Molecular Interaction Database Research Articles

IntAct Database for Accessing IMEx's Contextual Metadata of Molecular Interactions.

The International Molecular Exchange Consortium (IMEx) has evolved into a vital partnership of open resources dedicated to curating molecular interaction data from the scientific literature. This consortium, which includes IntAct, MINT, MatrixDB, and DIP, is a collaborative effort with a central mission of aggregating detailed molecular interaction experimental evidence in a machine-readable format, supported by controlled vocabularies and standard ontologies. The IntAct molecular interaction database (www.ebi.ac.uk/intact), as an IMEx partner, serves as a valuable portal for accessing IMEx data through user-friendly search options and an array of interactive filters. The resource currently hosts an extensive repository of 1,293,508 binary interactions meticulously captured from 75,098 experiments documented in 23,366 publications (as of the February 2024 release), with this corpora being added to by regular data releases. IMEx curation policy has consistently prioritized a fine-grained data and curation model, with a focus on capturing the relevant experimental details essential for interpreting molecular interaction data effectively. Our curation process is designed to support the generation of interactomes tailored to contexts such as disease-specific or tissue-/cell-type-specific interactomes. These interactions are ranked according to a scoring system based on the Proteomics Standard Initiative Molecular Interaction (PSI MI) standards. This scoring system allows users to assess the degree of confidence in binary interactions, enhancing the value of the data. The resource provides insights into the nature of relationships among interacting partners as defined by the experimental setup and the associated biological context. Interactive filters enable users to navigate these rich, multilayered data, promoting a deeper understanding of biological complexity. Additionally, the IntAct website fosters the creation of networks for collaborative analyses by the scientific community. The recent transformation of the IntAct website, supported by a graph-type database, empowers users to execute custom queries tailored to their specific research interests. This article illustrates the diverse levels of annotations available for interactions and the multiple search options at users' disposal to access data of interest. © 2024 European Molecular Biology Laboratory, European Bioinformatics Institute. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Using Quick Search, network visualization, and filters Support Protocol: Accessing fine annotations from intact: Unlocking the molecular details Alternate Protocol: Using batch search: Querying multiple interactors Basic Protocol 2: Using advanced search: Precision and customization.

Lipidomic profiling of the Brazilian yellow scorpion venom: new insights into inflammatory responses following Tityus serrulatus envenomation

ABSTRACT Due to the high prevalence and clinical relevance, scorpionism is a critical public health issue in several Brazilian regions. Tityus serrulatus, commonly known as the Brazilian yellow scorpion, is the most venomous genus found in Brazilian fauna and associated with severe clinical manifestations such as localized pain, hypertension, sweating, tachycardia and complex hyperinflammatory responses. In general, T. serrulatus venom contains a complex mixture of active compounds, including proteins, peptides, and amino acids. Although knowledge of the protein fractions of scorpion venom is available, venom lipid components are not yet comprehensively known. The aim of the present study was to determine and characterize the lipid constituents/profile of the T. serratus venom utilizing liquid chromatography coupled with high-resolution mass spectrometry. Lipid species (164 in total) belonging to 3 different lipid categories, glycerophospholipids, sphingolipids, and glycerolipids, were identified. A further search on MetaCore/MetaDrug platform, which is based upon a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism, and toxicity information, exhibited several metabolic pathways for 24 of previously identified lipid species, including activation of nuclear factor kappa B and oxidative stress pathways. Further several bioactive compounds, such as plasmalogens, lyso-platelet-activating factors, and sphingomyelins, associated with systemic responses triggered by T. serrulatus envenomation were detected. Finally, lipidomic data presented provide advanced and valuable information to better comprehend the mechanisms underlying the complex pathophysiology induced by T. serrulatus envenomation.

Inferring neuron-neuron communications from single-cell transcriptomics through NeuronChat

Neural communication networks form the fundamental basis for brain function. These communication networks are enabled by emitted ligands such as neurotransmitters, which activate receptor complexes to facilitate communication. Thus, neural communication is fundamentally dependent on the transcriptome. Here we develop NeuronChat, a method and package for the inference, visualization and analysis of neural-specific communication networks among pre-defined cell groups using single-cell expression data. We incorporate a manually curated molecular interaction database of neural signaling for both human and mouse, and benchmark NeuronChat on several published datasets to validate its ability in predicting neural connectivity. Then, we apply NeuronChat to three different neural tissue datasets to illustrate its functionalities in identifying interneural communication networks, revealing conserved or context-specific interactions across different biological contexts, and predicting communication pattern changes in diseased brains with autism spectrum disorder. Finally, we demonstrate NeuronChat can utilize spatial transcriptomics data to infer and visualize neural-specific cell-cell communication.

