Incorporating substrate sequence motifs and spatial amino acid composition to identify kinase-specific phosphorylation sites on protein three-dimensional structures

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1471-2105

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Incorporating substrate sequence motifs and spatial amino acid composition to identify kinase-specific phosphorylation sites on protein three-dimensional structures ; volume:14 ; number:16 ; day:22 ; month:10 ; year:2013 ; pages:1-11 ; date:10.2013
BMC bioinformatics ; 14, Heft 16 (22.10.2013), 1-11, 10.2013

Creator: Su, Min-Gang

Contributor: Lee, Tzong-Yi
SpringerLink (Online service)

DOI: 10.1186/1471-2105-14-S16-S2

URN: urn:nbn:de:1111-2016021742971

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:26 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Su, Min-Gang
Lee, Tzong-Yi
SpringerLink (Online service)

Other Objects (12)

PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity

Computational approaches to identify sites of phosphorylation

Incorporating molecular flexibility into three-dimensional structural kernels

The Effect of 2-Ketobutyrate on Mitochondrial Substrate-Level Phosphorylation

Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

Acetylation of insulin receptor substrate-1 is permissive for tyrosine phosphorylation

Phosphorylation of the compartmentalized PKA substrate TAF15 regulates RNA–protein interactions

Identification of lipocalin-2 as a PKCδ phosphorylation substrate in neutrophils

Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

ViralPhos: incorporating a recursively statistical method to predict phosphorylation sites on virus proteins

A network-based approach to identify substrate classes of bacterial glycosyltransferases

Dopamine transporter phosphorylation site threonine 53 regulates substrate reuptake and amphetamine-stimulated efflux

PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity

Computational approaches to identify sites of phosphorylation

Incorporating molecular flexibility into three-dimensional structural kernels

The Effect of 2-Ketobutyrate on Mitochondrial Substrate-Level Phosphorylation

Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

Acetylation of insulin receptor substrate-1 is permissive for tyrosine phosphorylation

Phosphorylation of the compartmentalized PKA substrate TAF15 regulates RNA–protein interactions

Identification of lipocalin-2 as a PKCδ phosphorylation substrate in neutrophils

Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

ViralPhos: incorporating a recursively statistical method to predict phosphorylation sites on virus proteins

A network-based approach to identify substrate classes of bacterial glycosyltransferases

Dopamine transporter phosphorylation site threonine 53 regulates substrate reuptake and amphetamine-stimulated efflux

PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity

Computational approaches to identify sites of phosphorylation

Incorporating molecular flexibility into three-dimensional structural kernels

The Effect of 2-Ketobutyrate on Mitochondrial Substrate-Level Phosphorylation

Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

Acetylation of insulin receptor substrate-1 is permissive for tyrosine phosphorylation

Phosphorylation of the compartmentalized PKA substrate TAF15 regulates RNA–protein interactions

Identification of lipocalin-2 as a PKCδ phosphorylation substrate in neutrophils

Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

ViralPhos: incorporating a recursively statistical method to predict phosphorylation sites on virus proteins

A network-based approach to identify substrate classes of bacterial glycosyltransferases

Dopamine transporter phosphorylation site threonine 53 regulates substrate reuptake and amphetamine-stimulated efflux

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Incorporating substrate sequence motifs and spatial amino acid composition to identify kinase-specific phosphorylation sites on protein three-dimensional structures

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity

Computational approaches to identify sites of phosphorylation

Incorporating molecular flexibility into three-dimensional structural kernels

The Effect of 2-Ketobutyrate on Mitochondrial Substrate-Level Phosphorylation

Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

Acetylation of insulin receptor substrate-1 is permissive for tyrosine phosphorylation

Phosphorylation of the compartmentalized PKA substrate TAF15 regulates RNA–protein interactions

Identification of lipocalin-2 as a PKCδ phosphorylation substrate in neutrophils

Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

ViralPhos: incorporating a recursively statistical method to predict phosphorylation sites on virus proteins

A network-based approach to identify substrate classes of bacterial glycosyltransferases

Dopamine transporter phosphorylation site threonine 53 regulates substrate reuptake and amphetamine-stimulated efflux

PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity

Computational approaches to identify sites of phosphorylation

Incorporating molecular flexibility into three-dimensional structural kernels

The Effect of 2-Ketobutyrate on Mitochondrial Substrate-Level Phosphorylation

Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

Acetylation of insulin receptor substrate-1 is permissive for tyrosine phosphorylation

Phosphorylation of the compartmentalized PKA substrate TAF15 regulates RNA–protein interactions

Identification of lipocalin-2 as a PKCδ phosphorylation substrate in neutrophils

Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

ViralPhos: incorporating a recursively statistical method to predict phosphorylation sites on virus proteins

A network-based approach to identify substrate classes of bacterial glycosyltransferases

Dopamine transporter phosphorylation site threonine 53 regulates substrate reuptake and amphetamine-stimulated efflux

PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity

Computational approaches to identify sites of phosphorylation

Incorporating molecular flexibility into three-dimensional structural kernels

The Effect of 2-Ketobutyrate on Mitochondrial Substrate-Level Phosphorylation

Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

Acetylation of insulin receptor substrate-1 is permissive for tyrosine phosphorylation

Phosphorylation of the compartmentalized PKA substrate TAF15 regulates RNA–protein interactions

Identification of lipocalin-2 as a PKCδ phosphorylation substrate in neutrophils

Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

ViralPhos: incorporating a recursively statistical method to predict phosphorylation sites on virus proteins

A network-based approach to identify substrate classes of bacterial glycosyltransferases

Dopamine transporter phosphorylation site threonine 53 regulates substrate reuptake and amphetamine-stimulated efflux

Related objects

Reset password