HTP: a neural network-based method for predicting the topology of helical transmembrane domains in proteins | Zendy

Piero Fariselli | Zendy; Rita Casadio | Zendy

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HTP: a neural network-based method for predicting the topology of helical transmembrane domains in proteins

Author(s) -

Piero Fariselli,

Rita Casadio

Publication year - 1996

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/12.1.41

Subject(s) - topology (electrical circuits) , transmembrane protein , artificial neural network , orientation (vector space) , membrane topology , membrane protein , computer science , network topology , integral membrane protein , microcomputer , transmembrane domain , biological system , membrane , artificial intelligence , chemistry , biology , mathematics , geometry , biochemistry , receptor , combinatorics , operating system , telecommunications , chip

In this paper we describe a microcomputer program (HTP) for predicting the location and orientation of alpha-helical transmembrane segments in integral membrane proteins. HTP is a neural network-based tool which gives as output the protein membrane topology based on the statistical propensity of residues to be located in external and internal loops. This method, which uses single protein sequences as input to the network system, correctly predicts the topology of 71 out of 92 membrane proteins of putative membrane orientation, independently of the protein source.

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