Drug screening and biomarker gene investigation in cancer therapy through the human transcriptional regulatory network

A complex and vast biological network regulates all biological functions in the human body in a sophisticated manner, and abnormalities in this network can lead to disease and even cancer. The construction of a high-quality human molecular interaction network is possible with the development of experimental techniques that facilitate the interpretation of the mechanisms of drug treatment for cancer. We collected 11 molecular interaction databases based on experimental sources and constructed a human protein-protein interaction (PPI) network and a human transcriptional regulatory network (HTRN). A random walk-based graph embedding method was used to calculate the diffusion profiles of drugs and cancers, and a pipeline was constructed by using five similarity comparison metrics combined with a rank aggregation algorithm, which can be implemented for drug screening and biomarker gene prediction. Taking NSCLC as an example, curcumin was identified as a potentially promising anticancer drug from 5450 natural small molecules, and combined with differentially expressed genes, survival analysis, and topological ranking, we obtained BIRC5 (survivin), which is both a biomarker for NSCLC and a key target for curcumin. Finally, the binding mode of curcumin and survivin was explored using molecular docking. This work has a guiding significance for antitumor drug screening and the identification of tumor markers.

Understanding allostery in purine nucleoside phosphorylases by machine learning and molecular dynamics interaction databases

Understanding allostery in purine nucleoside phosphorylases by machine learning and molecular dynamics interaction databases

The Intricacy of the Viral-Human Protein Interaction Networks: Resources, Data, and Analyses.

Viral infections are one of the major causes of human diseases that cause yearly millions of deaths and seriously threaten global health, as we have experienced with the COVID-19 pandemic. Numerous approaches have been adopted to understand viral diseases and develop pharmacological treatments. Among them, the study of virus-host protein-protein interactions is a powerful strategy to comprehend the molecular mechanisms employed by the virus to infect the host cells and to interact with their components. Experimental protein-protein interactions described in the scientific literature have been systematically captured into several molecular interaction databases. These data are organized in structured formats and can be easily downloaded by users to perform further bioinformatic and network studies. Network analysis of available virus-host interactomes allow us to understand how the host interactome is perturbed upon viral infection and what are the key host proteins targeted by the virus and the main cellular pathways that are subverted. In this review, we give an overview of publicly available viral-human protein-protein interactions resources and the community standards, curation rules and adopted ontologies. A description of the main virus-human interactome available is provided, together with the main network analyses that have been performed. We finally discuss the main limitations and future challenges to assess the quality and reliability of protein-protein interaction datasets and resources.

Serine hydroxymethyltransferase 2 (SHMT2) potentiates the aggressive process of oral squamous cell carcinoma by binding to interleukin enhancer-binding factor 2 (ILF2)

ABSTRACT Oral squamous cell carcinoma (OSCC) is a frequent threatening head and neck malignancy. Serine hydroxymethyltransferase 2 (SHMT2) was identified to be upregulated in OSCC and its high expression was associated with poor patient prognosis. This paper set out to assess the influence of SHMT2 on OSCC progression and the potential mechanisms related to interleukin enhancer-binding factor 2 (ILF2). First of all, reverse transcription-quantitative PCR (RT-qPCR) and western blot examined the expression of SHMT2 and ILF2 in OSCC cells. Cell Counting Kit-8 (CCK-8) and colony formation assays appraised cell proliferation. Terminal-deoxynucleotidyl Transferase Mediated Nick End Labeling (TUNEL) staining was to estimate the apoptotic rate of cells. Further, wound healing and transwell assays verified the migration and invasion of cells. Western blot was adopted to detect the expression of factors related to apoptosis, migration, and epithelial–mesenchymal transition (EMT). The possible interaction of SHMT2 and ILF2 was predicted by a Molecular INTeraction (MINT) and BioGRID databases and determined using co-immunoprecipitation (IP) assay. Subsequently, ILF2 was overexpressed to investigate whether SHMT2 regulated OSCC progression by binding to ILF2. Results implied that SHMT2 possessed increased expression in OSCC cells, and OSCC cell viability, migration, invasion, EMT were inhibited and apoptosis was potentiated after its silencing. ILF2 bound to SHMT2 and ILF2 expression was downregulated after SHMT2 silencing in OSCC cells. Importantly, ILF2 overexpression abolished the suppressive role of SHMT2 interference in the progression of OSCC. Collectively, SHMT2 could promote the progression of OSCC by binding to ILF2.

