Method Development: Efficient Computer Vision Based Algo

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The rapid increase in experimental data along with recent progress in computational methods has brought modern biology a step closer toward solving one of the most challenging problems: prediction of protein function. Comprehension of protein function at its most basic level requires understanding of molecular interactions. Currently, it is becoming universally accepted that the scale of the accumulated data for analysis and for prediction necessitate highly efficient computational tools with appropriate application capabilities. We are developing computational methods for structural pattern discovery and for prediction of molecular associations. We focus on their applications toward a range of biological problems and the advantages of the combination of these methods and their integration with biological experiments. We synergistically merge structural modeling, rigid and flexible structural alignment and detection of conserved structural patterns and docking (rigid and flexible with hinge-bending movements). Our goal is toward a broader utilization of computational methods, and their cross-fertilization with experiment. 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 multimolecular assemblies. We apply it to predict quaternary structures of both oligomers and multi-protein complexes. The algorithm predicted well a near-native arrangement of the input subunits for all cases in our data set, where the number of the subunits of the different target complexes varied from three to ten. In order to simulate a more realistic scenario, unbound cases were tested. In these cases the input conformations of the subunits are either unbound conformations of the subunits or a model obtained by a homology modeling technique. The successful predictions of the unbound cases, where the input conformations of the subunits are different from their conformations within the target complex, suggest that the algorithm is robust. We expect that this type of algorithm should be particularly useful to predict the structures of large macromolecular assemblies, which are difficult to solve by experimental structure determination. The flexible docking algorithm, FlexDock, is unique in its ability to handle any number of hinges in the flexible molecule, without degradation in run-time performance, as compared to rigid docking. Protein surface regions with similar physicochemical properties and shapes may perform similar functions and bind similar binding partners. We developed algorithms and software packages for recognition of the similarity of binding sites and interfaces. Both methods recognize local geometrical and physicochemical similarity, which can be present even in the absence of overall sequence or fold similarity. The first method, SiteEngine, receives as an input two protein structures and searches the complete surface of one protein for regions similar to the binding site of the other. The second, Interface-to-Interface (I2I)-SiteEngine, compares protein-protein interfaces, which are regions of interaction between two protein molecules. It receives as an input two structures of protein-protein complexes, extracts the interfaces and finds the three-dimensional transformation that maximizes the similarity between two pairs of interacting binding sites. The output consists of a superimposition in PDB file format and a list of physicochemical properties shared by the compared entities. The methods are highly efficient and the freely available software packages are suitable for large-scale database searches of the entire PDB.