Prediction of protein-protein interactions using distant conservation of sequence patterns and structure relationships

MOTIVATION: Given that association and dissociation of protein molecules is crucial in most biological processes several in silico methods have been recently developed to predict protein-protein interactions. Structural evidence has shown that usually interacting pairs of close homologs (interologs) physically interact in the same way. Moreover, conservation of an interaction depends on the conservation of the interface between interacting partners. In this article we make use of both, structural similarities among domains of known interacting proteins found in the Database of Interacting Proteins (DIP) and conservation of pairs of sequence patches involved in protein-protein interfaces to predict putative protein interaction pairs. RESULTS: We have obtained a large amount of putative protein-protein interaction (approximately 130,000). The list is independent from other techniques both experimental and theoretical. We separated the list of predictions into three sets according to their relationship with known interacting proteins found in DIP. For each set, only a small fraction of the predicted protein pairs could be independently validated by cross checking with the Human Protein Reference Database (HPRD). The fraction of validated protein pairs was always larger than that expected by using random protein pairs. Furthermore, a correlation map of interacting protein pairs was calculated with respect to molecular function, as defined in the Gene Ontology database. It shows good consistency of the predicted interactions with data in the HPRD database. The intersection between the lists of interactions of other methods and ours produces a network of potentially high-confidence interactions.     

Interactome-Wide Prediction of Protein-Protein Binding Sites Reveals Effects of Protein Sequence Variation in Arabidopsis thaliana

The specificity of protein-protein interactions is encoded in those parts of the sequence that compose the binding interface. Therefore, understanding how changes in protein sequence influence interaction specificity, and possibly the phenotype, requires knowing the location of binding sites in those sequences. However, large-scale detection of protein interfaces remains a challenge. Here, we present a sequence- and interactome-based approach to mine interaction motifs from the recently published Arabidopsis thaliana interactome. The resultant proteome-wide predictions are available via www.ab.wur.nl/sliderbio and set the stage for further investigations of protein-protein binding sites. To assess our method, we first show that, by using a priori information calculated from protein sequences, such as evolutionary conservation and residue surface accessibility, we improve the performance of interface prediction compared to using only interactome data. Next, we present evidence for the functional importance of the predicted sites, which are under stronger selective pressure than the rest of protein sequence. We also observe a tendency for compensatory mutations in the binding sites of interacting proteins. Subsequently, we interrogated the interactome data to formulate testable hypotheses for the molecular mechanisms underlying effects of protein sequence mutations. Examples include proteins relevant for various developmental processes. Finally, we observed, by analysing pairs of paralogs, a correlation between functional divergence and sequence divergence in interaction sites. This analysis suggests that large-scale prediction of binding sites can cast light on evolutionary processes that shape protein-protein interaction networks.     

Mapping of protein-protein interaction sites by the 'absence of interference' approach

Protein-protein interactions are critical to most biological processes, and locating protein-protein interfaces on protein structures is an important task in molecular biology. We developed a new experimental strategy called the ‘absence of interference’ approach to determine surface residues involved in protein-protein interaction of established yeast two-hybrid pairs of interacting proteins. One of the proteins is subjected to high-level randomization by error-prone PCR. The resulting library is selected by yeast two-hybrid system for interacting clones that are isolated and sequenced. The interaction region can be identified by an absence or depletion of mutations. For data analysis and presentation, we developed a Web interface that analyzes the mutational spectrum and displays the mutational frequency on the surface of the structure (or a structural model) of the randomized protein†. Additionally, this interface might be of use for the display of mutational distributions determined by other types of random mutagenesis experiments. We applied the approach to map the interface of the catalytic domain of the DNA methyltransferase Dnmt3a with its regulatory factor Dnmt3L. Dnmt3a was randomized with high mutational load. A total of 76 interacting clones were isolated and sequenced, and 648 mutations were identified. The mutational pattern allowed to identify a unique interaction region on the surface of Dnmt3a, which comprises about 500-600 Å². The results were confirmed by site-directed mutagenesis and structural analysis. The absence-of-interference approach will allow high-throughput mapping of protein interaction sites suitable for functional studies and protein docking.     

Predicting binding sites of hydrolase-inhibitor complexes by combining several methods

Background

Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks.

Results

In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods.

Conclusions

We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems.     

Computational design of a new hydrogen bond network and at least a 300-fold specificity switch at a protein-protein interface

The redesign of protein-protein interactions is a stringent test of our understanding of molecular recognition and specificity. Previously we engineered a modest specificity switch into the colicin E7 DNase-Im7 immunity protein complex by identifying mutations that are disruptive in the native complex, but can be compensated by mutations on the interacting partner. Here we extend the approach by systematically sampling alternate rigid body orientations to optimize the interactions in a binding mode specific manner. Using this protocol we designed a de novo hydrogen bond network at the DNase-immunity protein interface and confirmed the design with X-ray crystallographic analysis. Subsequent design of the second shell of interactions guided by insights from the crystal structure on tightly bound water molecules, conformational strain, and packing defects yielded new binding partners that exhibited specificities of at least 300-fold between the cognate and the non-cognate complexes. This multi-step approach should be applicable to the design of polar protein-protein interactions and contribute to the re-engineering of regulatory networks mediated by protein-protein interactions.     