Reconstruction of human protein-coding gene functional association network based on machine learning.

Networks consisting of molecular interactions are intrinsically dynamical systems of an organism. These interactions curated in molecular interaction databases are still not complete and contain false positives introduced by high-throughput screening experiments. In this study, we propose a framework to integrate interactions of functional associated protein-coding genes from 31 data sources to reconstruct a network with high coverage and quality. For each interaction, 369 features were constructed including properties of both the interaction and the involved genes. The training and validation sets were built on the pathway interactions as positives and the potential negative instances resulting from our proposed semi-supervised strategy. Random forest classification method was then applied to train and predict multiple times to give a score for each interaction. After setting a threshold estimated by a Binomial distribution, a Human protein-coding Gene Functional Association Network (HuGFAN) was reconstructed with 20 383 genes and 1185 429 high confidence interactions. Then, HuGFAN was compared with other networks from data sources with respect to network properties, suggesting that HuGFAN is more function and pathway related. Finally, HuGFAN was applied to identify cancer driver through two famous network-based methods (DriverNet and HotNet2) to show its outstanding performance compared with other networks. HuGFAN and other supplementary files are freely available at https://github.com/xthuang226/HuGFAN.

IMEx Databases: Displaying Molecular Interactions into a Single, Standards-Compliant Dataset.

Molecular interaction databases aim to systematically capture and organize the experimental interaction information described in the scientific literature. These data can then be used to perform network analysis, to assign putative roles to uncharacterized proteins and to investigate their involvement in cellular pathways.This chapter gives a brief overview of publicly available molecular interaction databases and focuses on the members of the IMEx Consortium, on their curation policies and standard data formats. All of the goals achieved by IMEx databases over the last 15years, the data types provided and the many different ways in which such data can be utilized by the research community, are described in detail. The IMEx databases curate molecular interaction data to the highest caliber, following a detailed curation model and supplying rich metadata by employing common curation rules and harmonized standards. The IMEx Consortium provides comprehensively annotated molecular interaction data integrated into a single, non-redundant, open access dataset.

Network Analysis of Dengue NS1 Interacting Core Human Proteins Driving Dengue Pathogenesis

Aim: We aimed to identify critical human proteins involved in cathepsin L regulation Background: It has been shown that Dengue Virus (DENV) NS1 activates cathepsin L (CTSL). The CTSL activates heparanase, which cleaves heparan sulfate proteoglycans and causes dengue pathogenesis. NS1 directly interacts with PTBP1 and Gab proteins. Gab protein activates the Ras signaling pathway. Still, no known direct interaction partners are linking GAB1 to cathepsin L. Objective: Our objective includes three main points.1-Network analysis of NS1 interacting human proteins 2- Identification of protein-drug and protein-disease interactions 3- Identification of core proteins involved in cathepsin L regulation. Method: We collected NS1 interacting Human proteins from DenHunt, Int-Act Molecular Interaction Database, Virus Mentha, Virus Pathogen Database and Analysis Resource (ViPR), and Virus MINT. We employed Pesca, cytohubba, and centiscape as the significant plug-ins in Cytoscape for network analysis. To study protein-diseases and protein-drugs interaction, we used NetworkAnalyst. Result: Based on the prior knowledge on the interaction of NS1 with GAB1 and PTBP1 human proteins, we found several core proteins that drive dengue pathogenesis. The proteins EED, NXF1, and MOV10, are the mediators between PTBP1 and CTSL. Similarly, DNM2, GRB2, PXN, PTPRC, and NTRK1 mediate GAB1 and PTBP1. The common first neighbors of MOV10, NXF1, and EED were identified, and the common primary pathways in all three subnetworks were mRNA processing and protein translation. The common interaction partners were considered for drug and disease network analysis. These proteins were; PARP1, NFKB2, HDAC2, SLC25A4, ATP5A1, EPN1, CTSL, UBR4, CLK3, and ARPC4. PARP1 was the highly connected node in the protein-drug network. The highest degree protein, LMNA, was associated with many diseases. The NXF1 is connected with LMNA. Here, we reported one essential protein, namely, NXF1 protein, which links PTBP1 with CTSL. The NXF1 is also connected with TPM3, which is connected to CTSL. Conclusion: We listed functionally important proteins which are involved in cathepsin L activation. Based on network properties, we proposed, NXF1 and TPM3 are the important high centrality proteins in dengue infection.

