A combinatorial score to distinguish biological and nonbiological protein-protein interfaces

With the large amount of protein-protein complex structural data available, to understand the key features governing the specificity of protein-protein recognition and to define a suitable scoring function for protein-protein interaction predictions, we have analyzed the protein interfaces from geometric and energetic points of view. Atom-based potential of mean force (PMFScore), packing density, contact size, and geometric complementarity are calculated for crystal contacts in 74 homodimers and 91 monomers, which include real biological interactions in dimers and nonbiological contacts in monomers and dimers. Simple cutoffs were developed for single and combinatorial parameters to distinguish biological and nonbiological contacts. The results show that PMFScore is a better discriminator between biological and nonbiological interfaces comparable in size. The combination of PMFScore and contact size is the most powerful pairwise discriminator. A combinatorial score (CFPScore) based on the four parameters was developed, which gives the success rate of the homodimer discrimination of 96.6% and error rate of the monomer discrimination of 6.0% and 19.8% according to Valdar's and our definition, respectively. Compared with other statistical learning models, the cutoffs for the four parameters and their combinations are directly based on physical models, simple, and can be easily applied to protein-protein interface analysis and docking studies.     

Fully automated protein complex prediction based on topological similarity and community structure

To understand the function of protein complexes and their association with biological processes, a lot of studies have been done towards analyzing the protein-protein interaction (PPI) networks. However, the advancement in high-throughput technology has resulted in a humongous amount of data for analysis. Moreover, high level of noise, sparseness, and skewness in degree distribution of PPI networks limits the performance of many clustering algorithms and further analysis of their interactions.In addressing and solving these problems we present a novel random walk based algorithm that converts the incomplete and binary PPI network into a protein-protein topological similarity matrix (PP-TS matrix). We believe that if two proteins share some high-order topological similarities they are likely to be interacting with each other. Using the obtained PP-TS matrix, we constructed and used weighted networks to further study and analyze the interaction among proteins. Specifically, we applied a fully automated community structure finding algorithm (Auto-HQcut) on the obtained weighted network to cluster protein complexes. We then analyzed the protein complexes for significance in biological processes. To help visualize and analyze these protein complexes we also developed an interface that displays the resulting complexes as well as the characteristics associated with each complex.Applying our approach to a yeast protein-protein interaction network, we found that the predicted protein-protein interaction pairs with high topological similarities have more significant biological relevance than the original protein-protein interactions pairs. When we compared our PPI network reconstruction algorithm with other existing algorithms using gene ontology and gene co-expression, our algorithm produced the highest similarity scores. Also, our predicted protein complexes showed higher accuracy measure compared to the other protein complex predictions.     

Integrated analysis of multiple data sources reveals modular structure of biological networks

It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks.     

Application of hydrogen/deuterium exchange mass spectrometry to study protein tyrosine phosphatase dynamics, ligand binding, and substrate specificity

Protein tyrosine phosphatases (PTPs) are signaling enzymes that control a diverse array of cellular processes. Further insight into the specific functional roles of PTPs in cellular signaling requires detailed understanding of the molecular basis for substrate recognition by the PTPs. A central question is how PTPs discriminate between multiple structurally diverse substrates that they encounter in the cell. Although X-ray crystallography is capable of revealing the intimate structural details for molecular interaction, structures of higher order PTP.substrate complexes are often difficult to obtain. Hydrogen/deuterium exchange mass spectrometry (H/DX-MS) is a powerful tool for mapping protein-protein interfaces, as well as identifying conformational and dynamic perturbations in proteins. In addition, H/DX-MS enables analysis of large protein complexes at physiological concentrations and provides insight into the solution behavior of these complexes that can not be gleaned from crystal structures. We have utilized H/DX-MS to probe PTP dynamics, ligand binding, and the structural basis of substrate recognition. In this article, we review general principles of H/DX-MS technology as applied to study protein-protein interactions and dynamics. We also provide protocols for H/DX-MS successfully used in our laboratory to define the molecular basis of ERK2 substrate recognition by MKP3. Many of the aspects that we cover in detail should be applicable to the study of other PTPs with their specific targets.     

