Evaluation of secondary structure of proteins from UV circular dichroism spectra using an unsupervised learning neural network.

Andrade MA; Chacon P; Merelo JJ; Moran F

Departamento de Bioquimica y Biologia Molecular I, Facultad de Ciencias Quimicas, Universidad Complutense de Madrid, Spain.

Protein Eng 6: 383-90 (1993)

Abstract
An optimized self-organizing map algorithm has been used to obtain protein topological (proteinotopic) maps. A neural network is able to arrange a set of proteins depending on their ultraviolet circular dichroism spectra in a completely unsupervised learning process. Analysis of the proteinotopic map reveals that the network extracts the main secondary structure features even with the small number of examples used. Some methods to use the proteinotopic map for protein secondary structure prediction are tested showing a good performance in the 200-240 nm wavelength range that is likely to increase as new protein structures are known.

Mesh Headings

Algorithms

Circular Dichroism*

Comparative Study

Myoglobin

Neural Networks (Computer)*

Protein Structure, Secondary*

Support, Non-U.S. Gov't

Unique Identifier: 93324517

Chemical Identifiers (Names)

(Myoglobin)