Protein-protein Interaction Pairs Research Articles

MInsc coordinates Par3 and NuMA condensates for assembly of the spindle orientation machinery in asymmetric cell division.

As a fundamental process governing the self-renewal and differentiation of stem cells, asymmetric cell division is controlled by several conserved regulators, including the polarity protein Par3 and the microtubule-associated protein NuMA, which orchestrate the assembly and interplay of the Par3/Par6/mInsc/LGN complex at the apical cortex and the LGN/Gαi/NuMA/Dynein complex at the mitotic spindle to ensure asymmetric segregation of cell fate determinants. However, this model, which is well-supported by genetic studies, has been challenged by evidence of competitive interaction between NuMA and mInsc for LGN. Here, the solved crystal structure of the Par3/mInsc complex reveals that mInsc competes with Par6β for Par3, raising questions about how proteins assemble overlapping targets into functional macromolecular complexes. Unanticipatedly, we discover that Par3 can recruit both Par6β and mInsc by forming a dynamic condensate through phase separation. Similarly, the phase-separated NuMA condensate enables the coexistence of competitive NuMA and mInsc with LGN in the same compartment. Bridge by mInsc, Par3/Par6β and LGN/NuMA condensates coacervate, robustly enriching all five proteins both in vitro and within cells. These findings highlight the pivotal role of protein condensates in assembling multi-component signalosomes that incorporate competitive protein-protein interaction pairs, effectively overcoming stoichiometric constraints encountered in conventional protein complexes.

Structure-Based Design and Discovery of a Potent and Cell-Active LC3A/B Covalent Inhibitor.

Autophagy is a highly conserved cellular homeostasis maintenance mechanism in eukaryotes. Microtubule-associated protein light chain 3 (LC3) plays a crucial role in autophagy. It has multiple pairs of protein-protein interactions (PPIs) with other proteins, and these PPIs have an effect on the regulation of autophagosome formation and the recruitment of autophagic substrates. In our previous work, a small molecule covalent inhibitor DC-LC3in-D5 which could inhibit LC3A/B PPIs was identified, but a detailed study of structure-activity relationships (SARs) was lacking. Herein, a new molecule LC3in-C42 was discovered utilizing the hybridization of advantageous fragments, whose potency (IC50 = 7.6 nM) had been greatly improved compared with that of DC-LC3in-D5. LC3in-C42 inhibits autophagy at the cellular level and its efficacy far exceeds that of DC-LC3in-D5. Thus far, LC3in-C42 stands as the most potent LC3A/B small molecule inhibitor. LC3in-C42 could serve as a powerful tool for LC3A/B protein and autophagy research.

Mapping the protein–protein interactome in the tumor immune microenvironment

Abstract The cell-to-cell communication primarily occurs through cell-surface and secreted proteins, which form a sophisticated network that coordinates systemic immune function. Uncovering these protein–protein interactions (PPIs) is indispensable for understanding the molecular mechanism and elucidating immune system aberrances under diseases. Traditional biological studies typically focus on a limited number of PPI pairs due to the relative low throughput of commonly used techniques. Encouragingly, classical methods have advanced, and many new systems tailored for large-scale protein–protein screening have been developed and successfully utilized. These high-throughput PPI investigation techniques have already made considerable achievements in mapping the immune cell interactome, enriching PPI databases and analysis tools, and discovering therapeutic targets for cancer and other diseases, which will definitely bring unprecedented insight into this field.

Potential Key Proteins, Molecular Networks, and Pathways in Perinatal Hypoxia.

