Nurcan Tuncbag

Identification of computational hot spots in protein interfaces

MOTIVATION Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. These residues are critical in understanding the principles of protein interactions. Experimental studies like alanine scanning mutagenesis require significant effort; therefore, there is a need for computational methods to predict hot spots in protein interfaces. RESULTS We present a new intuitive efficient method to determine computational hot spots based on conservation (C), solvent accessibility [accessible surface area (ASA)] and statistical pairwise residue potentials (PP) of the interface residues. Combination of these features is examined in a comprehensive way to study their effect in hot spot detection. The predicted hot spots are observed to match with the experimental hot spots with an accuracy of 70% and a precision of 64% in Alanine Scanning Energetics Database (ASEdb), and accuracy of 70% and a precision of 73% in Binding Interface Database (BID). Several machine learning methods are also applied to predict hot spots. Performance of our empirical approach exceeds learning-based methods and other existing hot spot prediction methods. Residue occlusion from solvent in the complexes and pairwise potentials are found to be the main discriminative features in hot spot prediction. CONCLUSION Our empirical method is a simple approach in hot spot prediction yet with its high accuracy and computational effectiveness. We believe that this method provides insights for the researchers working on characterization of protein binding sites and design of specific therapeutic agents for protein interactions. AVAILABILITY The list of training and test sets are available as Supplementary Data at http://prism.ccbb.ku.edu.tr/hotpoint/supplement.doc. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

A survey of available tools and web servers for analysis of protein–protein interactions and interfaces

The unanimous agreement that cellular processes are (largely) governed by interactions between proteins has led to enormous community efforts culminating in overwhelming information relating to these proteins; to the regulation of their interactions, to the way in which they interact and to the function which is determined by these interactions. These data have been organized in databases and servers. However, to make these really useful, it is essential not only to be aware of these, but in particular to have a working knowledge of which tools to use for a given problem; what are the tool advantages and drawbacks; and no less important how to combine these for a particular goal since usually it is not one tool, but some combination of tool-modules that is needed. This is the goal of this review.

HotPoint: hot spot prediction server for protein interfaces

The energy distribution along the protein–protein interface is not homogenous; certain residues contribute more to the binding free energy, called ‘hot spots’. Here, we present a web server, HotPoint, which predicts hot spots in protein interfaces using an empirical model. The empirical model incorporates a few simple rules consisting of occlusion from solvent and total knowledge-based pair potentials of residues. The prediction model is computationally efficient and achieves high accuracy of 70%. The input to the HotPoint server is a protein complex and two chain identifiers that form an interface. The server provides the hot spot prediction results, a table of residue properties and an interactive 3D visualization of the complex with hot spots highlighted. Results are also downloadable as text files. This web server can be used for analysis of any protein–protein interface which can be utilized by researchers working on binding sites characterization and rational design of small molecules for protein interactions. HotPoint is accessible at http://prism.ccbb.ku.edu.tr/hotpoint.

HotSprint: database of computational hot spots in protein interfaces

We present a new database of computational hot spots in protein interfaces: HotSprint. Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. HotSprint contains data for 35 776 protein interfaces among 49 512 protein interfaces extracted from the multi-chain structures in Protein Data Bank (PDB) as of February 2006. The conserved residues in interfaces with certain buried accessible solvent area (ASA) and complex ASA thresholds are flagged as computational hot spots. The predicted hot spots are observed to correlate with the experimental hot spots with an accuracy of 76%. Several machine-learning methods (SVM, Decision Trees and Decision Lists) are also applied to predict hot spots, results reveal that our empirical approach performs better than the others. A web interface for the HotSprint database allows users to browse and query the hot spots in protein interfaces. HotSprint is available at http://prism.ccbb.ku.edu.tr/hotsprint; and it provides information for interface residues that are functionally and structurally important as well as the evolutionary history and solvent accessibility of residues in interfaces.

Architectures and functional coverage of protein-protein interfaces.

The diverse range of cellular functions is performed by a limited number of protein folds existing in nature. One may similarly expect that cellular functional diversity would be covered by a limited number of protein-protein interface architectures. Here, we present 8205 interface clusters, each representing a unique interface architecture. This data set of protein-protein interfaces is analyzed and compared with older data sets. We observe that the number of both biological and crystal interfaces increases significantly compared to the number of Protein Data Bank entries. Furthermore, we find that the number of distinct interface architectures grows at a much faster rate than the number of folds and is yet to level off. We further analyze the growth trend of the functional coverage by constructing functional interaction networks from interfaces. The functional coverage is also found to steadily increase. Interestingly, we also observe that despite the diversity of interface architectures, some are more favorable and frequently used, and of particular interest, are the ones that are also preferred in single chains.

