A computational interactome and functional annotation for the human proteome

We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function.

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Integrated analysis of the expression, involved functions, and regulatory network of RUNX3 in melanoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210816121833 ◽

2021 ◽

Vol 24 ◽

Author(s):

Yanxin Liu ◽

Zhang Feng ◽

Huaxia Chen

Keyword(s):

Protein Interactions ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Integrated Analysis ◽

Protein Protein Interactions ◽

Nf Kappab ◽

Microarray Datasets

Background: As a tumor suppressor or oncogenic gene, abnormal expression of RUNX family transcription factor 3 (RUNX3) has been reported in various cancers. Introduction: This study aimed to investigate the role of RUNX3 in melanoma. Methods: The expression level of RUNX3 in melanoma tissues was analyzed by immunohistochemistry and the Oncomine database. Based on microarray datasets GSE3189 and GSE7553, differentially expressed genes (DEGs) in melanoma samples were screened, followed by functional enrichment analysis. Gene Set Enrichment Analysis (GSEA) was performed for RUNX3. DEGs that co-expressed with RUNX3 were analyzed, and the transcription factors (TFs) of RUNX3 and its co-expressed genes were predicted. The protein-protein interactions (PPIs) for RUNX3 were analyzed utilizing the GeneMANIA database. MicroRNAs (miRNAs) that could target RUNX3 expression, were predicted. Results : RUNX3 expression was significantly up-regulated in melanoma tissues. GSEA showed that RUNX3 expression was positively correlated with melanogenesis and melanoma pathways. Eleven DEGs showed significant co-expression with RUNX3 in melanoma, for example, TLE4 was negatively co-expressed with RUNX3. RUNX3 was identified as a TF that regulated the expression of both itself and its co-expressed genes. PPI analysis showed that 20 protein-encoding genes interacted with RUNX3, among which 9 genes were differentially expressed in melanoma, such as CBFB and SMAD3. These genes were significantly enriched in transcriptional regulation by RUNX3, RUNX3 regulates BCL2L11 (BIM) transcription, regulation of I-kappaB kinase/NF-kappaB signaling, and signaling by NOTCH. A total of 31 miRNAs could target RUNX3, such as miR-326, miR-330-5p, and miR-373-3p. Conclusion: RUNX3 expression was up-regulated in melanoma and was implicated in the development of melanoma.

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pathDIP 4: an extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species

Nucleic Acids Research ◽

10.1093/nar/gkz989 ◽

2019 ◽

Cited By ~ 5

Author(s):

Sara Rahmati ◽

Mark Abovsky ◽

Chiara Pastrello ◽

Max Kotlyar ◽

Richard Lu ◽

...

Keyword(s):

Protein Interactions ◽

Enrichment Analysis ◽

Human Model ◽

Model Organisms ◽

Pathway Enrichment Analysis ◽

Protein Protein Interactions ◽

Proteome Coverage ◽

Pathway Annotation ◽

Human Proteins ◽

Integrated Pathway

Abstract PathDIP was introduced to increase proteome coverage of literature-curated human pathway databases. PathDIP 4 now integrates 24 major databases. To further reduce the number of proteins with no curated pathway annotation, pathDIP integrates pathways with physical protein–protein interactions (PPIs) to predict significant physical associations between proteins and curated pathways. For human, it provides pathway annotations for 5366 pathway orphans. Integrated pathway annotation now includes six model organisms and ten domesticated animals. A total of 6401 core and ortholog pathways have been curated from the literature or by annotating orthologs of human proteins in the literature-curated pathways. Extended pathways are the result of combining these pathways with protein-pathway associations that are predicted using organism-specific PPIs. Extended pathways expand proteome coverage from 81 088 to 120 621 proteins, making pathDIP 4 the largest publicly available pathway database for these organisms and providing a necessary platform for comprehensive pathway-enrichment analysis. PathDIP 4 users can customize their search and analysis by selecting organism, identifier and subset of pathways. Enrichment results and detailed annotations for input list can be obtained in different formats and views. To support automated bioinformatics workflows, Java, R and Python APIs are available for batch pathway annotation and enrichment analysis. PathDIP 4 is publicly available at http://ophid.utoronto.ca/pathDIP.