Discrimination between stable and dynamic components of protein complexes by means of quantitative proteomics

To discriminate between stable and dynamic protein-protein interactions, we propose a strategy in which cells with and without tagged bait are differentially labeled with stable isotope and combined prior to complex purification. Mass-spectrometric analysis of the purified complexes identifies stable and dynamic components as those derived exclusively from the tagged cells and those from both cells, respectively. We successfully applied this strategy to analyze two yeast protein complexes, eIF2B-eIF2 and cyclin-Cdc28.     

Deconvolution of targeted protein-protein interaction maps

Current proteomic techniques allow researchers to analyze chosen biological pathways or an ensemble of related protein complexes at a global level via the measure of physical protein-protein interactions by affinity purification mass spectrometry (AP-MS). Such experiments yield information-rich but complex interaction maps whose unbiased interpretation is challenging. Guided by current knowledge on the modular structure of protein complexes, we propose a novel statistical approach, named BI-MAP, complemented by software tools and a visual grammar to present the inferred modules. We show that the BI-MAP tools can be applied from small and very detailed maps to large, sparse, and much noisier data sets. The BI-MAP tool implementation and test data are made freely available.     

Potential of mean force for protein-protein interaction studies.

Calculating protein-protein interaction energies is crucial for understanding protein-protein associations. On the basis of the methodology of mean-field potential, we have developed an empirical approach to estimate binding free energy for protein-protein interactions. This knowledge-based approach has been used to derive distance-dependent free energies of protein complexes from a nonredundant training set in the Protein Data Bank (PDB), with a careful treatment of homology. We calculate atom pair potentials for 16 pair interactions, which can reflect the importance of hydrophobic interactions and specific hydrogen-bonding interactions. The derived potentials for hydrogen-bonding interactions show a valley of favorable interactions at a distance of approximately 3 A, corresponding to that of an established hydrogen bond. For the test set of 28 protein complexes, the calculated energies have a correlation coefficient of 0.75 compared with experimental binding free energies. The performance of the method in ranking the binding energies of different protein-protein complexes shows that the energy estimation can be applied to value binding free energies for protein-protein associations.     

Interactive proteomics research technologies: recent applications and advances

Proteins rarely exert their function alone. They normally function in multiprotein complexes that play central roles in all biological functions. Thus, it is not surprising that the investigation of protein-protein interactions on a global scale, of so-called interactomes, has become crucial to modern molecular biology. Dissecting partners in protein complexes gives insight into their molecular function and can help in understanding disease-related mechanisms, ultimately resulting in better drug target definition. A variety of methods exist to unravel protein interaction circuitries and recently, significant progress has been made in adapting these tools for the generation of large-scale interaction datasets. Here, we present an overview of the latest advances and applications of interactive proteomics research technologies.     

Study of protein-protein interaction using conformational space annealing

We apply conformational space annealing (CSA), an efficient global optimization method, to the study of protein-protein interaction. The CSA is incorporated into the Tinker molecular modeling package along with a B-spline method for CAPRI Round 5 experiments. We have used an energy function for the protein-protein interaction that consists of electrostatic interaction, van der Waals interaction, and solvation energy terms represented by the occupancy desolvation method. The parameters of the AMBER94 all-atom empirical force field are used. Each energy term is calculated by precalculated grid potentials and B-spline method approximation. The ligand protein is placed inside a sphere of 50 A radius centered at an appropriate location, and the CSA rigid docking studies are carried out to find stable complexes. Up to 10 complexes are selected using the K-mean clustering method and biological information when available. These complexes are energy-minimized for further refinement by considering the flexibility of interacting proteins. The results show that the CSA method has a potential for the study of protein-protein interaction.     

Homology inference of protein-protein interactions via conserved binding sites

The coverage and reliability of protein-protein interactions determined by high-throughput experiments still needs to be improved, especially for higher organisms, therefore the question persists, how interactions can be verified and predicted by computational approaches using available data on protein structural complexes. Recently we developed an approach called IBIS (Inferred Biomolecular Interaction Server) to predict and annotate protein-protein binding sites and interaction partners, which is based on the assumption that the structural location and sequence patterns of protein-protein binding sites are conserved between close homologs. In this study first we confirmed high accuracy of our method and found that its accuracy depends critically on the usage of all available data on structures of homologous complexes, compared to the approaches where only a non-redundant set of complexes is employed. Second we showed that there exists a trade-off between specificity and sensitivity if we employ in the prediction only evolutionarily conserved binding site clusters or clusters supported by only one observation (singletons). Finally we addressed the question of identifying the biologically relevant interactions using the homology inference approach and demonstrated that a large majority of crystal packing interactions can be correctly identified and filtered by our algorithm. At the same time, about half of biological interfaces that are not present in the protein crystallographic asymmetric unit can be reconstructed by IBIS from homologous complexes without the prior knowledge of crystal parameters of the query protein.     

