Computational design of protein-protein interactions

Computational protein design strategies have been developed to reengineer protein-protein interfaces in an automated, generalizable fashion. In the past two years, these methods have been successfully applied to generate chimeric proteins and protein pairs with specificities different from naturally occurring protein-protein interactions. Although there are shortcomings in current approaches, both in the way conformational space is sampled and in the energy functions used to evaluate designed conformations, the successes suggest we are now entering an era in which computational methods can be used to modulate, reengineer and design protein-protein interaction networks in living cells.     

Computational prediction of host-pathogen protein-protein interactions

MOTIVATION: Infectious diseases such as malaria result in millions of deaths each year. An important aspect of any host-pathogen system is the mechanism by which a pathogen can infect its host. One method of infection is via protein-protein interactions (PPIs) where pathogen proteins target host proteins. Developing computational methods that identify which PPIs enable a pathogen to infect a host has great implications in identifying potential targets for therapeutics. RESULTS: We present a method that integrates known intra-species PPIs with protein-domain profiles to predict PPIs between host and pathogen proteins. Given a set of intra-species PPIs, we identify the functional domains in each of the interacting proteins. For every pair of functional domains, we use Bayesian statistics to assess the probability that two proteins with that pair of domains will interact. We apply our method to the Homo sapiens-Plasmodium falciparum host-pathogen system. Our system predicts 516 PPIs between proteins from these two organisms. We show that pairs of human proteins we predict to interact with the same Plasmodium protein are close to each other in the human PPI network and that Plasmodium pairs predicted to interact with same human protein are co-expressed in DNA microarray datasets measured during various stages of the Plasmodium life cycle. Finally, we identify functionally enriched sub-networks spanned by the predicted interactions and discuss the plausibility of our predictions. AVAILABILITY: Supplementary data are available at http://staff.vbi.vt.edu/dyermd/publications/dyer2007a.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Computational verification of protein-protein interactions by orthologous co-expression

Background  

High-throughput methods identify an overwhelming number of protein-protein interactions. However, the limited accuracy of these methods results in the false identification of many spurious interactions. Accordingly, the resulting interactions are regarded as hypothetical and computational methods are needed to increase their confidence. Several methods have recently been suggested for this purpose including co-expression as a confidence measure for interacting proteins, but their performance is still quite poor.     

Mean-field analysis of protein-protein interactions

Calculations were performed on the D1.3-E5.2 antibody-antibody complex estimating the binding affinities of the wild-type and 16 alanine substitutions. Analyzed were structural models of the interfacial region containing a zinc ion and crystallographic waters. A continuum approach was used to evaluate the electrostatic free energies and the hydrophobic effect was calculated by employing a buried molecular surface area relationship. Estimates of the absolute binding affinity reproduced the experimental value within the uncertainty of assessing entropic and strain energy contributions. The best correlation for mutants with experimental data was achieved when the hydrophilicity of created cavities were considered, and yielded a correlation coefficient of 0.7 and an average error of +/-1.4 kcal/mol. Empirically fitting the free energy function produced a smaller error of +/-1.0 kcal/mol. Depending on the electrical potential and electrostatic reorganization, scaling the 'protein dielectric constant' to approximately 10 may improve the accuracy of continuum models for evaluating amino acid substitutions.     

Prediction of protein-protein interactions by docking methods

Recently, developments have been made in predicting the structure of docked complexes when the coordinates of the components are known. The process generally consists of a stage during which the components are combined rigidly and then a refinement stage. Several rapid new algorithms have been introduced in the rigid docking problem and promising refinement techniques have been developed, based on modified molecular mechanics force fields and empirical measures of desolvation, combined with minimisations that switch on the short-range interactions gradually. There has also been progress in developing a benchmark set of targets for docking and a blind trial, similar to the trials of protein structure prediction, has taken place.     

Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners

Recent advances in high-throughput experimental methods for the identification of protein interactions have resulted in a large amount of diverse data that are somewhat incomplete and contradictory. As valuable as they are, such experimental approaches studying protein interactomes have certain limitations that can be complemented by the computational methods for predicting protein interactions. In this review we describe different approaches to predict protein interaction partners as well as highlight recent achievements in the prediction of specific domains mediating protein-protein interactions. We discuss the applicability of computational methods to different types of prediction problems and point out limitations common to all of them.     

Mapping protein-protein interactions with combinatorial biology methods

Extremely diverse, DNA-encoded libraries of peptides and proteins have been constructed that include a linkage between each polypeptide and the encoding DNA. Library members can be selected by virtue of a particular binding specificity, and their protein sequence can be deduced from the sequence of the cognate DNA. Such combinatorial biology methods have proven invaluable in both identifying natural protein-protein interactions and also in mapping the specificities and energetics of these interactions in fine detail.     