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Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

Current Medicinal Chemistry ◽

10.2174/0929867325666180214113704 ◽

2019 ◽

Vol 26 (21) ◽

pp. 3890-3910 ◽

Cited By ~ 6

Author(s):

Branislava Gemovic ◽

Neven Sumonja ◽

Radoslav Davidovic ◽

Vladimir Perovic ◽

Nevena Veljkovic

Keyword(s):

Drug Discovery ◽

Protein Interactions ◽

Protein Complexes ◽

Experimental Studies ◽

Protein Protein Interactions ◽

Web Based ◽

Prediction Tools ◽

Physiological Processes ◽

Modern Drug ◽

Ppi Prediction

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

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Protein Interaction Domains and Post-Translational Modifications: Structural Features and Drug Discovery Applications

Current Medicinal Chemistry ◽

10.2174/0929867326666190620101637 ◽

2020 ◽

Vol 27 (37) ◽

pp. 6306-6355 ◽

Cited By ~ 2

Author(s):

Marian Vincenzi ◽

Flavia Anna Mercurio ◽

Marilisa Leone

Keyword(s):

Drug Discovery ◽

Protein Interaction ◽

Protein Interactions ◽

Structural Information ◽

Protein Complexes ◽

Structural Features ◽

Protein Protein Interactions ◽

Modular Architecture ◽

Post Translational Modifications ◽

Interaction Domains

Background:: Many pathways regarding healthy cells and/or linked to diseases onset and progression depend on large assemblies including multi-protein complexes. Protein-protein interactions may occur through a vast array of modules known as protein interaction domains (PIDs). Objective:: This review concerns with PIDs recognizing post-translationally modified peptide sequences and intends to provide the scientific community with state of art knowledge on their 3D structures, binding topologies and potential applications in the drug discovery field. Method:: Several databases, such as the Pfam (Protein family), the SMART (Simple Modular Architecture Research Tool) and the PDB (Protein Data Bank), were searched to look for different domain families and gain structural information on protein complexes in which particular PIDs are involved. Recent literature on PIDs and related drug discovery campaigns was retrieved through Pubmed and analyzed. Results and Conclusion:: PIDs are rather versatile as concerning their binding preferences. Many of them recognize specifically only determined amino acid stretches with post-translational modifications, a few others are able to interact with several post-translationally modified sequences or with unmodified ones. Many PIDs can be linked to different diseases including cancer. The tremendous amount of available structural data led to the structure-based design of several molecules targeting protein-protein interactions mediated by PIDs, including peptides, peptidomimetics and small compounds. More studies are needed to fully role out, among different families, PIDs that can be considered reliable therapeutic targets, however, attacking PIDs rather than catalytic domains of a particular protein may represent a route to obtain selective inhibitors.

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Frequent assembly of chimeric complexes in the protein interaction network of an interspecies yeast hybrid

Molecular Biology and Evolution ◽

10.1093/molbev/msaa298 ◽

2020 ◽

Author(s):

Rohan Dandage ◽

Caroline M Berger ◽

Isabelle Gagnon-Arsenault ◽

Kyung-Mee Moon ◽

Richard Greg Stacey ◽

...

Keyword(s):

Protein Interactions ◽

Molecular Level ◽

Yeast Species ◽

Protein Complexes ◽

Mitochondrial Protein ◽

Chimeric Protein ◽

Interaction Network ◽

Protein Protein Interactions ◽

Extreme Phenotypes ◽

Yeast Hybrid

Abstract Hybrids between species often show extreme phenotypes, including some that take place at the molecular level. In this study, we investigated the phenotypes of an interspecies diploid hybrid in terms of protein-protein interactions inferred from protein correlation profiling. We used two yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum, which are interfertile, but yet have proteins diverged enough to be differentiated using mass spectrometry. Most of the protein-protein interactions are similar between hybrid and parents, and are consistent with the assembly of chimeric complexes, which we validated using an orthogonal approach for the prefoldin complex. We also identified instances of altered protein-protein interactions in the hybrid, for instance in complexes related to proteostasis and in mitochondrial protein complexes. Overall, this study uncovers the likely frequent occurrence of chimeric protein complexes with few exceptions, which may result from incompatibilities or imbalances between the parental proteins.

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Mass spectrometry-based cross-linking study shows that the Psb28 protein binds to cytochrome b559 in Photosystem II

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1620360114 ◽

2017 ◽

Vol 114 (9) ◽

pp. 2224-2229 ◽

Cited By ~ 18

Author(s):

Daniel A. Weisz ◽

Haijun Liu ◽

Hao Zhang ◽

Sundarapandian Thangapandian ◽

Emad Tajkhorshid ◽

...

