CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks.

ABSTRACT:

Background

One-dimensional protein structures such as secondary structures or contact numbers are useful for three-dimensional structure prediction and helpful for intuitive understanding of the sequence-structure relationship. Accurate prediction methods will serve as a basis for these and other purposes.

Results

We implemented a program CRNPRED which predicts secondary structures, contact numbers and residue-wise contact orders. This program is based on a novel machine learning scheme called critical random networks. Unlike most conventional one-dimensional structure prediction methods which are based on local windows of an amino acid sequence, CRNPRED takes into account the whole sequence. CRNPRED achieves, on average per chain, Q3 = 81% for secondary structure prediction

SUBMITTER: Kinjo AR 

PROVIDER: S-EPMC1578593 | biostudies-literature | 2006 Sep

REPOSITORIES: biostudies-literature