Subcellular localization of interacting proteins by bimolecular fluorescence complementation in planta

Bimolecular fluorescence complementation (BiFC) represents one of the most advanced and powerful tools for studying and visualizing protein-protein interactions in living cells. In this method, putative interacting protein partners are fused to complementary non-fluorescent fragments of an autofluorescent protein, such as the yellow spectral variant of the green fluorescent protein. Interaction of the test proteins may result in reconstruction of fluorescence if the two portions of yellow spectral variant of the green fluorescent protein are brought together in such a way that they can fold properly. BiFC provides an assay for detection of protein-protein interactions, and for the subcellular localization of the interacting protein partners. To facilitate the application of BiFC to plant research, we designed a series of vectors for easy construction of N-terminal and C-terminal fusions of the target protein to the yellow spectral variant of the green fluorescent protein fragments. These vectors carry constitutive expression cassettes with an expanded multi-cloning site. In addition, these vectors facilitate the assembly of BiFC expression cassettes into Agrobacterium multi-gene expression binary plasmids for co-expression of interacting partners and additional autofluorescent proteins that may serve as internal transformation controls and markers of subcellular compartments. We demonstrate the utility of these vectors for the analysis of specific protein-protein interactions in various cellular compartments, including the nucleus, plasmodesmata, and chloroplasts of different plant species and cell types.     

Analyzing protein-protein interactions in cell membranes

Interactions among membrane proteins regulate numerous cellular processes, including cell growth, cell differentiation and apoptosis. We need to understand which proteins interact, where they interact and to which extent they interact. This article describes a set of novel approaches to measure, on the surface of living cells, the number of clusters of proteins, the number of proteins per cluster, the number of clusters or membrane domains that contain pairs of interacting proteins and the fraction of one protein species that interacts with another protein within these domains. These data can then be interpreted in terms of the function of the protein-protein interactions.     

Detecting mutually exclusive interactions in protein-protein interaction maps

Comprehensive protein interaction maps can complement genetic and biochemical experiments and allow the formulation of new hypotheses to be tested in the system of interest. The computational analysis of the maps may help to focus on interesting cases and thereby to appropriately prioritize the validation experiments. We show here that, by automatically comparing and analyzing structurally similar regions of proteins of known structure interacting with a common partner, it is possible to identify mutually exclusive interactions present in the maps with a sensitivity of 70% and a specificity higher than 85% and that, in about three fourth of the correctly identified complexes, we also correctly recognize at least one residue (five on average) belonging to the interaction interface. Given the present and continuously increasing number of proteins of known structure, the requirement of the knowledge of the structure of the interacting proteins does not substantially impact on the coverage of our strategy that can be estimated to be around 25%. We also introduce here the Estrella server that embodies this strategy, is designed for users interested in validating specific hypotheses about the functional role of a protein-protein interaction and it also allows access to pre-computed data for seven organisms.     

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.     

ISIS: interaction sites identified from sequence

MOTIVATION: Large-scale experiments reveal pairs of interacting proteins but leave the residues involved in the interactions unknown. These interface residues are essential for understanding the mechanism of interaction and are often desired drug targets. Reliable identification of residues that reside in protein-protein interface typically requires analysis of protein structure. Therefore, for the vast majority of proteins, for which there is no high-resolution structure, there is no effective way of identifying interface residues. RESULTS: Here we present a machine learning-based method that identifies interacting residues from sequence alone. Although the method is developed using transient protein-protein interfaces from complexes of experimentally known 3D structures, it never explicitly uses 3D information. Instead, we combine predicted structural features with evolutionary information. The strongest predictions of the method reached over 90% accuracy in a cross-validation experiment. Our results suggest that despite the significant diversity in the nature of protein-protein interactions, they all share common basic principles and that these principles are identifiable from sequence alone.     

How reliable are experimental protein-protein interaction data?

Data of protein-protein interactions provide valuable insight into the molecular networks underlying a living cell. However, their accuracy is often questioned, calling for a rigorous assessment of their reliability. The computation offered here provides an intelligible mean to assess directly the rate of true positives in a data set of experimentally determined interacting protein pairs. We show that the reliability of high-throughput yeast two-hybrid assays is about 50%, and that the size of the yeast interactome is estimated to be 10,000-16,600 interactions.     

Can simple codon pair usage predict protein-protein interaction?