An iterative knowledge-based scoring function for protein-protein recognition

Using an efficient iterative method, we have developed a distance-dependent knowledge-based scoring function to predict protein-protein interactions. The function, referred to as ITScore-PP, was derived using the crystal structures of a training set of 851 protein-protein dimeric complexes containing true biological interfaces. The key idea of the iterative method for deriving ITScore-PP is to improve the interatomic pair potentials by iteration, until the pair potentials can distinguish true binding modes from decoy modes for the protein-protein complexes in the training set. The iterative method circumvents the challenging reference state problem in deriving knowledge-based potentials. The derived scoring function was used to evaluate the ligand orientations generated by ZDOCK 2.1 and the native ligand structures on a diverse set of 91 protein-protein complexes. For the bound test cases, ITScore-PP yielded a success rate of 98.9% if the top 10 ranked orientations were considered. For the more realistic unbound test cases, the corresponding success rate was 40.7%. Furthermore, for faster orientational sampling purpose, several residue-level knowledge-based scoring functions were also derived following the similar iterative procedure. Among them, the scoring function that uses the side-chain center of mass (SCM) to represent a residue, referred to as ITScore-PP(SCM), showed the best performance and yielded success rates of 71.4% and 30.8% for the bound and unbound cases, respectively, when the top 10 orientations were considered. ITScore-PP was further tested using two other published protein-protein docking decoy sets, the ZDOCK decoy set and the RosettaDock decoy set. In addition to binding mode prediction, the binding scores predicted by ITScore-PP also correlated well with the experimentally determined binding affinities, yielding a correlation coefficient of R = 0.71 on a test set of 74 protein-protein complexes with known affinities. ITScore-PP is computationally efficient. The average run time for ITScore-PP was about 0.03 second per orientation (including optimization) on a personal computer with 3.2 GHz Pentium IV CPU and 3.0 GB RAM. The computational speed of ITScore-PP(SCM) is about an order of magnitude faster than that of ITScore-PP. ITScore-PP and/or ITScore-PP(SCM) can be combined with efficient protein docking software to study protein-protein recognition.     

Identification of protein-protein interaction sites from docking energy landscapes

Protein recognition is one of the most challenging and intriguing problems in structural biology. Despite all the available structural, sequence and biophysical information about protein-protein complexes, the physico-chemical patterns, if any, that make a protein surface likely to be involved in protein-protein interactions, remain elusive. Here, we apply protein docking simulations and analysis of the interaction energy landscapes to identify protein-protein interaction sites. The new protocol for global docking based on multi-start global energy optimization of an all-atom model of the ligand, with detailed receptor potentials and atomic solvation parameters optimized in a training set of 24 complexes, explores the conformational space around the whole receptor without restrictions. The ensembles of the rigid-body docking solutions generated by the simulations were subsequently used to project the docking energy landscapes onto the protein surfaces. We found that highly populated low-energy regions consistently corresponded to actual binding sites. The procedure was validated on a test set of 21 known protein-protein complexes not used in the training set. As much as 81% of the predicted high-propensity patch residues were located correctly in the native interfaces. This approach can guide the design of mutations on the surfaces of proteins, provide geometrical details of a possible interaction, and help to annotate protein surfaces in structural proteomics.     

Unraveling the dynamics of protein interactions with quantitative mass spectrometry

Knowledge of structure and dynamics of proteins and protein complexes is important to unveil the molecular basis and mechanisms involved in most biological processes. Protein complex dynamics can be defined as the changes in the composition of a protein complex during a cellular process. Protein dynamics can be defined as conformational changes in a protein during enzyme activation, for example, when a protein binds to a ligand or when a protein binds to another protein. Mass spectrometry (MS) combined with affinity purification has become the analytical tool of choice for mapping protein-protein interaction networks and the recent developments in the quantitative proteomics field has made it possible to identify dynamically interacting proteins. Furthermore, hydrogen/deuterium exchange MS is emerging as a powerful technique to study structure and conformational dynamics of proteins or protein assemblies in solution. Methods have been developed and applied for the identification of transient and/or weak dynamic interaction partners and for the analysis of conformational dynamics of proteins or protein complexes. This review is an overview of existing and recent developments in studying the overall dynamics of in vivo protein interaction networks and protein complexes using MS-based methods.     