Keyword(s):

Mass Spectrometry ◽

Photosystem Ii ◽

Protein Interactions ◽

Protein Complexes ◽

Protein Docking ◽

Cross Linking ◽

Protein Protein Interactions ◽

Cytochrome B559 ◽

Reaction Center Complex ◽

Assembly Intermediate

Photosystem II (PSII), a large pigment protein complex, undergoes rapid turnover under natural conditions. During assembly of PSII, oxidative damage to vulnerable assembly intermediate complexes must be prevented. Psb28, the only cytoplasmic extrinsic protein in PSII, protects the RC47 assembly intermediate of PSII and assists its efficient conversion into functional PSII. Its role is particularly important under stress conditions when PSII damage occurs frequently. Psb28 is not found, however, in any PSII crystal structure, and its structural location has remained unknown. In this study, we used chemical cross-linking combined with mass spectrometry to capture the transient interaction of Psb28 with PSII. We detected three cross-links between Psb28 and the α- and β-subunits of cytochrome b559, an essential component of the PSII reaction-center complex. These distance restraints enable us to position Psb28 on the cytosolic surface of PSII directly above cytochrome b559, in close proximity to the QB site. Protein–protein docking results also support Psb28 binding in this region. Determination of the Psb28 binding site and other biochemical evidence allow us to propose a mechanism by which Psb28 exerts its protective effect on the RC47 intermediate. This study also shows that isotope-encoded cross-linking with the “mass tags” selection criteria allows confident identification of more cross-linked peptides in PSII than has been previously reported. This approach thus holds promise to identify other transient protein–protein interactions in membrane protein complexes.

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Metallocluster transactions: dynamic protein interactions guide the biosynthesis of Fe–S clusters in bacteria

Biochemical Society Transactions ◽

10.1042/bst20180365 ◽

2018 ◽

Vol 46 (6) ◽

pp. 1593-1603 ◽

Cited By ~ 9

Author(s):

Chenkang Zheng ◽

Patricia C. Dos Santos

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Specific Protein ◽

Biological Synthesis ◽

Cluster Assembly ◽

Sequence Motifs ◽

Protein Protein Interactions ◽

Cysteine Desulfurase ◽

Wide Range ◽

Domains Of Life

Iron–sulfur (Fe–S) clusters are ubiquitous cofactors present in all domains of life. The chemistries catalyzed by these inorganic cofactors are diverse and their associated enzymes are involved in many cellular processes. Despite the wide range of structures reported for Fe–S clusters inserted into proteins, the biological synthesis of all Fe–S clusters starts with the assembly of simple units of 2Fe–2S and 4Fe–4S clusters. Several systems have been associated with the formation of Fe–S clusters in bacteria with varying phylogenetic origins and number of biosynthetic and regulatory components. All systems, however, construct Fe–S clusters through a similar biosynthetic scheme involving three main steps: (1) sulfur activation by a cysteine desulfurase, (2) cluster assembly by a scaffold protein, and (3) guided delivery of Fe–S units to either final acceptors or biosynthetic enzymes involved in the formation of complex metalloclusters. Another unifying feature on the biological formation of Fe–S clusters in bacteria is that these systems are tightly regulated by a network of protein interactions. Thus, the formation of transient protein complexes among biosynthetic components allows for the direct transfer of reactive sulfur and Fe–S intermediates preventing oxygen damage and reactions with non-physiological targets. Recent studies revealed the importance of reciprocal signature sequence motifs that enable specific protein–protein interactions and consequently guide the transactions between physiological donors and acceptors. Such findings provide insights into strategies used by bacteria to regulate the flow of reactive intermediates and provide protein barcodes to uncover yet-unidentified cellular components involved in Fe–S metabolism.

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H2V, a Database for Human Proteins and Genes in Response to SARS-CoV-2, SARS-CoV, and MERS-CoV Infection

10.21203/rs.3.rs-61338/v1 ◽

2020 ◽

Author(s):

Nan Zhou ◽

Jinku Bao ◽

Yuping Ning

Keyword(s):

