HumanPSD

 

The Human Proteome Survey Database (HumanPSDTM) is a catalog of proteins and their complexes from human cells, plus their orthologs from mouse and rat sources.
Its main focus is on the association of human proteins with diseases as well as on their potential use as biomarkers.

Drugs targeting human proteins are reported. In addition, information can be retrieved on the molecular functions, biological roles, localization, and modifications of proteins, expression patterns across cells, tissues, organs, and tumors, consequences of gene mutations in mice, and the physical and regulatory interactions between proteins and genes. The TRANSPATH® database on signal transduction and metabolic pathways is an integral part of HumanPSDTM.

Picture of HumanPSD

TP53_HumanPSD
Contents of a standard locus report of HumanPSD

Introduction
Description
Synonyms
Biomarker Associations
Diseases associated with MYC
Inherit MYC mutations
Drug Interactions
Drug(s) targeting MYC
Gene Ontology
Molecular function
Biological process
Cellular component
Expression
Tissue expression

Regulation of MYC expression

Mutant Phenotype

Mutant phenotype of closely related homolog(s)

Pathways & interactions
Pathways
Protein-protein interactions
Events acting on MYC
Events triggered by MYC
Transcriptional Regulation
Add a subscription to TRANSFAC® and this report will display additional information
RNA Features
Overview of RNA sequence
Protein Features
Overview of protein sequence and structure
Post-translational modifications of MYC protein
View complex containing MYC protein
Identifiers
Accessions mapped to this record
Annotations
Description
Editor’s Notes
Disease related

Biomarker / disease associations

A tabular summary of literature-derived relationships between human genes and gene products with human diseases is given.
These associations are clearly sorted according to their type, e.g. whether a gene/protein has a causal relationship with a disease to develop, or whether it is merely correlative, etc.

table of diseases associated with MYC

Tabulated summary of disease associations of the human MYC gene.

Key features

Reports
About more than 53,000 proteins and 5000 microRNAs.
Gene-disease assignments
More than 136,000 extracted from original scientific literature and evaluated by experts, referring to more than 3,900 diseases (human) or disease models (mouse).
Drug-protein interactions
More than 52,000 and referring to more than 9,200 drugs.
Gene Ontology (GO)
More than 603,000 assignments to GO, manually annotated and quality-checked.
Gene expression
More than 2,403,000  assignments.
Annotations
More than 2,343,000 annotation statements given.
Peer-reviewed publications
More than 437,000 references to scientific publications provided.
Ontology Browser
An integrated tool supports easy selection of defined sets of gene/molecules.
Functional Analysis
A tool allows identification of shared characteristics in a set of genes/proteins or miRNAs.
TRANSPATH included!
Find out more.

Benefits

Quickly access detailed reports
For individual genes, proteins, miRNAs, diseases, and drugs without time-consuming literature search.
Uncover biologically relevant connections
Between seemingly disparate genes, diseases, and drugs.
Identify and rank potential therapeutic targets
Based on known functional characteristics.
Explore canonical pathways and build custom protein networks
Overlaying known disease and drug associations.

Videos

Drug report and pathway report in HumanPSD+TRANSPATH® – this video demonstrates how drug report looks like in the online interface of HumanPSD+TRANSPATH® database. Target molecules, corresponding to a particular drug, are demonstrated, and pathways, in which these molecules are involved, are shown in a very detailed way, with all the underlying reactions, from which the corresponding pathways are constructed. Clinical trials info is also shown for each particular drug and for certain cases even metabolizing enzymes and associated pathways info is also available.

Disease report overview in HumanPSD+TRANSPATH®this video demonstrates how disease report looks like in the online interface of HumanPSD+TRANSPATH® database. Disease biomarkers are shown (genes or proteins or miRNAs) with the type of association and type of indication info. All respective references are demonstrated. Disease similarity maps are also shown (these maps are based on the similarity of different diseases in respect to the common biomarkers that they share).

Disease similarity maps by common biomarkers in HumanPSD+TRANSPATH® – this video shows the overview of disease similarity maps. Common biomarkers of different diseases are displayed on these schemas. The searched disease is placed in the center of the map. The maps have the following color code: diseases sharing common biomarkers are connected with red lines; diseases that have ontological relationships according to the MeSH ontology are connected by gray lines.