The IntAct database: efficient access to fine-grained molecular interaction data.

The IntAct molecular interaction database (https://www.ebi.ac.uk/intact) is a curated resource of molecular interactions, derived from the scientific literature and from direct data depositions. As of August 2021, IntAct provides more than one million binary interactions, curated by twelve global partners of the International Molecular Exchange consortium, for which the IntAct database provides a shared curation and dissemination platform. The IMEx curation policy has always emphasised a fine-grained data and curation model, aiming to capture the relevant experimental detail essential for the interpretation of the provided molecular interaction data. Here, we present recent curation focus and progress, as well as a completely redeveloped website which presents IntAct data in a much more user-friendly and detailed way.

Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts.

For any molecule, network, or process of interest, keeping up with new publications on these is becoming increasingly difficult. For many cellular processes, the amount molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large-scale molecular interaction maps and database curation. Text mining and Natural-Language-Processing (NLP)-based techniques are finding their applications in mining the biological literature, handling problems such as Named Entity Recognition (NER) and Relationship Extraction (RE). Both rule-based and Machine-Learning (ML)-based NLP approaches have been popular in this context, with multiple research and review articles examining the scope of such models in Biological Literature Mining (BLM). In this review article, we explore self-attention-based models, a special type of Neural-Network (NN)-based architecture that has recently revitalized the field of NLP, applied to biological texts. We cover self-attention models operating either at the sentence level or an abstract level, in the context of molecular interaction extraction, published from 2019 onwards. We conducted a comparative study of the models in terms of their architecture. Moreover, we also discuss some limitations in the field of BLM that identifies opportunities for the extraction of molecular interactions from biological text.

Integration of transcription coregulator complexes with sequence-specific DNA-binding factor interactomes

The domain of transcription regulation has been notoriously difficult to annotate in the Gene Ontology, partly because of the intricacies of gene regulation which involve molecular interactions with DNA as well as amongst protein complexes. The molecular function ‘transcription coregulator activity’ is a part of the biological process ‘regulation of transcription, DNA-templated’ that occurs in the cellular component ‘chromatin’. It can mechanistically link sequence-specific DNA-binding transcription factor (dbTF) regulatory DNA target sites to coactivator and corepressor target sites through the molecular function ‘cis-regulatory region sequence-specific DNA binding’. Many questions arise about transcription coregulators (coTF). Here, we asked how many unannotated, putative coregulators can be identified in protein complexes? Therefore, we mined the CORUM and hu.MAP protein complex databases with known and strongly presumed human transcription coregulators. In addition, we trawled the BioGRID and IntAct molecular interaction databases for interactors of the known 1457 human dbTFs annotated by the GREEKC and GO consortia. This yielded 1093 putative transcription factor coregulator complex subunits, of which 954 interact directly with a dbTF. This substantially expands the set of coTFs that could be annotated to ‘transcription coregulator activity’ and sets the stage for renewed annotation and wet-lab research efforts. To this end, we devised a prioritisation score based on existing GO annotations of already curated transcription coregulators as well as interactome representation. Since all the proteins that we mined are parts of protein complexes, we propose to concomitantly engage in annotation of the putative transcription coregulator-containing complexes in the Complex Portal database.

Improved Prediction of Protein-Protein Interaction Mapping on Homo Sapiens by Using Amino Acid Sequence Features in a Supervised Learning Framework.