Perinatal hypoxia is a common risk factor for CNS development. Using bioinformatics databases, a list of 129 genes involved in perinatal hypoxia was selected from the literature and analyzed with respect to proteins important for biological processes influencing the brain development. Functional enrichment analysis using the DAVID database was performed to identify relevant Gene Ontology (GO) biological processes like response to hypoxia, inflammatory response, positive and negative regulation of apoptosis, and positive and negative regulation of cell proliferation. The selected GO processes contain 17-30 proteins and show an enrichment of 6.3-14.3-fold. The STRING protein-protein interaction network and the Cytoscape data analyzer were used to identify interacting proteins playing a significant role in these processes. The two top protein pairs referring to the proteins with highest degrees and the corresponding proteins connected by high score edges exert opposite or regulatory functions and are essential for the balance between damaging, repairing, protective, or epigenetic processes. The GO response to hypoxia is characterized by the high score protein-protein interaction pairs CASP3/FAS promoting apoptosis and by the protective acting BDNF/MECP2 protein pair. Core components of the GO processes positive and negative regulation of apoptosis are the proteins CASP3/FAS/AKT/eNOS/RPS6KB1 involved in several signal pathways. The GO processes cell proliferation are characterized by the high-score protein-protein interaction pairs MYC/ MAPK1, JUN/MAPK1, IL6/IL1B, and JUN/HDAC1. The study provides new insights into the pathophysiology of perinatal hypoxia and is of importance for future investigations, diagnostics, and therapy of perinatal hypoxia.

Targeted cross-linker delivery for the in situ mapping of protein conformations and interactions in mitochondria

Current methods for intracellular protein analysis mostly require the separation of specific organelles or changes to the intracellular environment. However, the functions of proteins are determined by their native microenvironment as they usually form complexes with ions, nucleic acids, and other proteins. Here, we show a method for in situ cross-linking and analysis of mitochondrial proteins in living cells. By using the poly(lactic-co-glycolic acid) (PLGA) nanoparticles functionalized with dimethyldioctadecylammonium bromide (DDAB) to deliver protein cross-linkers into mitochondria, we subsequently analyze the cross-linked proteins using mass spectrometry. With this method, we identify a total of 74 pairs of protein-protein interactions that do not exist in the STRING database. Interestingly, our data on mitochondrial respiratory chain proteins ( ~ 94%) are also consistent with the experimental or predicted structural analysis of these proteins. Thus, we provide a promising technology platform for in situ defining protein analysis in cellular organelles under their native microenvironment.

Orthogonal Light-Dependent Membrane Adhesion Induces Social Self-Sorting and Member-Specific DNA Communication in Synthetic Cell Communities.

Developing orthogonal chemical communication pathways in diverse synthetic cell communities is a considerable challenge due to the increased crosstalk and interference associated with large numbers of different types of sender-receiver pairs. Herein, the authors control which sender-receiver pairs communicate in a three-membered community of synthetic cells through red and blue light illumination. Semipermeable protein-polymer-based synthetic cells (proteinosomes) with complementary membrane-attached protein adhesion communicate through single-stranded DNA oligomers and synergistically process biochemical information within a community consisting of one sender and two different receiver populations. Different pairs of red and blue light-responsive protein-protein interactions act as membrane adhesion mediators between the sender and receivers such that they self-assemble and socially self-sort into different multicellular structures under red and blue light. Consequently, distinct sender-receiver pairs come into the signaling range depending on the light illumination and are able to communicate specifically without activation of the other receiver population. Overall, this work shows how photoswitchable membrane adhesion gives rise to different self-sorting protocell patterns that mediate member-specific DNA-based communication in ternary populations of synthetic cells and provides a step towards the design of orthogonal chemical communication networks in diverse communities of synthetic cells.

Modular Peroxidase-Based Reporters for Detecting Protease Activity and Protein Interactions with Temporal Gating.

Enzymatic reporters have been widely applied to study various biological processes because they can amplify signal through enzymatic reactions and provide good sensitivity. However, there is still a need for modular motifs for designing a series of enzymatic reporters. Here, we report a modular peroxidase-based motif, named CLAPon, that features acid-base coil-caged enhanced ascorbate peroxidase (APEX). We demonstrate the modularity of CLAPon by designing a series of reporters for detecting protease activity and protein-protein interactions (PPIs). CLAPon for protease activity showed a 390-fold fluorescent signal increase upon tobacco etch virus protease cleavage. CLAPon for PPI detection (PPI-CLAPon) has two variants, PPI-CLAPon1.0 and 1.1. PPI-CLAPon1.0 showed a signal-to-noise ratio (SNR) of up to 107 for high-affinity PPI pairs and enabled imaging with sub-cellular spatial resolution. However, the more sensitive PPI-CLAPon1.1 is required for detecting low-affinity PPI pairs. PPI-CLAPon1.0 was further engineered to a reporter with light-dependent temporal gating, called LiPPI-CLAPon1.0, which can detect a 3-min calcium-dependent PPI with an SNR of 17. LiPPI-CLAPon enables PPI detection within a specific time window with rapid APEX activation and diverse readout. Lastly, PPI-CLAPon1.0 was designed to have chemical gating, providing more versatility to complement the LiPPI-CLAPon. These CLAPon-based reporter designs can be broadly applied to study various signaling processes that involve protease activity and PPIs and provide a versatile platform to design various genetically encoded reporters.

