted to interact with each of the 14 SARS-CoV-2 proteins by both methods, comprising 279 high-confidence putative interactions involving 225 human proteins. Notably, the Spike-ACE2 interaction was the highest ranked for both the PIPE4 and SPRINT predictors, corroborating existing evidence for this PPI. Furthermore, the PIPE-Sites algorithm was used to predict the putative subsequence that might mediate each interaction and thereby inform the design of inhibitory polypeptides intended to disrupt the corresponding host-pathogen interactions.We hereby publicly release the comprehensive set of PPI predictions and their corresponding PIPE-Sites landscapes in the following DataVerse repository: 10.5683/SP2/JZ77XA. All data and metadata are released under a CC-BY 4.0 licence. The information provided represents theoretical modeling only and caution should be exercised in its use. It is intended as a resource for the scientific community at large in furthering our understanding of SARS-CoV-2.

Author(s): Gordon, David E; Jang, Gwendolyn M; Bouhaddou, Mehdi; Xu, Jiewei; Obernier, Kirsten; O'Meara, Matthew J; Guo, Jeffrey Z; Swaney, Danielle L; Tummino, Tia A; Huttenhain, Ruth; Kaake, Robyn M; Richards, Alicia L; Tutuncuoglu, Beril; Foussard, Helene; Batra, Jyoti; Haas, Kelsey; Modak, Maya; Kim, Minkyu; Haas, Paige; Polacco, Benjamin J; Braberg, Hannes; Fabius, Jacqueline M; Eckhardt, Manon; Soucheray, Margaret; Bennett, Melanie J; Cakir, Merve; McGregor, Michael J; Li, Qiongyu; Naing, Zun Zar Chi; Zhou, Yuan; Peng, Shiming; Kirby, Ilsa T; Melnyk, James E; Chorba, John S; Lou, Kevin; Dai, Shizhong A; Shen, Wenqi; Shi, Ying; Zhang, Ziyang; Barrio-Hernandez, Inigo; Memon, Danish; Hernandez-Armenta, Claudia; Mathy, Christopher JP; Perica, Tina; Pilla, Kala B; Ganesan, Sai J; Saltzberg, Daniel J; Ramachandran, Rakesh; Liu, Xi; Rosenthal, Sara B; Calviello, Lorenzo; Venkataramanan, Srivats; Lin, Yizhu; Wankowicz, Stephanie A; Bohn, Markus; Trenker, Raphael; Young, Janet M; Cavero, Devin; Hiatt, Joe; Roth, Theo; Rathore, Ujjwal; Subramanian, Advait; Noack, Julia; Hubert, Mathieu; Roesch, Ferdinand; Vallet, Thomas; Meyer, Bjorn; White, Kris M; Miorin, Lisa; Agard, David; Emerman, Michael; Ruggero, Davide; Garcia-Sastre, Adolfo; Jura, Natalia; von Zastrow, Mark; Taunton, Jack; Schwartz, Olivier; Vignuzzi, Marco; d'Enfert, Christophe; Mukherjee, Shaeri; Jacobson, Matt; Malik, Harmit S; Fujimori, Danica G; Ideker, Trey; Craik, Charles S | Abstract: An outbreak of the novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 290,000 people since the end of 2019, killed over 12,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven efficacy nor are there vaccines for its prevention. Unfortunately, the scientific community has little knowledge of the molecular details of SARS-CoV-2 infection. To illuminate this, we cloned, tagged and expressed 26 of the 29 viral proteins in human cells and identified the human proteins physically associated with each using affinity- purification mass spectrometry (AP-MS), which identified 332 high confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 existing FDA-approved drugs, drugs in clinical trials and/or preclinical compounds, that we are currently evaluating for efficacy in live SARS-CoV-2 infection assays. The identification of host dependency factors mediating virus infection may provide key insights into effective molecular targets for developing broadly acting antiviral therapeutics against SARS-CoV-2 and other deadly coronavirus strains.

The nematode Caenorhabditis elegans (C. elegans) is a convenient tool to evaluate iron metabolism as it shares great orthology with human proteins involved in iron transport, in addition to being transparent and readily available. In this work, we describe how wild-type (N2) C. elegans nematodes in the first larval stage can be loaded with acetomethoxycalcein (CAL-AM) and study it as a whole-organism model for both iron speciation and chelator permeability of the labile iron pool (LIP). This model may be relevant for high throughput assessment of molecules intended for chelation therapy of iron overload diseases.

