Prediction of Protein Topologies Using Generalized IOHMMs and RNNs

We develop and test new machine learning methods for the prediction oftopological representations of protein structures in the form of coarse-or fine-grained contact or distance maps that are translation androtation invariant. The methods are based on generalized input-output hidden Markov models (GIOHMMs) and generalized recursive neural networks (GRNNs). The methods are used to predict topologydirectly in the fine-grained case and, in the coarsegrained case,indirectly by first learning how to score candidate graphs and then using the scoring function to search the space of possible configurations. Computer simulations show that the predictors achievestate-of-the-art performance.