Prediction of DNA-Binding Proteins from Relational Features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00201128" target="_blank" >RIV/68407700:21230/12:00201128 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.proteomesci.com/content/pdf/1477-5956-10-66.pdf" target="_blank" >http://www.proteomesci.com/content/pdf/1477-5956-10-66.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/1477-5956-10-66" target="_blank" >10.1186/1477-5956-10-66</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Prediction of DNA-Binding Proteins from Relational Features

Popis výsledku v původním jazyce

The process of protein-DNA binding has an essential role in the biological processing of genetic information. We use relational machine learning to predict DNA-binding propensity of proteins from their structures. Automatically discovered structural features are able to capture some characteristic spatial configurations of amino acids in proteins. Prediction based only on structural relational features already achieves competitive results to existing methods based on physicochemical properties on several protein datasets. Predictive performance is further improved when structural features are combined with physicochemical features. Moreover, the structural features provide some insights not revealed by physicochemical features. Our method is able to detect common spatial substructures. We demonstrate this in experiments with zinc finger proteins.

Název v anglickém jazyce

Prediction of DNA-Binding Proteins from Relational Features

Popis výsledku anglicky

The process of protein-DNA binding has an essential role in the biological processing of genetic information. We use relational machine learning to predict DNA-binding propensity of proteins from their structures. Automatically discovered structural features are able to capture some characteristic spatial configurations of amino acids in proteins. Prediction based only on structural relational features already achieves competitive results to existing methods based on physicochemical properties on several protein datasets. Predictive performance is further improved when structural features are combined with physicochemical features. Moreover, the structural features provide some insights not revealed by physicochemical features. Our method is able to detect common spatial substructures. We demonstrate this in experiments with zinc finger proteins.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Proteome Science

ISSN

1477-5956

e-ISSN

—

Svazek periodika

2012

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

17

Strana od-do

—

Kód UT WoS článku

000315389600001

EID výsledku v databázi Scopus

—