Prediction of 3D neighbours of molecular surface patches in proteins by artificial neural networks

Motivation: Molecular Surface Patches (MSPs) of proteins are responsible for selective interactions between internal parts of one protein molecule or between protein and other molecules. The prediction of the neighbours of a distinct Secondary Structural Element (SSE) would be an important step for protein structure prediction. Results: Based on a computational analysis of complementary molecular patches of SSEs, feed-forward Neural Networks (NNs) are trained on a large set of helices for predicting the neighbours of given MSPs. Accuracy of prediction is 96% if only two types of neighbours: solvent or 'protein' are considered, yet drops to 81% for three types of neighbours: (1) solvent, (2) helix/strand or (3) coil. Implications of the method for the prediction of protein structure and subunit interaction are discussed. As a special test case, the structurally equivalent helices of monomeric myoglobin and the homologous subunits of tetrameric haemoglobin are compared.