Prediction of interacting surfaces by the Evolutionary Trace method
 

Olivier Lichtarge, Baylor College of Medicine.
 
 

Protein-protein interactions are the elementary units from which molecular pathways and cellular networks are built.  But the description of the functional surfaces that determine protein binding still elude us. The Evolutionary Trace (ET) approach to this problem is to combine sequences, evolutionary trees, and structures to reveal the canonical determinants of a protein¹s function.  Large-scale studies show that these determinants cluster spatially in the structure and that they match functional sites on proteins surfaces. Their discovery allows experimentalists to rationally design activity through targeted mutagenesis, for example along the G protein-signaling pathway. The scalability and generality of ET further suggest that proteome-wide annotation of functional sites is within reach. The activity of many protein structures may then be traced to narrow sets of relevant amino acids that form ³elementary units of function and of interaction². From a practical viewpoint, these units can be engineered to analyze and manipulate the molecular basis of protein function.The majority of proteins function when associated in multimolecular assemblies. Yet, prediction of the structures of multimolecular complexes has largely not been addressed, probably due to the magnitude of the combinatorial complexity of the problem. Docking applications have traditionally been used to predict pairwise interactions between molecules. We have developed an algorithm that extends the application of docking to multi-molecular assemblies.
  We apply it to predict both quaternary structures of oligomers and multi-proteins complexes. Moreover, adapting the algorithm to consider backbone connectivity, we also show that it may be useful in the prediction of protein tertiary structures when the structures of the protein parts are available. This application was tested both on domain assembly in order to predict the spatial arrangement of domains in multi-domain proteins, and on protein building blocks (substructures of domains with relatively high population times) assembly to predict their arrangement within a domain in the native protein