Combining a binary input encoding scheme with RBFNN for globulin protein inter-residue contact map prediction

In this paper, we focus on protein inter-residue contacts map prediction, one of the most important intermediate steps to the protein folding problem, based on radial basis function neural network (RBFNN), and propose a novel binary encoding scheme for the purpose of learning the inter-residue contact patterns. The experimental evidence on globulin protein indicates the utility of our proposed encoding strategy. Moreover, the simulation results demonstrate that the network get a better performance for these proteins, whose residue length falls into the area of (100, 300), and our proposed encoding strategy has promising future in the research on contacts map prediction problem.