Improved prediction of trans-membrane spans in proteins using an Artificial Neural Network.
IEEE Symp Comput Intell Bioinforma Comput Biol Proc
; 2009: 68-74, 2009.
Article
en En
| MEDLINE
| ID: mdl-27747315
ABSTRACT
Tools for the identification of trans-membrane spans from the protein sequence are widely used in the experimental community. Computational structural biology seeks to increase the prediction accuracy of such methods since they represent a first step towards membrane protein tertiary structure prediction from the amino acid sequence. We introduce a predictor that is able to identify trans-membrane spans from the sequence of a protein. The novelty of the approach presented here is the simultaneous prediction of trans-membrane spanning α-helices and ß-strands within a single tool. An artificial neural network was trained on databases of 102 membrane proteins and 3499 soluble proteins. Prediction accuracies of up to 92% for soluble residues, 75% for residues in the interface, and 73% for TM residues are achieved. On average the algorithm predicts 79% of the residues correctly which is a substantial improvement from a previously published implementation which achieved 57% accuracy (Koehler et al., Proteins Structure, Function, and Bioinformatics, 2008). The algorithm was applied to four membrane proteins to illustrate the applicability to both α-helical bundles and ß-barrels.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
IEEE Symp Comput Intell Bioinforma Comput Biol Proc
Año:
2009
Tipo del documento:
Article
País de afiliación:
Estados Unidos