Identification of genes related to sexual differentiation and sterility in embryonic gonads of Mule ducks by transcriptome analysis.

The key genes of avian gonadal development are of great significance for sex determination. Transcriptome sequencing analysis of Mule duck gonad as potential sterile model is expected to screen candidate genes related to avian gonad development. In this study, the embryonic gonadal tissues of Mule ducks, Jinding ducks, and Muscovy ducks were collected and identified. Six sample groups including female Mule duck (A), male Mule duck (B), female Jinding duck (C), male Jinding duck (D), female Muscovy duck (E), and male Muscovy duck (F) were subjected to RNA sequencing analysis. A total of 9,471 differential genes (DEGs) and 691 protein-protein interaction pairs were obtained. Totally, 12 genes (Dmrt1, Amh, Sox9, Tex14, Trim71, Slc26a8, Spam1, Tdrp, Tsga10, Boc, Cxcl14, and Hsd17b3) were identified to be specifically related to duck testicular development, and 11 genes (Hsd17b1, Cyp19a1, Cyp17a1, Hhipl2, Tdrp, Uts2r, Cdon, Axin2, Nxph1, Brinp2, and Brinp3) were specifically related to duck ovarian development. Seven genes (Stra8, Dmc1, Terb1, Tex14, Tsga10, Spam1, and Plcd4) were screened to be specifically involved in the female sterility of Mule ducks; eight genes (Gtsf1, Nalcn, Tat, Slc26a8, Kmo, Plcd4, Aldh4a1, and Hgd) were specifically involved in male sterility; and five genes (Terb1, Stra8, Tex14 Tsga10 and Spam1) were involved in both female and male sterility. This study provides an insight into the differential development between male and female gonads of ducks and the sterility mechanism of Mule ducks through function, pathway, and protein interaction analyses. Our findings provide theoretical basis for the further research on sex determination and differentiation of birds and the sterility of Mule ducks.

A novel prognostic 6-gene signature for osteoporosis.

IntroductionThe incidence of osteoporosis (OP) keeps increasing due to global aging of the population. Therefore, identifying the diagnostic and prognostic biomarkers of OP is of great significance.MethodsmRNA data from OP and non-OP samples were obtained from GEO database, which were divided into training set (GSE35959) and testing sets (GSE7158, GSE62402, GSE7429 and GSE56815). Gene modules most significantly related to OP were revealed using weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) between OP and normal samples in training set were identified using limma R package. Thereafter, above two gene sets were intersected to obtain the genes potentially related to OP. Protein-protein interaction (PPI) pairs were screened by STRING database and visualized using Cytoscape, while the plug-in cytoHubba was used to screen hub genes by determining their topological parameters. Afterwards, a diagnostic model was constructed using those hub genes, whose creditability was further evaluated by testing sets.ResultsThe results of WGCNA analysis found the Black module was most significantly related to OP, which included altogether 1286 genes. Meanwhile, 2771 DEGs were discovered between OP patients and the normal controls. After taking the intersection, 479 genes were identified potentially correlated with the development of OP. Subsequently, six hub genes were discovered through PPI network construction and node topological analysis. Finally, we constructed a support vector machine model based on these six genes, which can accurately classified training and testing set samples into OP and normal groups.ConclusionOur current study constructed a six hub genes-based diagnostic model for OP. Our findings may shed some light on the research of the early diagnosis for OP and had certain practical significance.

Protein-protein interaction networks as miners of biological discovery.

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid (Y2H), mass spectrometry (MS), co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks.

DWPPI: A Deep Learning Approach for Predicting Protein-Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network.