The localization of proteins at a tissue- or cell-type-specific level is tightly linked to the protein function. To better understand each protein's role in cellular systems, spatial information constitutes an important complement to quantitative data. The standard methods for determining the spatial distribution of proteins in single cells of complex tissue samples make use of antibodies. For a stringent analysis of the human proteome, we used orthogonal methods and independent antibodies to validate 5981 antibodies that show the expression of 3775 human proteins across all major human tissues. This enhanced validation uncovered 56 proteins corresponding to the group of "missing proteins" and 171 proteins of unknown function. The presented strategy will facilitate further discussions around criteria for evidence of protein existence based on immunohistochemistry and serves as a useful guide to identify candidate proteins for integrative studies with quantitative proteomics methods.

Allosteric regulation is one of the most direct and efficient ways to fine-tune protein function; it is induced by the binding of a ligand at an allosteric site that is topographically distinct from an orthosteric site. The Allosteric Database (ASD, available online at http://mdl.shsmu.edu.cn/ASD) was developed ten years ago to provide comprehensive information related to allosteric regulation. In recent years, allosteric regulation has received great attention in biological research, bioengineering, and drug discovery, leading to the emergence of entire allosteric landscapes as allosteromes. To facilitate research from the perspective of the allosterome, in ASD 2019, novel features were curated as follows: (i) >10 000 potential allosteric sites of human proteins were deposited for allosteric drug discovery; (ii) 7 human allosterome maps, including protease and ion channel maps, were built to reveal allosteric evolution within families; (iii) 1312 somatic missense mutations at allosteric sites were collected from patient samples from 33 cancer types and (iv) 1493 pharmacophores extracted from allosteric sites were provided for modulator screening. Over the past ten years, the ASD has become a central resource for studying allosteric regulation and will play more important roles in both target identification and allosteric drug discovery in the future.

Proteins are major functional molecules that physically and functionally interact to carry out cellular processes. The physical interactions are generally mediated by domain-level interactions. Thus, novel protein-protein interactions can be predicted using various computational methods based on domain-domain interactions, using resolved structures of protein complexes. Functional protein interactions can be inferred based on shared domains between proteins, since proteins involved in the same biological processes tend to harbor common domains. We recently developed a method of inferring functional interactions between proteins using associations between their domain compositions, which can be represented as domain profiles. Since the method requires only protein domain annotations, it can be easily applied to any species with a sequenced genome. Here, we describe in detail the method of generating domain profiles for proteins and measuring the association between them to infer functional interactions between proteins. We also demonstrate that domain profile association can be used to successfully construct a large-scale functional network of human proteins.

Polyphosphorylation is a newly described non-enzymatic post-translational modification wherein long chains of inorganic phosphates are attached to lysine residues. The first targets of polyphosphorylation identified were S. cerevisiae proteins Nsr1 and Top1. Building on this theme, we recently exploited functional genomics tools in yeast to identify 15 new targets, including a conserved network of nucleolar proteins implicated in ribosome biogenesis. We also described the polyphosphorylation of six human proteins, suggesting that this unique post-translational modification could be conserved throughout eukaryotes. The study of polyphosphorylation seems poised to uncover novel modes of protein regulation in pathways spanning diverse biological processes. In this review, we establish a framework for future work by outlining critical questions related to the biochemistry of polyphosphorylation, its therapeutic potential, and everything in between.

Smokeless tobacco (SLT) products are consumed by millions of people in over 130 countries around the world. Consumption of SLT has been estimated to cause a number of diseases accounting to more than 0.65 million deaths per year. There is sufficient epidemiological evidence on the association of SLT products with nicotine addiction, cancers of oral cavity and digestive systems but there is a lack of understanding of the role of toxic chemicals in these diseases. We provide the first comprehensive in-silico analysis of chemical compounds present in different SLT products used worldwide. Many of these compounds are found to have good absorption, solubility and permeability along with mutagenic and toxic properties. They are also found to target more than 350 human proteins involved in a plethora of human biological processes and pathways. Along with all the previously known diseases, the present study has identified the association of compounds of SLT products with a number of unknown diseases like neurodegenerative, immune and cardiac diseases (Left ventricular non compaction, dilated cardiomyopathy etc). These findings indicate far-reaching impact of SLT products on human health than already known which needs further validations using epidemiological, in-vitro and in-vivo methodologies. Thus, this study will provide one stop information for the policy makers in development of regulatory policies on toxic contents of SLT products.

