New Machine Learning Methods for the Prediction of Protein Topologies
Protein structures are translation and rotation invariant. In protein structure prediction, it is therefore important to be able to assess and predict intermediary topological representations, such as distance or contact maps, that are translation and rotation invariant. Here we develop several new machine learning methods for the prediction and assessment of fine-grained and coarse topological representations of proteins. In particular, we introduce a general class of graphical model architectures together with the corresponding neural network implementations. These architectures can be viewed as Bayesian network generalizations of input-output hidden Markov models (GIOHMMs), involving an input layer, an output layer, and a hidden layer supported by one or several directed acyclic graphs. The corresponding generalized recursive neural network (GRNN) architectures are derived by preserving the graphical structures of the GIOHMMs, but replacing the conditional probability tables with learnable deterministic functions. Two methods are proposed for the prediction of protein topological structures. The first method uses a GIOHMM organized into six horizontal layers: one input plane, four hidden planes, and one output plane that directly represents the adjacency matrix of the contact map (or the distance matrix). Each hidden plane is associated with one of the four cardinal corners towards which all the edges of the corresponding lattice are oriented. The corresponding GRNNs are used to construct a fine-grained contact map predictor. The second method uses a GIOHMM approach to learn a graph scoring function which, in turn, is used to efficiently search the space of possible configurations. The corresponding GRNNs are used to construct a coarse-grained contact map predictor. Computer simulations show that the predictors for both tasks achieve stateof-the-art performance.