Using indirect protein-protein interactions for protein complex prediction

Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein-protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.     

A mechanism for death receptor discrimination by death adaptors

The death domain and death effector domain are two common motifs that mediate protein-protein interactions between components of cell death signaling complexes. The mechanism by which these domains engage their binding partners has been explored by extensive mutagenesis of two death adaptors, FADD and TRADD, suggesting that a death adaptor can discriminate its intended binding partners from other proteins harboring similar motifs. Death adaptors are found to utilize one of two topologically conserved surfaces for protein-protein interaction, whether that partner is another adaptor or its cognate receptor. These surfaces are topologically related to the interaction between death domains observed in the x-ray crystal structure of the Drosophila adaptor Tube bound to Pelle kinase. Comparing the topology of protein-protein interactions for FADD complexes to TRADD complexes reveals that FADD uses a Tube-like surface in each of its death motifs to engage either CD95 or TRADD. TRADD reverses these roles, employing a Pelle-like surface to interact with either receptor TNFR1 or adaptor FADD. Since death adaptors display a Tube-like or Pelle-like preference for engaging their binding partners, Tube/Pelle-like pairing provides a mechanism for death adaptor discrimination of death receptors.     

Identifying protein complexes from interaction networks based on clique percolation and distance restriction

Background

Identification of protein complexes in large interaction networks is crucial to understand principles of cellular organization and predict protein functions, which is one of the most important issues in the post-genomic era. Each protein might be subordinate multiple protein complexes in the real protein-protein interaction networks. Identifying overlapping protein complexes from protein-protein interaction networks is a considerable research topic.

Result

As an effective algorithm in identifying overlapping module structures, clique percolation method (CPM) has a wide range of application in social networks and biological networks. However, the recognition accuracy of algorithm CPM is lowly. Furthermore, algorithm CPM is unfit to identifying protein complexes with meso-scale when it applied in protein-protein interaction networks. In this paper, we propose a new topological model by extending the definition of k-clique community of algorithm CPM and introduced distance restriction, and develop a novel algorithm called CP-DR based on the new topological model for identifying protein complexes. In this new algorithm, the protein complex size is restricted by distance constraint to conquer the shortcomings of algorithm CPM. The algorithm CP-DR is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes.

Conclusion

The proposed algorithm CP-DR based on clique percolation and distance restriction makes it possible to identify dense subgraphs in protein interaction networks, a large number of which correspond to known protein complexes. Compared to algorithm CPM, algorithm CP-DR has more outstanding performance.

     

An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes

Hydrogen bonding is a key contributor to the specificity of intramolecular and intermolecular interactions in biological systems. Here, we develop an orientation-dependent hydrogen bonding potential based on the geometric characteristics of hydrogen bonds in high-resolution protein crystal structures, and evaluate it using four tests related to the prediction and design of protein structures and protein-protein complexes. The new potential is superior to the widely used Coulomb model of hydrogen bonding in prediction of the sequences of proteins and protein-protein interfaces from their structures, and improves discrimination of correctly docked protein-protein complexes from large sets of alternative structures.     

基于质谱技术的蛋白质互作研究进展

蛋白质作为生命活动的执行者,其功能往往体现在与其他蛋白质的相互作用中,研究蛋白-蛋白相互作用对于人们深入了解和预防传染病、靶向治疗多基因疾病、阐明蛋白质的分子作用机制及各种复杂的生命现象具有重要意义。目前,有多种技术被用来研究蛋白间的相互作用,研究难点在于实时捕获瞬时或弱蛋白质间的相互作用,质谱技术（mass spectrometry, MS）可在某种程度上解决该难点。由于质谱技术可研究简单的蛋白质复合物再到大规模的蛋白质组实验,基于质谱技术研究蛋白质间相互作用被越来越多地应用于科学研究中。综述了蛋白质间相互作用检测方法的研究进展,重点介绍了氢氘交换质谱法和化学交联质谱法研究蛋白质间相互作用的优缺点及其应用,最后对基于质谱技术研究蛋白质间相互作用进行了总结与展望,以期为深入开展相关研究提供借鉴。