Protein-Protein Interaction (PPI) has emerged as a key role in the control of many biological processes including protein function, disease incidence, and therapy design. However, the identification of PPI by wet lab experiment is a challenging task, since it is laborious, time consuming and expensive. Therefore, computational prediction of PPI is now given emphasis before going to the experimental validation, since it is simultaneously less laborious, time saver and cost minimizer. The objective of this study is to develop an improved computational method for PPI prediction mapping on Homo sapiens by using the amino acid sequence features in a supervised learning framework. The experimentally validated 91 positive-PPI pairs of human protein sequences were collected from IntAct Molecular Interaction Database. Then we constructed three balanced datasets with ratios 1:1, 1:2 and 1:3 of positive and negative PPI samples. Then we partitioned each dataset into training (80%) and independent test (20%) datasets. Again each training dataset was partitioned into four mutually exclusive groups of equal sizes for interchanging each group with independent test group to perform 5-fold cross validation (CV). Then we trained candidate seven classifiers (NN, SVM, LR, NB, KNN, AB and RF) with each ratio case to obtain the better PPI predictor by comparing their performance scores. The random forest (RF) based predictor that was trained with 1:2 ratio of positive-PPI and negative-PPI samples based on AAC encoding features provided the most accurate PPI prediction by producing the highest average performance scores of accuracy (93.50%), sensitivity (95.0%), MCC (85.2%), AUC (0.941) and pAUC (0.236) with the 5-fold cross-validation. It also achieved the highest average performance scores of accuracy (92.0%), sensitivity (94.0%), MCC (83.6%), AUC (0.922) and pAUC (0.207) with the independent test datasets in a comparison of the other candidate and existing predictors. The final resultant prediction strongly recommend that the RF based predictor is a better prediction model of PPI mapping on Homo sapiens.

Using the MINT Database to Search Protein Interactions

The Molecular INTeractions Database (MINT) is a public database designed to store information about protein interactions. Protein interactions are extracted from scientific literature and annotated in the database by expert curators. Currently (October 2019), MINT contains information on more than 26,000 proteins and more than 131,600 interactions in over 30 model organisms. This article provides protocols for searching MINT over the Internet, using the new MINT Web Page. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Searching MINT over the internet Alternate Protocol: MINT visualizer Basic Protocol 2: Submitting interaction data.

The identification and preliminary study of lncRNA TUG1 and its related genes in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common malignant tumour of the digestive system, which is a threat to public health. The purpose of this study was to investigate the featured genes and pathways of HCC from a bioinformatics database, and verify their correlation with diagnosis and prognosis of HCC. We downloaded the gene expression profile on HCC from the Gene Expression Omnibus (GEO), and software R was used to identify differentially expressed lncRNA (DEL). The target genes of the lncRNA were further predicted by using a cluster database and molecular interaction database. Subsequently, a combined interaction network of target genes was constructed using the Cytoscape platform with preliminary verification at the level of different databases, cell lines, and tissues. Finally, we explored the effectiveness of TUG1 and its target genes on the diagnosis and prognosis of HCC by univariate Cox analysis and survival analysis. A total of four DELs were identified and the most remarkably up-regulated lncRNA was TUG1, which included 12 high-confidence target genes. Moreover, we found that the expression changes of TUG1 and its target genes in different databases, cell lines, and liver cancer tissues were consistent with the prediction. The high expression of TUG1 and its target genes could significantly predict the shorter survival time of HCC patients, among which NCAPG, MCM6, PIGC, PEA15, and RACGAP1 have significant diagnostic value for HCC (AUC > 0.9). This study provides a starting point for the screening of therapeutically relevant targets in HCC. Further experiment should be conducted to verify our findings.

The IUPHAR/BPS Guide to PHARMACOLOGY in 2020: extending immunopharmacology content and introducing the IUPHAR/MMV Guide to MALARIA PHARMACOLOGY.

The IUPHAR/BPS Guide to PHARMACOLOGY (www.guidetopharmacology.org) is an open-access, expert-curated database of molecular interactions between ligands and their targets. We describe significant updates made over the seven releases during the last two years. The database is notably enhanced through the continued linking of relevant pharmacology with key immunological data types as part of the IUPHAR Guide to IMMUNOPHARMACOLOGY (www.guidetoimmunopharmacology.org) and by a major new extension, the IUPHAR/MMV Guide to Malaria PHARMACOLOGY (www.guidetomalariapharmacology.org). The latter has been constructed in partnership with the Medicines for Malaria Venture, an organization dedicated to identifying, developing and delivering new antimalarial therapies that are both effective and affordable. This is in response to the global challenge of over 200 million cases of malaria and 400 000 deaths worldwide, with the majority in the WHO Africa Region. It provides new pharmacological content, including molecular targets in the malaria parasite, interaction data for ligands with antimalarial activity, and establishes curation of data from screening assays, used routinely in antimalarial drug discovery, against the whole organism. A dedicated portal has been developed to provide quick and focused access to these new data.