O-GlcNAc is an intracellular posttranslational modification that governs myriad cell biological processes and is dysregulated in human diseases. Despite this broad pathophysiological significance, the biochemical effects of most O-GlcNAcylation events remain uncharacterized. One prevalent hypothesis is that O-GlcNAc moieties may be recognized by "reader" proteins to effect downstream signaling. However, no general O-GlcNAc readers have been identified, leaving a considerable gap in the field. To elucidate O-GlcNAc signaling mechanisms, we devised a biochemical screen for candidate O-GlcNAc reader proteins. We identified several human proteins, including 14-3-3 isoforms, that bind O-GlcNAc directly and selectively. We demonstrate that 14-3-3 proteins bind O-GlcNAc moieties in human cells, and we present the structures of 14-3-3β/α and γ bound to glycopeptides, providing biophysical insights into O-GlcNAc-mediated protein-protein interactions. Because 14-3-3 proteins also bind to phospho-serine and phospho-threonine, they may integrate information from O-GlcNAc and O-phosphate signaling pathways to regulate numerous physiological functions.

Guanine-rich DNA/RNA fragments can fold into G-quadruplexes (G4s) – non-canonical four-strand secondary structures. The article contains data on quadruplex interaction with human proteins. Binding of three topologically different G4 structures to more than 9000 human proteins was analyzed. Physicochemical methods were used to verify the results.The dataset was generated to identify the protein targets for DNA quadruplex structures for the purpose of better understanding the role of the structures in gene expression regulation. Presented data include functional interpretation of obtained gene lists, visualized with Cytoscape.

Pathogens infect host organisms by exploiting host cellular mechanisms and evading host defence mechanisms through molecular pathogen–host interactions (PHIs). Discovering new interactions between pathogen and human proteins is very crucial in understanding the infection mechanisms. By analysing interaction networks, the interactions responsible for infectious diseases can be detected and new drugs disabling these interactions can be delivered. In this paper, we propose a method based on Bayesian matrix factorization for predicting PHIs along with a projection-based technique and combine the results by employing an ensemble method. Furthermore, two features, target similarity and attacker similarity, are utilized for the first time in the literature for PHI prediction. The advantages of the proposed methods are two folds. Firstly, they relieve the need for negative samples which is significant since there is no available dataset providing negative samples for most of the pathogenic systems. Secondly, the experiments demonstrate that the proposed approach outperforms state-of-the-art methods; roughly 20% of top 50 predictions are among recently validated interactions. So, the search space for wet-lab experiments to obtain validated interactions can be considerably narrowed down from a huge number of possible interactions.

Dental calculus represents the first fossilized record of bacterial communities as a testimony of evolutionary biology. The development of dental calculus is a dynamic process that starts with a nonmineralized biofilm which eventually calcifies. Nonmineralized dental biofilm entraps particles from the oral cavity, including large amounts of oral bacteria, human proteins, viruses and food remnants, and preserves their DNA. The process of mineralization involves metabolic activities of the bacterial colonies and strengthens the attachment of nonmineralized biofilms to the tooth surface. From a clinical point of view, dental calculus always harbors a living, nonmineralized biofilm, jeopardizing the integrity of the dento-gingival or implanto-mucosal unit. This narrative review presents a brief historical overview of dental calculus formation and its clinical relevance in modern periodontal practice.

Intrinsically disordered proteins are proteins with intrinsically disordered regions (IDRs) that do not adopt a globular structure in their free state. IDRs have unique regions having protein-binding segments of which play pivotal roles in many biological processes. The binding sites in IDRs are heterogeneous in terms of their sequence conservation: some are conserved and others are not. We have been running a database of intrinsically disordered proteins, IDEAL, and collecting such binding segments, which are called protean segments (ProSs), within it. In this study, we compared the sequence conservation of ProSs, structural domains (SDs), and IDRs other than ProSs (non-ProSs) and found that i) functionally constrained residues in ProSs tend to be conserved, ii) the distribution of conservation scores of ProSs is similar to those of SDs but not non-ProSs, and iii) ProSs found in human proteins are mostly conserved only in vertebrates. These results indicate that the conservation patterns in ProSs principally follow the general rules found in SDs. However, we need to consider evolutionary distance when comparing IDR sequences because ProSs can readily emerge and disappear over the course of protein evolution. Moreover, many ProSs may remain to be identified, which may account for the heterogeneity of the sequence conservation of IDRs.

