High-accuracy peptide mass fingerprinting using peak intensity data with machine learning.

Yang, Dongmei; Ramkissoon, Kevin; Hamlett, Eric; Giddings, Morgan C

Yang, Dongmei; Ramkissoon, Kevin; Hamlett, Eric; Giddings, Morgan C.

Afiliación

Yang D; Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

J Proteome Res ; 7(1): 62-9, 2008 Jan.

Article en En | MEDLINE | ID: mdl-17914788

RESUMEN

For MALDI-TOF mass spectrometry, we show that the intensity of a peptide-ion peak is directly correlated with its sequence, with the residues M, H, P, R, and L having the most substantial effect on ionization. We developed a machine learning approach that exploits this relationship to significantly improve peptide mass fingerprint (PMF) accuracy based on training data sets from both true-positive and false-positive PMF searches. The model's cross-validated accuracy in distinguishing real versus false-positive database search results is 91%, rivaling the accuracy of MS/MS-based protein identification.

Asunto(s)

Inteligencia Artificial; Mapeo Peptídico/métodos; Espectrometría de Masas en Tándem/métodos; Interpretación Estadística de Datos; Proteínas/análisis; Espectrometría de Masa por Láser de Matriz Asistida de Ionización Desorción

Buscar en Google

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Mapeo Peptídico / Inteligencia Artificial / Espectrometría de Masas en Tándem Idioma: En Revista: J Proteome Res Asunto de la revista: BIOQUIMICA Año: 2008 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

Buscar en Google

Añadir a Mi BVS

Imprimir

XML

PubMed Links