Screening of FDA approved drugs for finding potential inhibitors against Granzyme B as a potent drug-repurposing target

Granzyme B is one of the best-characterized and extensively studied member of cytotoxic lymphocytes (CL) proteases. Initially, it is thought to be involved in eliminating virally infected or cancerous cells by using a specialized mechanism through which they are internalized into target cells. In the last decade, however this dimension has changed as there are several reports show that not only CL but also other immune cells can also synthesize Granzyme B. This leads to the possibility of the presence of these proteases in extracellular environment. Being active protease, it then raises the possibility of damaging host tissues as evident from the available reported literature. In many instances, Granzyme B is directly involved in pathogenicity, however in others, it contributes to the disease severity as their over expression makes the clinical situation quite worse which ultimately leads to the chronic state of the disease. Serine protease inhibitor-9 is a natural known intracellular inhibitor of Granzyme B, however there is less data available about the potential inhibitors that can regulate its activity in an extracellular environment. Current study is an effort to identify potential novel inhibitors of Granzyme B. For this aim, drug repurposing study was performed. Around 7900 FDA approved drugs were screened using both ligand- and target-driven approaches. Initially, all molecules were docked using induced fit docking (IFD) approach and selected 318 high-docking scored molecules were used in short (1-ns) molecular dynamics (MD) simulations. Based on MM/GBSA binding free energy calculations, 6 compounds were selected and used in long (100-ns) MD simulations. These compounds were then used in binary QSAR analysis. Therapeutic activity potentials of studied compounds were investigated by Clarivate Analytics’s MetaCore/MetaDrug platform which uses binary QSAR models. It is developed based on manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism and toxicity information. Results of selected compounds were compared with a positive control molecule. Current drug repurposing study is a step ahead in finding potential lead compounds by screening database of FDA approved molecules. We have identified novel inhibitors (Tannic acid, Mupirocin, Phytonadiol sodium diphosphate, Cefpiramide, Xenazoic acid) that have potential to decrease the activity of Granzyme B.

Protein-Protein Interactions in Candida albicans.

Despite being one of the most important human fungal pathogens, Candida albicans has not been studied extensively at the level of protein-protein interactions (PPIs) and data on PPIs are not readily available in online databases. In January 2018, the database called “Biological General Repository for Interaction Datasets (BioGRID)” that contains the most PPIs for C. albicans, only documented 188 physical or direct PPIs (release 3.4.156) while several more can be found in the literature. Other databases such as the String database, the Molecular INTeraction Database (MINT), and the Database for Interacting Proteins (DIP) database contain even fewer interactions or do not even include C. albicans as a searchable term. Because of the non-canonical codon usage of C. albicans where CUG is translated as serine rather than leucine, it is often problematic to use the yeast two-hybrid system in Saccharomyces cerevisiae to study C. albicans PPIs. However, studying PPIs is crucial to gain a thorough understanding of the function of proteins, biological processes and pathways. PPIs can also be potential drug targets. To aid in creating PPI networks and updating the BioGRID, we performed an exhaustive literature search in order to provide, in an accessible format, a more extensive list of known PPIs in C. albicans.

MorCVD: A Unified Database for Host-Pathogen Protein-Protein Interactions of Cardiovascular Diseases Related to Microbes

Microbe induced cardiovascular diseases (CVDs) are less studied at present. Host-pathogen interactions (HPIs) between human proteins and microbial proteins associated with CVD can be found dispersed in existing molecular interaction databases. MorCVD database is a curated resource that combines 23,377 protein interactions between human host and 432 unique pathogens involved in CVDs in a single intuitive web application. It covers endocarditis, myocarditis, pericarditis and 16 other microbe induced CVDs. The HPI information has been compiled, curated, and presented in a freely accessible web interface (http://morcvd.sblab-nsit.net/About). Apart from organization, enrichment of the HPI data was done by adding hyperlinked protein ID, PubMed, gene ontology records. For each protein in the database, drug target and interactors (same as well as different species) information has been provided. The database can be searched by disease, protein ID, pathogen name or interaction detection method. Interactions detected by more than one method can also be listed. The information can be presented in tabular form or downloaded. A comprehensive help file has been developed to explain the various options available. Hence, MorCVD acts as a unified resource for retrieval of HPI data for researchers in CVD and microbiology.