A grid-enabled protein secondary structure predictor

We present an integrated Grid system for the prediction of protein secondary structures, based on the frequent automatic update of proteins in the training set. The predictor model is based on a feed-forward multilayer perceptron (MLP) neural network which is trained with the back-propagation algorithm; the design reuses existing legacy software and exploits novel grid components. The predictor takes into account the evolutionary information found in multiple sequence alignment (MSA); the information is obtained running an optimized parallel version of the PSI-BLAST tool, based on the MPI Master-Worker paradigm. The training set contains proteins of known structure. Using Grid technologies and efficient mechanisms for running the tools and extracting the data, the time needed to train the neural network is dramatically reduced, whereas the results are comparable to a set of well-known predictor tools.