Neural network definitions of highly predictable protein secondary structure classes
- Los Alamos National Lab., NM (United States)
- Toronto Univ., ON (Canada). Dept. of Computer Science
We use two co-evolving neural networks to determine new classes of protein secondary structure which are significantly more predictable from local amino sequence than the conventional secondary structure classification. Accurate prediction of the conventional secondary structure classes: alpha helix, beta strand, and coil, from primary sequence has long been an important problem in computational molecular biology. Neural networks have been a popular method to attempt to predict these conventional secondary structure classes. Accuracy has been disappointingly low. The algorithm presented here uses neural networks to similtaneously examine both sequence and structure data, and to evolve new classes of secondary structure that can be predicted from sequence with significantly higher accuracy than the conventional classes. These new classes have both similarities to, and differences with the conventional alpha helix, beta strand and coil.
- Research Organization:
- Los Alamos National Lab., NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 10121251
- Report Number(s):
- LA-UR-94-110; CONF-9311171-2; ON: DE94006234
- Resource Relation:
- Conference: Neural information processing systems (NIPS) conference,Denver, CO (United States),30 Nov - 2 Dec 1993; Other Information: PBD: [1994]
- Country of Publication:
- United States
- Language:
- English
Similar Records
Pairwise covariance adds little to secondary structure prediction but improves the prediction of non-canonical local structure
Characterizing and learning of protein conformations