Differentially Expressed Genes ◽

Protein Interactions ◽

Viral Infections ◽

Antiviral Agents ◽

Differentially Expressed ◽

Easy Access ◽

Differentially Expressed Proteins ◽

Protein Protein Interactions ◽

Associated Proteins ◽

Human Proteins

Abstract The ongoing COVID-19 pandemic in the world is caused by SARS-CoV-2, a new coronavirus firstly discovered in the end of 2019. It has led to more than 10 million confirmed cases and more than 500,000 confirmed deaths across 216 countries by 1 July 2020, according to WHO statistics. SARS-CoV-2, SARS-CoV, and MERS-CoV are alike, killing people, impairing economy, and inflicting long-term impacts on the society. However, no specific drug or vaccine has been approved as a cure for these viruses. The efforts to develop antiviral measures are hampered by insufficient understanding of molecular responses of human to viral infections. In this study, we collected experimentally validated human proteins that interact with SARS-CoV-2 proteins, human proteins whose expression, translation and phosphorylation levels experience significantly changes after SARS-CoV-2 or SARS-CoV infection, human proteins that correlate with COVID-19 severity, and human genes whose expression levels significantly changed upon SARS-CoV-2 or MERS-CoV infection. A database, H2V, was then developed for easy access to these data. Currently H2V includes: 332 human-SARS-CoV-2 protein-protein interactions; 65 differentially expressed proteins, 232 differentially translated proteins, 1298 differentially phosphorylated proteins, 204 severity associated proteins, and 4012 differentially expressed genes responding to SARS-CoV-2 infection; 66 differentially expressed proteins responding to SARS-CoV infection; and 6981 differentially expressed genes responding to MERS-CoV infection. H2V can help to understand the cellular responses associated with SARS-CoV-2, SARS-CoV and MERS-CoV infection. It is expected to speed up the development of antiviral agents and shed light on the preparation for potential coronavirus emergency in the future.Database url: http://www.zhounan.org/h2v

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Interfacial Peptides as Affinity Modulating Agents of Protein-Protein Interactions

Biomolecules ◽

10.3390/biom12010106 ◽

2022 ◽

Vol 12 (1) ◽

pp. 106

Author(s):

Pavel V. Ershov ◽

Yuri V. Mezentsev ◽

Alexis S. Ivanov

Keyword(s):

Protein Interactions ◽

Targeted Delivery ◽

Protein Complexes ◽

Protein Protein Interactions ◽

Good Efficiency ◽

Cellular Targets ◽

Proteolytic Stability ◽

Clinically Significant

The identification of disease-related protein-protein interactions (PPIs) creates objective conditions for their pharmacological modulation. The contact area (interfaces) of the vast majority of PPIs has some features, such as geometrical and biochemical complementarities, “hot spots”, as well as an extremely low mutation rate that give us key knowledge to influence these PPIs. Exogenous regulation of PPIs is aimed at both inhibiting the assembly and/or destabilization of protein complexes. Often, the design of such modulators is associated with some specific problems in targeted delivery, cell penetration and proteolytic stability, as well as selective binding to cellular targets. Recent progress in interfacial peptide design has been achieved in solving all these difficulties and has provided a good efficiency in preclinical models (in vitro and in vivo). The most promising peptide-containing therapeutic formulations are under investigation in clinical trials. In this review, we update the current state-of-the-art in the field of interfacial peptides as potent modulators of a number of disease-related PPIs. Over the past years, the scientific interest has been focused on following clinically significant heterodimeric PPIs MDM2/p53, PD-1/PD-L1, HIF/HIF, NRF2/KEAP1, RbAp48/MTA1, HSP90/CDC37, BIRC5/CRM1, BIRC5/XIAP, YAP/TAZ–TEAD, TWEAK/FN14, Bcl-2/Bax, YY1/AKT, CD40/CD40L and MINT2/APP.

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Network Pharmacology-Based Strategy to Investigate Pharmacological Mechanisms of the Drug Pair Astragalus-Angelica for Treatment of Male Infertility

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/8281506 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Feng Zhao ◽

Yingjun Deng ◽

Guanchao Du ◽

Shengjing Liu ◽

Jun Guo ◽

...

Keyword(s):

Male Infertility ◽

Signaling Pathway ◽

Mechanism Of Action ◽

Protein Interactions ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Protein Protein Interactions ◽

Drug Pair ◽

Effective Components ◽

New Ideas

Background. The traditional Chinese medicines Astragalus and Angelica are often combined to treat male infertility, but the specific therapeutic mechanism is not clear. Therefore, this study applies a network pharmacology approach to investigate the possible mechanism of action of the drug pair Astragalus-Angelica (PAA) in the treatment of male infertility. Methods. Relevant targets for PAA treatment of male infertility are obtained through databases. Protein-protein interactions (PPIs) are constructed through STRING database and screen core targets, and an enrichment analysis is conducted through the Metascape platform. Finally, molecular docking experiments were carried out to evaluate the affinity between the target protein and the ligand of PAA. Results. The active ingredients of 112 PAA, 980 corresponding targets, and 374 effective targets of PAA for the treatment of male infertility were obtained, which are related to PI3K-Akt signaling pathway, HIF-1 signaling pathway, AGE-RAGE signaling pathway, IL-17 signaling pathway, and thyroid hormone signaling pathway. Conclusion. In this study, using a network pharmacology method, we preliminarily analyzed the effective components and action targets of the PAA. We also explored the possible mechanism of action of PAA in treating male infertility. They also lay a foundation for expanding the clinical application of PAA and provide new ideas and directions for further research on the mechanisms of action of the PAA and its components for male infertility treatment.

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