The prediction of protein–protein interactions (PPIs) in plants is vital for probing the cell function. Although multiple high-throughput approaches in the biological domain have been developed to identify PPIs, with the increasing complexity of PPI network, these methods fall into laborious and time-consuming situations. Thus, it is essential to develop an effective and feasible computational method for the prediction of PPIs in plants. In this study, we present a network embedding-based method, called DWPPI, for predicting the interactions between different plant proteins based on multi-source information and combined with deep neural networks (DNN). The DWPPI model fuses the protein natural language sequence information (attribute information) and protein behavior information to represent plant proteins as feature vectors and finally sends these features to a deep learning–based classifier for prediction. To validate the prediction performance of DWPPI, we performed it on three model plant datasets: Arabidopsis thaliana (A. thaliana), mazie (Zea mays), and rice (Oryza sativa). The experimental results with the fivefold cross-validation technique demonstrated that DWPPI obtains great performance with the AUC (area under ROC curves) values of 0.9548, 0.9867, and 0.9213, respectively. To further verify the predictive capacity of DWPPI, we compared it with some different state-of-the-art machine learning classifiers. Moreover, case studies were performed with the AC149810.2_FGP003 protein. As a result, 14 of the top 20 PPI pairs identified by DWPPI with the highest scores were confirmed by the literature. These excellent results suggest that the DWPPI model can act as a promising tool for related plant molecular biology.

The Protein-Protein Interaction Network of Hereditary Parkinsonism Genes Is a Hierarchical Scale-Free Network.

PurposeHereditary parkinsonism genes consist of causative genes of familial Parkinson’s disease (PD) with a locus symbol prefix (PARK genes) and hereditary atypical parkinsonian disorders that present atypical features and limited responsiveness to levodopa (non-PARK genes). Although studies have shown that hereditary parkinsonism genes are related to idiopathic PD at the phenotypic, gene expression, and genomic levels, no study has systematically investigated connectivity among the proteins encoded by these genes at the protein-protein interaction (PPI) level.Materials and MethodsTopological measurements and physical interaction enrichment were performed to assess PPI networks constructed using some or all the proteins encoded by hereditary parkinsonism genes (n=96), which were curated using the Online Mendelian Inheritance in Man database and literature.ResultsNon-PARK and PARK genes were involved in common functional modules related to autophagy, mitochondrial or lysosomal organization, catecholamine metabolic process, chemical synapse transmission, response to oxidative stress, neuronal apoptosis, regulation of cellular protein catabolic process, and vesicle-mediated transport in synapse. The hereditary parkinsonism proteins formed a single large network comprising 51 nodes, 83 edges, and three PPI pairs. The probability of degree distribution followed a power-law scaling behavior, with a degree exponent of 1.24 and a correlation coefficient of 0.92. LRRK2 was identified as a hub gene with the highest degree of betweenness centrality; its physical interaction enrichment score was 1.28, which was highly significant.ConclusionBoth PARK and non-PARK genes show high connectivity at the PPI and biological functional levels.

RNA-RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection.

Some studies reported that genomic RNA of SARS-CoV-2 can absorb a few host miRNAs that regulate immune-related genes and then deprive their function. In this perspective, we conjecture that the absorption of the SARS-CoV-2 genome to host miRNAs is not a coincidence, which may be an indispensable approach leading to viral survival and development in host. In our study, we collected five datasets of miRNAs that were predicted to interact with the genome of SARS-CoV-2. The targets of these miRNAs in the five groups were consistently enriched immune-related pathways and virus-infectious diseases. Interestingly, the five datasets shared no one miRNA but their targets shared 168 genes. The signaling pathway enrichment of 168 shared targets implied an unbalanced immune response that the most of interleukin signaling pathways and none of the interferon signaling pathways were significantly different. Protein–protein interaction (PPI) network using the shared targets showed that PPI pairs, including IL6-IL6R, were related to the process of SARS-CoV-2 infection and pathogenesis. In addition, we found that SARS-CoV-2 absorption to host miRNA could benefit two popular mutant strains for more infectivity and pathogenicity. Conclusively, our results suggest that genomic RNA absorption to host miRNAs may be a vital approach by which SARS-CoV-2 disturbs the host immune system and infects host cells.

Over-expression of EGFR regulated by RARA contributes to 5-FU resistance in colon cancer.

