Unknown

Dataset Information

0

Improved prediction of trans-membrane spans in proteins using an Artificial Neural Network.


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 ?-helices and ?-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 ?-helical bundles and ?-barrels.

SUBMITTER: Koehler J 

PROVIDER: S-EPMC5065243 | biostudies-literature | 2009 Mar-Apr

REPOSITORIES: biostudies-literature

altmetric image

Publications

Improved prediction of trans-membrane spans in proteins using an Artificial Neural Network.

Koehler Julia J   Mueller Ralf R   Meiler Jens J  

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology 20090301


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 simultaneou  ...[more]

Similar Datasets

| S-EPMC7039514 | biostudies-literature
| S-EPMC7178452 | biostudies-literature
| S-EPMC8860515 | biostudies-literature
| S-EPMC6028566 | biostudies-literature
| S-EPMC9713966 | biostudies-literature
| S-EPMC140555 | biostudies-literature
| S-EPMC6505829 | biostudies-literature
| S-EPMC3596400 | biostudies-literature
| S-EPMC9074795 | biostudies-literature
| S-EPMC7013409 | biostudies-literature