Motivation For in-depth understanding the functions of proteins in a cell, the knowledge of their subcellular localization is indispensable. The current study is focused on human protein subcellular location prediction based on the sequence information alone. Although considerable efforts have been made in this regard, the problem is far from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions that are particularly important for both basic research and drug design. Results Using the multi-label theory, we present a new predictor called ‘pLoc-mHum’ by extracting the crucial GO (Gene Ontology) information into the general PseAAC (Pseudo Amino Acid Composition). Rigorous cross-validations on a same stringent benchmark dataset have indicated that the proposed pLoc-mHum predictor is remarkably superior to iLoc-Hum, the state-of-the-art method in predicting the human protein subcellular localization. Availability and implementation To maximize the convenience of most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc-mHum/, by which users can easily get their desired results without the need to go through the complicated mathematics involved. Supplementary information Supplementary data are available at Bioinformatics online.

Identification of potential virus–host interactions is useful and vital to control the highly infectious virus-caused diseases. This may contribute toward development of new drugs to treat the viral infections. Recently, database records of clinically and experimentally validated interactions between a small set of human proteins and Ebola virus (EBOV) have been published. Using the information of the known human interaction partners of EBOV, our main objective is to identify a set of proteins that may interact with EBOV proteins. Here, we first review the state-of-the-art, computational methods used for prediction of novel virus–host interactions for infectious diseases followed by a case study on EBOV–human interactions. The assessment result shows that the predicted human host proteins are highly similar with known human interaction partners of EBOV in the context of structure and semantics and are responsible for similar biochemical activities, pathways and host–pathogen relationships.

S100A6 is a Ca2+-signal transducer that interacts with numerous proteins and regulates their biochemical functions. Here we identified a centrosomal protein, FOR20 (FOP-related protein of 20 kDa) as a novel S100A6 target by screening protein microarrays carrying 19,676 recombinant GST-fused human proteins. Binding experiments revealed that S100A6 interacts with the N-terminal region (residues 1–30) of FOR20 in a Ca2+-dependent manner in vitro and in living cells. Several S100 proteins including S100A1, A2, A4, A11, B also exhibited Ca2+-dependent interactions with FOR20 as well as S100A6. We found that two distantly related centrosomal proteins, FOP and OFD1, also possess N-terminal regions with a significant sequence similarity to the putative S100A6-binding site (residues 1–30) in FOR20 and are capable of binding to S100A6 in a Ca2+-dependent manner. Taken together, these results may indicate that S100A6 interacts with FOR20 and related centrosomal proteins through a conserved N-terminal domain, suggesting a novel Ca2+-dependent regulation of centrosomal function.

An increasing number of human proteins are being found to bear a prion-like domain (PrLD) driving the formation of membraneless compartments through liquid-liquid phase separation. Point mutations in these PrLDs promote the transition to an amyloid-like state. There has been much debate on whether this aberrant aggregation is caused by compositional or sequential changes. A recent extensive mutational study of the ALS-associated prion-like hnRNPA2 protein provides a framework to discriminate the molecular determinants behind pathogenic PrLDs aggregation. The effect of mutations on the aggregation propensity of hnRNPA2 is best predicted by combining their impact on PrLD amino acid composition and sequence-based amyloid propensity. This opens an avenue for the prediction of disease causing mutations in other human prion-like proteins. This article is protected by copyright. All rights reserved.

The analysis of intact proteins (top-down strategy) by mass spectrometry has great potential to elucidate proteoform variation, including patterns of post-translational modifications (PTMs), which may not be discernable by analysis of peptides alone (bottom-up approach). To maximize sequence coverage and localization of PTMs, various fragmentation modes have been developed to produce fragment ions from deep within intact proteins. Ultraviolet photodissociation (UVPD) has recently been shown to produce high sequence coverage and PTM retention on a variety of proteins, with increasing evidence of efficacy on a chromatographic time scale. However, utilization of UVPD for high-throughput top-down analysis to date has been limited by bioinformatics. Here, we detected 153 proteins and 489 proteoforms using UVPD and 271 proteins and 982 proteoforms using higher-energy collisional dissociation (HCD) in a comparative analysis of HeLa whole-cell lysate by qualitative top-down proteomics. Of the total detected proteoforms, 286 overlapped between the UVPD and HCD datasets, with 68% of proteoforms having C-scores greater than 40 for UVPD and 63% for HCD. The average sequence coverage (28% ± 20% for UVPD versus 17% ± 8% for HCD, p < 0.0001) found to be higher for UVPD than HCD, and with a trend toward improvement in q-value for the UVPD dataset. This study demonstrates the complementarity of UVPD and HCD for more extensive protein profiling and proteoform characterization.

