Prediction of Protein Topologies Using GIOHMMs and GRNNs

We develop and test new machine learning methods for the predic- tion of topological representations of protein structures in the form of coarse- or fine-grained contact or distance maps that are transla- tion and rotation invariant. The methods are based on generalized input-output hidden Markov models (GIOHMMs) and generalized recursive neural networks (GRNNs). The methods are used to pre- dict topology directly in the fine-grained case and, in the coarse- grained 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 pre- dictors achieve state-of-the-art performance.