KinaMetrix


30321373	KinaMetrix: a web resource to investigate kinase conformations and inhibitor space. [PMID: 30321373] Rahman R, Ung PM, Schlessinger A. Abstract Protein kinases are among the most explored protein drug targets. Visualization of kinase conformations is critical for understanding structure-function relationship in this family and for developing chemically unique, conformation-specific small molecule drugs. We have developed Kinformation, a random forest classifier that annotates the conformation of over 3500 protein kinase structures in the Protein Data Bank. Kinformation was trained on structural descriptors derived from functionally important motifs to automatically categorize kinases into five major conformations with pharmacological relevance. Here we present KinaMetrix (http://KinaMetrix.com), a web resource enabling researchers to investigate the protein kinase conformational space as well as a subset of kinase inhibitors that exhibit conformational specificity. KinaMetrix allows users to classify uploaded kinase structures, as well as to derive structural descriptors of protein kinases. Uploaded structures can then be compared to atomic structures of other kinases, enabling users to identify kinases that occupy a similar conformational space to their uploaded structure. Finally, KinaMetrix also serves as a repository for both small molecule substructures that are significantly associated with each conformation type, and for homology models of kinases in inactive conformations. We expect KinaMetrix to serve as a resource for researchers studying kinase structural biology or developing conformation-specific kinase inhibitors. Nucleic Acids Res. 2019:47(D1) \| 22 Citations (from Europe PMC, 2025-03-29)

KinaMetrix: a web resource to investigate kinase conformations and inhibitor space. [PMID: 30321373]

Rahman R, Ung PM, Schlessinger A.

Protein kinases are among the most explored protein drug targets. Visualization of kinase conformations is critical for understanding structure-function relationship in this family and for developing chemically unique, conformation-specific small molecule drugs. We have developed Kinformation, a random forest classifier that annotates the conformation of over 3500 protein kinase structures in the Protein Data Bank. Kinformation was trained on structural descriptors derived from functionally important motifs to automatically categorize kinases into five major conformations with pharmacological relevance. Here we present KinaMetrix (http://KinaMetrix.com), a web resource enabling researchers to investigate the protein kinase conformational space as well as a subset of kinase inhibitors that exhibit conformational specificity. KinaMetrix allows users to classify uploaded kinase structures, as well as to derive structural descriptors of protein kinases. Uploaded structures can then be compared to atomic structures of other kinases, enabling users to identify kinases that occupy a similar conformational space to their uploaded structure. Finally, KinaMetrix also serves as a repository for both small molecule substructures that are significantly associated with each conformation type, and for homology models of kinases in inactive conformations. We expect KinaMetrix to serve as a resource for researchers studying kinase structural biology or developing conformation-specific kinase inhibitors.

Nucleic Acids Res. 2019:47(D1) | 22 Citations (from Europe PMC, 2025-03-29)

URL:	http://KinaMetrix.com
Full name:
Description:	a web resource enabling researchers to investigate the protein kinase conformational space as well as a subset of kinase inhibitors that exhibit conformational specificity.
Year founded:	2019
Last update:
Version:
Accessibility:	Accessible
Country/Region:	United States

Data type:	Protein
Data object:	Animal Plant
Database category:	Structure
Major species:	Rattus norvegicus Homo sapiens Mus musculus Sus scrofa Schizosaccharomyces pombe Zea mays Arabidopsis thaliana Toxoplasma gondii
Keywords:	kinase inhibitors protein kinase

University/Institution:	Icahn school of Medicine at Mount Sinai
Address:	Department of Pharmacological Sciences, Icahn school of Medicine at Mount Sinai, New York, NY 10029, USA.
City:
Province/State:
Country/Region:	United States
Contact name (PI/Team):	Rayees Rahman
Contact email (PI/Helpdesk):	rayees.rahman@icahn.mssm.edu

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Database Commons a catalog of worldwide biological databases