Thinking the impossible: how to solve the protein folding problem with and without homologous structures and more.

ABSTRACT

Structure prediction of proteins is a difficult task as well as prediction of protein-protein interaction. When no homologous sequence with known structure is available for the target protein, search of distantly related proteins to the target may be done automatically (fold recognition/threading). However, there are difficult proteins for which still modeling on the basis of a putative scaffold is nearly impossible. In the following, we describe that for some specific examples, human expertise was able to derive alignments to proteins of similar function with the aid of machine learning-based methods specifically suited for predicting structural features. The manually curate search of putative templates was successful in generating low-resolution three-dimensional (3D) models in at least two cases the human tissue transglutaminase and the alcohol dehydrogenase from Sulfolobus solfataricus. This is based on the structural comparison of the model with the 3D protein structure that became available after prediction. For protein-protein interaction, a knowledge-based method can give predictions of putative interaction patches on the protein surface; this feature may help in adding additional weight to specific nodes in nets of interacting proteins.