Donald Petrey

Structure-based prediction of protein–protein interactions on a genome-wide scale

The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms. Much of our present knowledge derives from high-throughput techniques such as the yeast two-hybrid assay and affinity purification, as well as from manual curation of experiments on individual systems. A variety of computational approaches based, for example, on sequence homology, gene co-expression and phylogenetic profiles, have also been developed for the genome-wide inference of protein–protein interactions (PPIs). Yet comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, termed PrePPI, which combines structural information with other functional clues, is comparable in accuracy to high-throughput experiments, yielding over 30,000 high-confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of considerable biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.

Using Multiple Structure Alignments, Fast Model Building, and Energetic Analysis in Fold Recognition and Homology Modeling

We participated in the fold recognition and homology sections of CASP5 using primarily in‐house software. The central feature of our structure prediction strategy involved the ability to generate good sequence‐to‐structure alignments and to quickly transform them into models that could be evaluated both with energy‐based methods and manually. The in‐house tools we used include: a) HMAP (Hybrid Multidimensional Alignment Profile)—a profile‐to‐profile alignment method that is derived from sequence‐enhanced multiple structure alignments in core regions, and sequence motifs in non‐structurally conserved regions. b) NEST–a fast model building program that applies an “artificial evolution” algorithm to construct a model from a given template and alignment. c) GRASP2–a new structure and alignment visualization program incorporating multiple structure superposition and domain database scanning modules. These methods were combined with model evaluation based on all atom and simplified physical‐chemical energy functions. All of these methods were under development during CASP5 and consequently a great deal of manual analysis was carried out at each stage of the prediction process. This interactive model building procedure has several advantages and suggests important ways in which our and other methods can be improved, examples of which are provided. Proteins 2003;53:430–435.

GRASP2: visualization, surface properties, and electrostatics of macromolecular structures and sequences.

The widespread use of the original version of GRASP revealed the importance of the visualization of physicochemical and structural properties on the molecular surface. This chapter describes a new version of GRASP that contains many new capabilities. In terms of analysis tools, the most notable new features are sequence and structure analysis and alignment tools and the graphical integration of sequence and structural information. Not all the new GRASP2 could be described here and more capabilities are continually being added. An on-line manual, details on obtaining the software, and technical notes about the program and the Troll software library can be found at the Honig laboratory Web site (http://trantor.bioc.columbia.edu).

Examination of Shape Complementarity in Docking of Unbound Proteins

Here we carry out an examination of shape complementarity as a criterion in protein‐protein docking and binding. Specifically, we examine the quality of shape complementarity as a critical determinant not only in the docking of 26 protein‐protein “bound” complexed cases, but in particular, of 19 “unbound” protein‐protein cases, where the structures have been determined separately. In all cases, entire molecular surfaces are utilized in the docking, with no consideration of the location of the active site, or of particular residues/atoms in either the receptor or the ligand that participate in the binding. To evaluate the goodness of the strictly geometry‐based shape complementarity in the docking process as compared to the main favorable and unfavorable energy components, we study systematically a potential correlation between each of these components and the root mean square deviation (RMSD) of the “unbound” protein‐protein cases. Specifically, we examine the non‐polar buried surface area, polar buried surface area, buried surface area relating to groups bearing unsatisfied buried charges, and the number of hydrogen bonds in all docked protein‐protein interfaces. For these cases, where the two proteins have been crystallized separately, and where entire molecular surfaces are considered without a predefinition of the binding site, no correlation is observed. None of these parameters appears to consistently improve on shape complementarity in the docking of unbound molecules. These findings argue that simplicity in the docking process, utilizing geometrical shape criteria may capture many of the essential features in protein‐protein docking. In particular, they further reinforce the long held notion of the importance of molecular surface shape complementarity in the binding, and hence in docking. This is particularly interesting in light of the fact that the structures of the docked pairs have been determined separately, allowing side chains on the surface of the proteins to move relatively freely.

Protein interface conservation across structure space

With the advent of Systems Biology, the prediction of whether two proteins form a complex has become a problem of increased importance. A variety of experimental techniques have been applied to the problem, but three-dimensional structural information has not been widely exploited. Here we explore the range of applicability of such information by analyzing the extent to which the location of binding sites on protein surfaces is conserved among structural neighbors. We find, as expected, that interface conservation is most significant among proteins that have a clear evolutionary relationship, but that there is a significant level of conservation even among remote structural neighbors. This finding is consistent with recent evidence that information available from structural neighbors, independent of classification, should be exploited in the search for functional insights. The value of such structural information is highlighted through the development of a new protein interface prediction method, PredUs, that identifies what residues on protein surfaces are likely to participate in complexes with other proteins. The performance of PredUs, as measured through comparisons with other methods, suggests that relationships across protein structure space can be successfully exploited in the prediction of protein-protein interactions.