Towards inferring time dimensionality in protein–protein interaction networks by integrating structures: the p53 example

Structural data, efficient structural comparison algorithms and appropriate datasets and filters can assist in getting an insight into time dimensionality in interaction networks; in predicting which interactions can and cannot co-exist; and in obtaining concrete predictions consistent with experiment.

Predicting Protein–Protein Interactions from the Molecular to the Proteome Level

Identification of protein-protein interactions (PPIs) is at the center of molecular biology considering the unquestionable role of proteins in cells. Combinatorial interactions result in a repertoire of multiple functions; hence, knowledge of PPI and binding regions naturally serve to functional proteomics and drug discovery. Given experimental limitations to find all interactions in a proteome, computational prediction/modeling of protein interactions is a prerequisite to proceed on the way to complete interactions at the proteome level. This review aims to provide a background on PPIs and their types. Computational methods for PPI predictions can use a variety of biological data including sequence-, evolution-, expression-, and structure-based data. Physical and statistical modeling are commonly used to integrate these data and infer PPI predictions. We review and list the state-of-the-art methods, servers, databases, and tools for protein-protein interaction prediction.

SteinerNet: a web server for integrating ‘omic’ data to discover hidden components of response pathways

High-throughput technologies including transcriptional profiling, proteomics and reverse genetics screens provide detailed molecular descriptions of cellular responses to perturbations. However, it is difficult to integrate these diverse data to reconstruct biologically meaningful signaling networks. Previously, we have established a framework for integrating transcriptional, proteomic and interactome data by searching for the solution to the prize-collecting Steiner tree problem. Here, we present a web server, SteinerNet, to make this method available in a user-friendly format for a broad range of users with data from any species. At a minimum, a user only needs to provide a set of experimentally detected proteins and/or genes and the server will search for connections among these data from the provided interactomes for yeast, human, mouse, Drosophila melanogaster and Caenorhabditis elegans. More advanced users can upload their own interactome data as well. The server provides interactive visualization of the resulting optimal network and downloadable files detailing the analysis and results. We believe that SteinerNet will be useful for researchers who would like to integrate their high-throughput data for a specific condition or cellular response and to find biologically meaningful pathways. SteinerNet is accessible at http://fraenkel.mit.edu/steinernet.

Fast and accurate modeling of protein–protein interactions by combining template-interface-based docking with flexible refinement

The similarity between folding and binding led us to posit the concept that the number of protein–protein interface motifs in nature is limited, and interacting protein pairs can use similar interface architectures repeatedly, even if their global folds completely vary. Thus, known protein–protein interface architectures can be used to model the complexes between two target proteins on the proteome scale, even if their global structures differ. This powerful concept is combined with a flexible refinement and global energy assessment tool. The accuracy of the method is highly dependent on the structural diversity of the interface architectures in the template dataset. Here, we validate this knowledge‐based combinatorial method on the Docking Benchmark and show that it efficiently finds high‐quality models for benchmark complexes and their binding regions even in the absence of template interfaces having sequence similarity to the targets. Compared to “classical” docking, it is computationally faster; as the number of target proteins increases, the difference becomes more dramatic. Further, it is able to distinguish binders from nonbinders. These features allow performing large‐scale network modeling. The results on an independent target set (proteins in the p53 molecular interaction map) show that current method can be used to predict whether a given protein pair interacts. Overall, while constrained by the diversity of the template set, this approach efficiently produces high‐quality models of protein–protein complexes. We expect that with the growing number of known interface architectures, this type of knowledge‐based methods will be increasingly used by the broad proteomics community. Proteins 2012;

Characterization and prediction of protein interfaces to infer protein-protein interaction networks.

Complex protein-protein interaction networks govern biological processes in cells. Protein interfaces are the sites where proteins physically interact. Identification and characterization of protein interfaces will lead to understanding how proteins interact with each other and how they are involved in protein-protein interaction networks. What makes a given interface bind to different proteins; how similar/different the interactions in proteins are some key questions to be answered. Enormous amount of protein structures and experimental protein-protein interactions data necessitate advanced computational methods for analyzing and inferring new knowledge. Interface prediction methods use a wide range of sequence, structural and physico-chemical characteristics that distinguish interface residues from non-interface surface residues. Here, we present a review focusing on the characteristics of interfaces and the current status of interface prediction methods.