  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, and correlation coefficients of 0.75 and 0.61 for contact
number and residue-wise contact order predictions, respectively. Conclusion:
CRNPRED will be a useful tool for computational as well as experimental
biologists who need accurate one-dimensional protein structure predictions.