PrePPI: a structure-informed database of protein–protein interactions

PrePPI (http://bhapp.c2b2.columbia.edu/PrePPI) is a database that combines predicted and experimentally determined protein–protein interactions (PPIs) using a Bayesian framework. Predicted interactions are assigned probabilities of being correct, which are derived from calculated likelihood ratios (LRs) by combining structural, functional, evolutionary and expression information, with the most important contribution coming from structure. Experimentally determined interactions are compiled from a set of public databases that manually collect PPIs from the literature and are also assigned LRs. A final probability is then assigned to every interaction by combining the LRs for both predicted and experimentally determined interactions. The current version of PrePPI contains ∼2 million PPIs that have a probability more than ∼0.1 of which ∼60 000 PPIs for yeast and ∼370 000 PPIs for human are considered high confidence (probability > 0.5). The PrePPI database constitutes an integrated resource that enables users to examine aggregate information on PPIs, including both known and potentially novel interactions, and that provides structural models for many of the PPIs.

Electrostatic contributions to protein-protein interactions: Fast energetic filters for docking and their physical basis

The methods of continuum electrostatics are used to calculate the binding free energies of a set of protein–protein complexes including experimentally determined structures as well as other orientations generated by a fast docking algorithm. In the native structures, charged groups that are deeply buried were often found to favor complex formation (relative to isosteric nonpolar groups), whereas in nonnative complexes generated by a geometric docking algorithm, they were equally likely to be stabilizing as destabilizing. These observations were used to design a new filter for screening docked conformations that was applied, in conjunction with a number of geometric filters that assess shape complementarity, to 15 antibody–antigen complexes and 14 enzyme‐inhibitor complexes. For the bound docking problem, which is the major focus of this paper, native and near‐native solutions were ranked first or second in all but two enzyme‐inhibitor complexes. Less success was encountered for antibody–antigen complexes, but in all cases studied, the more complete free energy evaluation was able to idey native and near‐native structures. A filter based on the enrichment of tyrosines and tryptophans in antibody binding sites was applied to the antibody–antigen complexes and resulted in a native and near‐native solution being ranked first and second in all cases. A clear improvement over previously reported results was obtained for the unbound antibody–antigen examples as well. The algorithm and various filters used in this work are quite efficient and are able to reduce the number of plausible docking orientations to a size small enough so that a final more complete free energy evaluation on the reduced set becomes computationally feasible.

Structural relationships among proteins with different global topologies and their implications for function annotation strategies

It has become increasingly apparent that geometric relationships often exist between regions of two proteins that have quite different global topologies or folds. In this article, we examine whether such relationships can be used to infer a functional connection between the two proteins in question. We find, by considering a number of examples involving metal and cation binding, sugar binding, and aromatic group binding, that geometrically similar protein fragments can share related functions, even if they have been classified as belonging to different folds and topologies. Thus, the use of classifications inevitably limits the number of functional inferences that can be obtained from the comparative analysis of protein structures. In contrast, the development of interactive computational tools that recognize the “continuous” nature of protein structure/function space, by increasing the number of potentially meaningful relationships that are considered, may offer a dramatic enhancement in the ability to extract information from protein structure databases. We introduce the MarkUs server, that embodies this strategy and that is designed for a user interested in developing and validating specific functional hypotheses.

PredUs: a web server for predicting protein interfaces using structural neighbors

We describe PredUs, an interactive web server for the prediction of protein–protein interfaces. Potential interfacial residues for a query protein are identified by ‘mapping’ contacts from known interfaces of the query protein’s structural neighbors to surface residues of the query. We calculate a score for each residue to be interfacial with a support vector machine. Results can be visualized in a molecular viewer and a number of interactive features allow users to tailor a prediction to a particular hypothesis. The PredUs server is available at: http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:PredUs.

Is protein classification necessary? Toward alternative approaches to function annotation

The current nonredundant protein sequence database contains over seven million entries and the number of individual functional domains is significantly larger than this value. The vast quantity of data associated with these proteins poses enormous challenges to any attempt at function annotation. Classification of proteins into sequence and structural groups has been widely used as an approach to simplifying the problem. In this article we question such strategies. We describe how the multifunctionality and structural diversity of even closely related proteins confounds efforts to assign function on the basis of overall sequence or structural similarity. Rather, we suggest that strategies that avoid classification may offer a more robust approach to protein function annotation.