A promising new strategy for cancer therapy is to target the autophagic pathway. However, comprehensive characterization of autophagy genes and their clinical relevance in cancer is still lacking. Here, we systematically characterized alterations of autophagy genes in multiple cancer lines by analyzing data from The Cancer Genome Atlas and CellMiner database. Interactions between autophagy genes and clinically actionable genes (CAGs) were identified by analyzing co-expression, protein-protein interactions (PPIs) and transcription factor (TF) data. A key subnetwork was identified that included 18 autophagy genes and 22 CAGs linked by 28 PPI pairs and 1 TF-target pair, which was EGFR targeted by RARA. Alterations in the expression of autophagy genes were associated with patient survival in multiple cancer types. RARA and EGFR were associated with worse survival in colorectal cancer patients. The regulatory role of EGFR in 5-FU resistance was validated in colon cancer cells in vivo and in vitro. EGFR contributed to 5-FU resistance in colon cancer cells through autophagy induction, and EGFR overexpression in 5-FU resistant colon cancer was regulated by RARA. The present study provides a comprehensive analysis of autophagy in different cancer cell lines and highlights the potential clinical utility of targeting autophagy genes.

Identification of potential pathogenic candidates or diagnostic biomarkers in papillary thyroid carcinoma using expression and methylation profiles.

The mechanisms underlying the pathogenesis of papillary thyroid carcinoma (PTC) have not yet been elucidated. The aim of the current study was to identify potential pathogenic biomarkers in PTC by comprehensively analyzing gene expression and methylation profiles, and to increase the understanding of PTC pathogenesis. The gene expression profiles of the GSE97001 and GSE83520 datasets, the miRNA expression profiles of the GSE73182 dataset, and the DNA methylation profiles of the GSE86961 and GSE97466 datasets were downloaded from Gene Expression Omnibus database. The differentially expressed genes (DEGs) and the differentially expressed microRNAs (DEMs) were identified using the limma package in R, and the differentially methylated sites (DMSs) were identified using the β distribution and two-sample t-tests. The Database for Annotation, Visualization and Integrated Discovery, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome were subsequently used to perform functional and pathway enrichment analysis. The miRNA target genes were predicted using the online databases miRWalk. The protein-protein interactions (PPI) were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins. The regulatory network was constructed, and the gene expression and methylation levels of the key nodes were detected using reverse-transcription quantitative-polymerase chain reaction (PCR) and methylation-specific PCR. A total of 155 overlapping DEGs were identified between the GSE97001 and GSE83520 datasets, and 19 DEMs between PTC tissue and normal tissue samples were identified in the GSE73182 set. In the GSE86961 and GSE97466 datasets, 2,910 overlapping DMSs that were associated with 38 downregulated methylated genes were identified. The overlapping DEGs were enriched in 46 Gene Ontology terms and one KEGG pathway. A total of 60 PPI pairs were identified for the overlapping DEGs and 12 negative miRNA-gene pairs were identified for the DEMs. The expression levels of hsa-miR-199a-5p and decorin (DCN) were decreased in patients with PTC. C-X-C motif chemokine ligand 12 (CXCL12) was hypermethylated and had a decreased expression level in PTC tissues. LDL receptor related protein 4 (LRP4) and carbonic anhydrase 12 (CA12) were hypomethylated and had an increased expression level. The present study revealed that hsa-miR-199a-5p, DCN, CXCL12, LRP4 and CA12 may serve important roles in the pathogenesis of PTC.

Megadock-Web: An Integrated Database of High-Throughput Structure-Based Protein-Protein Interaction Predictions

Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7,528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. MEGADOCK-Web is freely available for use at http://www.bi.cs.titech.ac.jp/megadock-web/.

Exploring the binding mechanism between human profilin (PFN1) and polyproline-10 through binding mode screening.