Background Admixture between early modern humans and Neandertals approximately 50,000–60,000 years ago has resulted in 1.5–4% Neandertal ancestry in the genomes of present-day non-Africans. Evidence is accumulating that some of these archaic alleles are advantageous for modern humans, while others are deleterious; however, the major mechanism by which these archaic alleles act has not been fully explored. Results Here we assess the contributions of introgressed non-synonymous and regulatory variants to modern human protein and gene expression variation. We show that gene expression changes are more often associated with Neandertal ancestry than expected, and that the introgressed non-synonymous variants tend to have less predicted functional effect on modern human proteins than mutations that arose on the human lineage. Conversely, introgressed alleles contribute proportionally more to expression variation than non-introgressed alleles. Conclusions Our results suggest that the major influence of Neandertal introgressed alleles is through their effects on gene regulation. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1181-7) contains supplementary material, which is available to authorized users.

The neXtProt human protein knowledgebase (https://www.nextprot.org) continues to add new content and tools, with a focus on proteomics and genetic variation data. neXtProt now has proteomics data for over 85% of the human proteins, as well as new tools tailored to the proteomics community. Moreover, the neXtProt release 2016-08-25 includes over 8000 phenotypic observations for over 4000 variations in a number of genes involved in hereditary cancers and channelopathies. These changes are presented in the current neXtProt update. All of the neXtProt data are available via our user interface and FTP site. We also provide an API access and a SPARQL endpoint for more technical applications.

Knowledge of the molecular interactions of human proteins within tissues is important for identifying their tissue-specific roles and for shedding light on tissue phenotypes. However, many protein-protein interactions (PPIs) have no tissue-contexts. The TissueNet database bridges this gap by associating experimentally-identified PPIs with human tissues that were shown to express both pair-mates. Users can select a protein and a tissue, and obtain a network view of the query protein and its tissue-associated PPIs. TissueNet v.2 is an updated version of the TissueNet database previously featured in NAR. It includes over 40 human tissues profiled via RNA-sequencing or protein-based assays. Users can select their preferred expression data source and interactively set the expression threshold for determining tissue-association. The output of TissueNet v.2 emphasizes qualitative and quantitative features of query proteins and their PPIs. The tissue-specificity view highlights tissue-specific and globally-expressed proteins, and the quantitative view highlights proteins that were differentially expressed in the selected tissue relative to all other tissues. Together, these views allow users to quickly assess the unique versus global functionality of query proteins. Thus, TissueNet v.2 offers an extensive, quantitative and user-friendly interface to study the roles of human proteins across tissues. TissueNet v.2 is available at http://netbio.bgu.ac.il/tissuenet.

NeXtProt is a web-based protein knowledge platform that supports research on human proteins. NeXtProt (release 2015-04-28) lists 20,060 proteins, among them, 3373 canonical proteins (16.8%) lack credible experimental evidence at protein level (PE2:PE5). Therefore, they are considered as "missing proteins". A comprehensive bioinformatic workflow has been proposed to analyze these "missing" proteins. The aims of current study were to analyze physicochemical properties, existence and distribution of the tryptic cleavage sites, and to pinpoint the signature peptides of the missing proteins. Our findings showed that 23.7% of missing proteins were hydrophobic proteins possessing transmembrane domains (TMD). Also, forty missing entries generate tryptic peptides were either out of mass detection range (30aa) or mapped to different proteins (9aa). Additionally, 21% of missing entries didn't generate any unique tryptic peptides. In silico endopeptidase combination strategy increased the possibility of missing proteins identification. Coherently, using both mature protein database and signal peptidome database could be a promising option to identify some missing proteins by targeting their unique N-terminal tryptic peptide from mature protein database and or C-terminus tryptic peptide from signal peptidome database. In conclusion, Identification of missing protein requires additional consideration during sample preparation, extraction, digestion and data analysis to increase its incidence of identification.

Blood-cell targeting Autoimmune Diseases (BLADs) are complex diseases that affect blood cell formation or prevent blood cell production. Since these clinical conditions are gathering growing attention, experimental approaches are being used to investigate the mechanisms behind their pathogenesis and to identify proteins associated with them. However, computational approaches have not been utilized extensively in the study of BLADs. This study aims to investigate the interaction network of proteins associated with BLADs (BLAD interactome) and to identify novel associations with other human proteins. The method followed in this study combines information regarding protein-protein interaction network properties and autoimmune disease terms. Proteins with high network scores and statistically significant autoimmune disease term enrichment were obtained and 14 of them were designated as candidate proteins associated with BLADs. Additionally, clustering analysis of the BLAD interactome was used and allowed the detection of 17 proteins that act as “connectors” of different BLADs. We expect our findings to further extend experimental efforts for the investigation of the pathogenesis and the relationships of BLADs.