Chromatographic methods to study protein-protein interactions.

Proteins and enzymes are now generally thought to be organized within the cell to form clusters in a dynamic and versatile way, and heterologous protein-protein interactions are believed to be involved in virtually all cellular events. Therefore we need appropriate tools to detect and study such interactions. Chromatographic techniques prove to be well suited for this kind of investigation. Real complexes formed between proteins can be studied by classic gel filtration. When enzymes are studied, active enzyme gel chromatography is a useful alternative. A variant of classic gel filtration is gel filtration equilibrium analysis, which is similar to equilibrium dialysis. When the association formed is only dynamic and equilibrates very rapidly, either the Hummel-Dryer method of equilibrium gel filtration or large-zone equilibrium filtration sometimes allows the interactions to be analyzed, both qualitatively and quantitatively. Very often, however, interactions between enzymes and proteins can only be evidenced in vitro in media that mimic the intracellular situation. Immobilized proteins are excellent tools for this type of research. Several examples are indeed known where the immobilization of an enzyme on a solid support does not affect its real properties, but rather changes its environment in such a way that the diffusion becomes limiting. Affinity chromatography using immobilized proteins allows the analysis of heterologous protein-protein interactions, both qualitatively and quantitatively. A useful alternative appears to be affinity electrophoresis. The latter technique, however, is exclusively qualitative. All these techniques are described and illustrated with examples taken from the literature.     

Electrophoretic methods for studying protein-protein interactions.

Protein-protein interactions are involved in many biological processes ranging from DNA replication, to signal transduction, to metabolism control, to viral assembly. The understanding of those interactions would allow the effective design of new drugs and further manipulation of those interactions. Several useful analytical methods are available for the study of protein-protein binding, and among them, electrophoresis is commonly used. We describe two types of electrophoresis: gel electrophoresis and capillary electrophoresis. Gel electrophoresis is a well-established method used to study protein-protein interactions and includes overlay gel electrophoresis, charge shift method, band shift assay, countermigration electrophoresis, affinophoresis, affinity electrophoresis, rocket immunoelectrophoresis, and crossed immunoelectrophoresis. These techniques are briefly described along with their advantages and limitations. Capillary electrophoresis, on the other hand, is a relatively new method and affinity capillary electrophoresis has demonstrated its value in the measurement of binding constants, the estimation of kinetic rate constants, and the determination of stoichiometry of biomolecular interactions. It offers short analysis time, requires minute amounts of protein samples, usually involves no radiolabeled compounds, and, most importantly, is carried out in solution. We summarize the principles of affinity capillary electrophoresis for studying protein-protein interactions along with current limitations and describe in depth its application to the determination of stoichiometries of tight and weak binding protein-protein interactions. The protocol presented in the experimental section details the use of affinity capillary electrophoresis for the determination of stoichiometry of protein complexes.     

Computational methods for prediction of protein-RNA interactions

Understanding the molecular mechanism of protein-RNA recognition and complex formation is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes by X-ray crystallography and nuclear magnetic resonance spectroscopy (NMR) is tedious and difficult. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental observations, computational predictions can be sufficiently accurate to prompt functional hypotheses and guide experiments, e.g. to identify individual amino acid or nucleotide residues. In this article we review 10 methods for predicting protein-RNA interactions, seven of which predict RNA-binding sites from protein sequences and three from structures. We also developed a meta-predictor that uses the output of top three sequence-based primary predictors to calculate a consensus prediction, which outperforms all the primary predictors. In order to fully cover the software for predicting protein-RNA interactions, we also describe five methods for protein-RNA docking. The article highlights the strengths and shortcomings of existing methods for the prediction of protein-RNA interactions and provides suggestions for their further development.     

Computational framework for analysis of prey-prey associations in interaction proteomics identifies novel human protein-protein interactions and networks

Large-scale protein-protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific "bait" protein and its associated "prey" proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait-prey and cocomplexed preys (prey-prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein-protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait-prey and prey-prey interactions can be used to refine network topology and extend known protein networks.     