The large magnitude of protein-protein interaction (PPI) pairs within the human interactome necessitates the development of predictive models and screening tools to better understand this fundamental molecular communication. However, despite enormous efforts from various groups to develop predictive techniques in the last decade, PPI complex structures are in general still very challenging to predict due to the large number of degrees of freedom. In this study, we use the binding complex of human profilin (PFN1) and polyproline-10 (P10) as a model system to examine various approaches, with the aim of going beyond normal protein docking for PPI prediction and evaluation. The potential of mean force (PMF) was first obtained from the time-consuming umbrella sampling, which confirmed that the most stable binding structure identified by the maximal PMF difference is indeed the crystallographic binding structure. Moreover, crucial residues previously identified in experimental studies, W3, H133, and S137 of PFN1, were found to form favorable hydrogen bonds with P10, suggesting a zipping process during the binding between PFN1 and P10. We then explored both regular molecular dynamics (MD) and steered molecular dynamics (SMD) simulations, seeking for better criteria of ranking the PPI prediction. Despite valuable information obtained from conventional MD simulations, neither the commonly used interaction energy between the two binding parties nor the long-term root mean square displacement correlates well with the PMF results. On the other hand, with a sizable collection of trajectories, we demonstrated that the average and minimal rupture works calculated from SMD simulations correlate fairly well with the PMFs (R 2 = 0.67), making this a promising PPI screening method.

In silico-prediction of protein\u2013protein interactions network about MAPKs and PP2Cs reveals a novel docking site variants in Brachypodium distachyon

Protein-protein interactions (PPIs) underlie the molecular mechanisms of most biological processes. Mitogen-activated protein kinases (MAPKs) can be dephosphorylated by MAPK-specific phosphatases such as PP2C, which are critical to transduce extracellular signals into adaptive and programmed responses. However, the experimental approaches for identifying PPIs are expensive, time-consuming, laborious and challenging. In response, many computational methods have been developed to predict PPIs. Yet, these methods have inherent disadvantages such as high false positive and negative results. Thus, it is crucial to develop in silico approaches for predicting PPIs efficiently and accurately. In this study, we identified PPIs among 16 BdMAPKs and 86 BdPP2Cs in B. distachyon using a novel docking approach. Further, we systematically investigated the docking site (D-site) of BdPP2C which plays a vital role for recognition and docking of BdMAPKs. D-site analysis revealed that there were 96 pairs of PPIs including all BdMAPKs and most BdPP2Cs, which indicated that BdPP2C may play roles in other signaling networks. Moreover, most BdPP2Cs have a D-site for BdMAPKs in our prediction results, which suggested that our method can effectively predict PPIs, as confirmed by their 3D structure. In addition, we validated this methodology with known Arabidopsis and yeast phosphatase-MAPK interactions from the STRING database. The results obtained provide a vital research resource for exploring an accurate network of PPIs between BdMAPKs and BdPP2Cs.

ProfPPIdb: Pairs of physical protein-protein interactions predicted for entire proteomes.

MotivationProtein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods.ResultsWe extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, recent methods explicitly ignored such a network-structure and rely either on domain knowledge or sequence-only methods. Our approach is independent of domain-knowledge and leverages evolutionary information. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/. The data can also be downloaded from https://figshare.com/collections/ProfPPI-DB/4141784.

Integrated network analysis to identify the key genes, transcription factors, and microRNAs involved in hepatocellular carcinoma.

HCC (hepatocellular carcinoma), which can be induced by cirrhosis and viral hepatitis infection, is the most frequent form of liver cancer. This study is performed to investigate the mechanisms of HCC. GSE57957 was obtained from Gene Expression Omnibus database, including 39 HCC samples and 39 adjacent non-tumorous samples. The DEGs (differentially expressed genes) were screened using the limma package in R, and then were conducted with enrichment analysis using "BioCloud" platform. Using STRING database, WebGestalt tool, as well as ITFP and TRANSFAC databases, PPI (protein-protein interaction) pairs, miRNA (microRNA)-target pairs, and TF (transcription factor)-target pairs separately were predicted. Followed by integrated network was constructed by Cytoscape software and module analysis was performed using the MCODE plugin of Cytoscape software. There were 518 DEGs identified from the HCC samples, among which 17 up-regulated genes (including MCM2, MCM6, and CDC20) and 5 down-regulated genes could also function as TFs. In the integrated network for the down-regulated genes, FOS and ESR1 had higher degrees, and both of them were targeted by miR-221 and miR-222. Additionally, MCM2 had interaction with MCM6 in the up-regulated module with the highest score. MCM2, MCM6, CDC20, FOS, ESR1, miR-221 and miR-222 might affect the pathogenesis of HCC.