 

Recent applications

Selection of articles reporting about HumanPSD applications:
  • Kawashima Y., Nagai H., Konno R., Ishikawa M., Nakajima D., Sato H., Nakamura R., Furuyashiki T., Ohara O. (2022) Single-Shot 10K Proteome Approach: Over 10,000 Protein Identifications by Data-Independent Acquisition-Based Single-Shot Proteomics with Ion Mobility Spectrometry. J Proteome Res. 21(6), 1418–1427. Link
  • Lim, J. S., Ibaseta, A., Fischer, M. M., Cancilla, B., O’Young, G., Cristea, S., Luca, V. C., Yang, D., Jahchan, N. S., Hamard, C., Antoine, M., Wislez, M., Kong, C., Cain, J., Liu, Y. W., Kapoun, A. M., Garcia, K. C., Hoey, T., Murriel, C. L., & Sage, J. (2017). Intratumoural heterogeneity generated by Notch signalling promotes small-cell lung cancer. Nature, 545(7654), 360–364. Link
  • Reales‐Calderón, J. A., Aguilera‐Montilla, N., Corbí, Á. L., Molero, G., & Gil, C. (2014). Proteomic characterization of human proinflammatory M1 and anti‐inflammatory M2 macrophages and their response to Candida albicans. Proteomics, 14(12), 1503-1518. Link
  • Martínez‐Solano, L., Nombela, C., Molero, G., & Gil, C. (2006). Differential protein expression of murine macrophages upon interaction with Candida albicans. Proteomics, 6(S1), S133-S144. Link

Publications

Selection of publications authored by the geneXplain team:
  • Stelmashenko, D., Kel-Margoulis, O., Apalko, S., & Kel, A. (2022) GENE NETWORKS AND DRUGS. WHAT CAN WE LEARN USING BIO-AND CHEMOINFORMATICS? In XXXVIII Symposium of Bioinformatics and Computer-Aided Drug Discovery (pp. 21-21).
  • Kalya, M., Kel, A., Leha, A., Altynbekova, K., Wingender, E., Beißbarth, T. (2022). Machine Learning based Survival Group Prediction in Glioblastoma. Preprints 2022, 2022020051. Link

Get HumanPSD

Current HumanPSD™ release

HumanPSD™ release 2022.2

The Human Proteome Survey Database (HumanPSDTM) with focus on human proteins as disease biomarkers and drug targets contains these new features:

  • Biomarker and drug target data update

The number of disease annotations increased to 378,522 and the number of unique gene/biomarker – disease assignments to 129,784. Targets for FDA – approved drugs have been added by manual curation and the number of drug – target protein associations is now 53,034.

          Price request HumanPSD

          Request your evaluation package here.

          Reports

          The basic information unit is a “locus report”, which summarizes the existing knowledge about the product(s) of a gene. It is part of a hierarchy, with individual proteins (isoforms such as splice variants) encoded by a gene at a level under the locus report, and summarizing features of the orthologs of human, mouse and rat origin at a higher level.

          Information downloads

          HumanPSDTM Statistics (download)
          HumanPSDTM Features (download)
          HumanPSD FlyerTM (download)

          Videos

          Publications

          Stegmaier, P., Krull, M., Voss, N., Kel, A.E., Wingender, E. (2010) Molecular mechanistic associations of human diseases. BMC Syst Biol. 4, 124. doi: 10.1186/1752-0509-4-124. PubMed.

          Michael, H., Hogan, J., Kel, A., Kel-Margoulis, O., Schacherer, F., Voss, N., Wingender, E. (2008) Building a knowledge base for systems pathology. Brief. Bioinform. 9, 518-531. doi: 10.1093/bib/bbn038. PubMed.

          Wingender, E., Crass, T., Hogan, J.D., Kel, A.E., Kel-Margoulis, O.V., Potapov, A.P. (2007) Integrative content-driven concepts for bioinformatics “beyond the cell”. J Biosci. 32, 169-180. PubMed.

          Hodges PE, Carrico, P.M., Hogan, J.D., O’Neill, K.E., Owen, J.J., Mangan, M., Davis, B.P., Brooks, J.E., Garrels, J.I. (2002) Annotating the human proteome: the Human Proteome Survey Database (HumanPSD) and an in-depth target database for G protein-coupled receptors (GPCR-PD) from Incyte Genomics. Nucleic Acids Res. 30:137-141. PubMed.

          HumanPSD is a trademark of QIAGEN GmbH.

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