MOTIVATION: Predictions of protein local structure, derived from sequence 
alignment information alone, provide visualization tools for biologists to 
evaluate the importance of amino acid residue positions of interest in the 
absence of X-ray crystal/NMR structures or homology models. They are also useful 
as inputs to sequence analysis and modeling tools, such as hidden Markov models 
(HMMs), which can be used to search for homology in databases of known protein 
structure. In addition, local structure predictions can be used as a component 
of cost functions in genetic algorithms that predict protein tertiary structure. 
We have developed a program (predict-2nd) that trains multilayer neural networks 
and have applied it to numerous local structure alphabets, tuning network 
parameters such as the number of layers, the number of units in each layer and 
the window sizes of each layer. We have had the most success with four-layer 
networks, with gradually increasing window sizes at each layer.
RESULTS: Because the four-layer neural nets occasionally get trapped in poor 
local optima, our training protocol now uses many different random starts, with 
short training runs, followed by more training on the best performing networks 
from the short runs. One recent addition to the program is the option to add a 
guide sequence to the profile inputs, increasing the number of inputs per 
position by 20. We find that use of a guide sequence provides a small but 
consistent improvement in the predictions for several different local-structure 
alphabets.
AVAILABILITY: Local structure prediction with the methods described here is 
available for use online at 
http://www.soe.ucsc.edu/compbio/SAM_T08/T08-query.html. The source code and 
example networks for PREDICT-2ND are available at 
http://www.soe.ucsc.edu/~karplus/predict-2nd/ A required C++ library is 
available at http://www.soe.ucsc.edu/~karplus/ultimate/