Co-evolutionary analysis reveals insights into protein-protein interactions

Protein-protein interactions play crucial roles in biological processes. Experimental methods have been developed to survey the proteome for interacting partners and some computational approaches have been developed to extend the impact of these experimental methods. Computational methods are routinely applied to newly discovered genes to infer protein function and plausible protein-protein interactions. Here, we develop and extend a quantitative method that identifies interacting proteins based upon the correlated behavior of the evolutionary histories of protein ligands and their receptors. We have studied six families of ligand-receptor pairs including: the syntaxin/Unc-18 family, the GPCR/G-alpha's, the TGF-beta/TGF-beta receptor system, the immunity/colicin domain collection from bacteria, the chemokine/chemokine receptors, and the VEGF/VEGF receptor family. For correlation scores above a defined threshold, we were able to find an average of 79% of all known binding partners. We then applied this method to find plausible binding partners for proteins with uncharacterized binding specificities in the syntaxin/Unc-18 protein and TGF-beta/TGF-beta receptor families. Analysis of the results shows that co-evolutionary analysis of interacting protein families can reduce the search space for identifying binding partners by not only finding binding partners for uncharacterized proteins but also recognizing potentially new binding partners for previously characterized proteins. We believe that correlated evolutionary histories provide a route to exploit the wealth of whole genome sequences and recent systematic proteomic results to extend the impact of these studies and focus experimental efforts to categorize physiologically or pathologically relevant protein-protein interactions.     

Ensemble non-negative matrix factorization methods for clustering protein-protein interactions

MOTIVATION: When working with large-scale protein interaction data, an important analysis task is the assignment of pairs of proteins to groups that correspond to higher order assemblies. Previously a common approach to this problem has been to apply standard hierarchical clustering methods to identify such a groups. Here we propose a new algorithm for aggregating a diverse collection of matrix factorizations to produce a more informative clustering, which takes the form of a 'soft' hierarchy of clusters. RESULTS: We apply the proposed Ensemble non-negative matrix factorization (NMF) algorithm to a high-quality assembly of binary protein interactions derived from two proteome-wide studies in yeast. Our experimental evaluation demonstrates that the algorithm lends itself to discovering small localized structures in this data, which correspond to known functional groupings of complexes. In addition, we show that the algorithm also supports the assignment of putative functions for previously uncharacterized proteins, for instance the protein YNR024W, which may be an uncharacterized component of the exosome.     

大规模蛋白质相互作用研究方法进展

随着2000年酵母大规模蛋白质相互作用网络图谱的成功描绘,蛋白质相互作用特别是大规模蛋白质相互作用研究成为生命科学领域的又一个研究热点。酵母、果蝇、线虫以及人类蛋白的大规模相互作用图谱的相继完成,不仅对系统研究细胞内各种生命活动有着重要意义,也标志着蛋白质相互作用研究方法的不断发展和完善。本文综述了当前大规模研究蛋白质相互作用的技术方法并进行了比较分析。每种技术都有其各自的优缺点,在实验中要根据不同的要求和目的选择适宜的方法。     

Negation of protein-protein interactions: analysis and extraction

MOTIVATION: Negative information about protein-protein interactions--from uncertainty about the occurrence of an interaction to knowledge that it did not occur--is often of great use to biologists and could lead to important discoveries. Yet, to our knowledge, no proposals focusing on extracting such information have been proposed in the text mining literature. RESULTS: In this work, we present an analysis of the types of negative information that is reported, and a heuristic-based system using a full dependency parser to extract such information. We performed a preliminary evaluation study that shows encouraging results of our system. Finally, we have obtained an initial corpus of negative protein-protein interactions as basis for the construction of larger ones. AVAILABILITY: The corpus is available by request from the authors.     

Computational prediction of protein-protein complexes

ABSTRACT: BACKGROUND: Protein-protein interactions form the core of several biological processes. With protein-protein interfaces being considered as drug targets, studies on their interactions and molecular mechanisms are gaining ground. As the number of protein complexes in databases is scarce as compared to a spectrum of independent protein molecules, computational approaches are being considered for speedier model derivation and assessment of a plausible complex. In this study, a good approach towards in silico generation of protein-protein heterocomplex and identification of the most probable complex among thousands of complexes thus generated is documented. This approach becomes even more useful in the event of little or no binding site information between the interacting protein molecules. FINDINGS: A plausible protein-protein hetero-complex was fished out from 10 docked complexes which are a representative set of complexes obtained after clustering of 2000 generated complexes using protein-protein docking softwares. The interfacial area for this complex was predicted by two hotspot prediction programs employing different algorithms. Further, this complex had the lowest energy and most buried surface area of all the complexes with the same interfacial residues. CONCLUSIONS: For the generation of a plausible protein heterocomplex, various software tools were employed. Prominent are the protein-protein docking methods, prediction of 'hotspots' which are the amino acid residues likely to be in an interface and measurement of buried surface area of the complexes. Consensus generated in their predictions lends credence to the use of the various softwares used.     

Computational methods for the prediction of protein interactions

Establishing protein interaction networks is crucial for understanding cellular operations. Detailed knowledge of the 'interactome', the full network of protein-protein interactions, in model cellular systems should provide new insights into the structure and properties of these systems. Parallel to the first massive application of experimental techniques to the determination of protein interaction networks and protein complexes, the first computational methods, based on sequence and genomic information, have emerged.