Analysis of protein structures can help predict protein functions. We develop a weighted network that depicts the protein structures. We also develop a deep neural network (DNN) to use this measure for classification.
As proteins with similar structures often have similar functions, analysis of
protein structures can help predict protein functions and is thus important. We
consider the problem of protein structure classification, which computationally
classifies the structures of proteins into pre-defined groups. We develop a
weighted network that depicts the protein structures, and more importantly, we
propose the first graphlet-based measure that applies to weighted networks.
Further, we develop a deep neural network (DNN) composed of both convolutional
and recurrent layers to use this measure for classification. Put together, our
approach shows dramatic improvements in performance over existing
graphlet-based approaches on 36 real datasets. Even comparing with the
state-of-the-art approach, it almost halves the classification error. In
addition to protein structure networks, our weighted-graphlet measure and DNN
classifier can potentially be applied to classification of other weighted
networks in computational biology as well as in other domains.