Deciphering functional interactions between proteins is one of the great challenges in biology. Sequence-based homology-free encoding schemes have been increasingly applied to develop promising protein-protein interaction (PPI) predictors by means of statistical or machine learning methods. Here we analyze the relationship between codon pair usage and PPIs in yeast. We show that codon pair usage of interacting protein pairs differs significantly from randomly expected. This motivates the development of a novel approach for predicting PPIs, with codon pair frequency difference as input to a Support Vector Machine predictor, termed as CCPPI. 10-fold cross-validation tests based on yeast PPI datasets with balanced positive-to-negative ratios indicate that CCPPI performs better than other sequence-based encoding schemes. Moreover, it ranks the best when tested on an unbalanced large-scale dataset. Although CCPPI is subjected to high false positive rates like many PPI predictors, statistical analyses of the predicted true positives confirm that the success of CCPPI is partly ascribed to its capability to capture proteomic co-expression and functional similarities between interacting protein pairs. Our findings suggest that codon pairs of interacting protein pairs evolve in a coordinated manner and consequently they provide additional information beyond amino acids-based encoding schemes. CCPPI has been made freely available at: http://protein.cau.edu.cn/ccppi.     

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.     

The origins of specificity in polyketide synthase protein interactions

Polyketides, a diverse group of heteropolymers with antibiotic and antitumor properties, are assembled in bacteria by multiprotein chains of modular polyketide synthase (PKS) proteins. Specific protein-protein interactions determine the order of proteins within a multiprotein chain, and thereby the order in which chemically distinct monomers are added to the growing polyketide product. Here we investigate the evolutionary and molecular origins of protein interaction specificity. We focus on the short, conserved N- and C-terminal docking domains that mediate interactions between modular PKS proteins. Our computational analysis, which combines protein sequence data with experimental protein interaction data, reveals a hierarchical interaction specificity code. PKS docking domains are descended from a single ancestral interacting pair, but have split into three phylogenetic classes that are mutually noninteracting. Specificity within one such compatibility class is determined by a few key residues, which can be used to define compatibility subclasses. We identify these residues using a novel, highly sensitive co-evolution detection algorithm called CRoSS (correlated residues of statistical significance). The residue pairs selected by CRoSS are involved in direct physical interactions in a docked-domain NMR structure. A single PKS system can use docking domain pairs from multiple classes, as well as domain pairs from multiple subclasses of any given class. The termini of individual proteins are frequently shuffled, but docking domain pairs straddling two interacting proteins are linked as an evolutionary module. The hierarchical and modular organization of the specificity code is intimately related to the processes by which bacteria generate new PKS pathways.     

A quantitative analysis of interfacial amino acid conservation in protein-protein hetero complexes

A long-standing question in molecular biology is whether interfaces of protein-protein complexes are more conserved than the rest of the protein surfaces. Although it has been reported that conservation can be used as an indicator for predicting interaction sites on proteins, there are recent reports stating that the interface regions are only slightly more conserved than the rest of the protein surfaces, with conservation signals not being statistically significant enough for predicting protein-protein binding sites. In order to properly address these controversial reports we have studied a set of 28 well resolved hetero complex structures of proteins that consists of transient and non-transient complexes. The surface positions were classified into four conservation classes and the conservation index of the surface positions was quantitatively analyzed. The results indicate that the surface density of highly conserved positions is significantly higher in the protein-protein interface regions compared with the other regions of the protein surface. However, the average conservation index of the patches in the interface region is not significantly higher compared with other surface regions of the protein structures. This finding demonstrates that the number of conserved residue positions is a more appropriate indicator for predicting protein-protein binding sites than the average conservation index in the interacting region. We have further validated our findings on a set of 59 benchmark complex structures. Furthermore, an analysis of 19 complexes of antigen-antibody interactions shows that there is no conservation of amino acid positions in the interacting regions of these complexes, as expected, with the variable region of the immunoglobulins interacting mostly with the antigens. Interestingly, antigen interacting regions also have a higher number of non-conserved residue positions in the interacting region than the rest of the protein surface.     

Non-Redundant Unique Interface Structures as Templates for Modeling Protein Interactions

Improvements in experimental techniques increasingly provide structural data relating to protein-protein interactions. Classification of structural details of protein-protein interactions can provide valuable insights for modeling and abstracting design principles. Here, we aim to cluster protein-protein interactions by their interface structures, and to exploit these clusters to obtain and study shared and distinct protein binding sites. We find that there are 22604 unique interface structures in the PDB. These unique interfaces, which provide a rich resource of structural data of protein-protein interactions, can be used for template-based docking. We test the specificity of these non-redundant unique interface structures by finding protein pairs which have multiple binding sites. We suggest that residues with more than 40% relative accessible surface area should be considered as surface residues in template-based docking studies. This comprehensive study of protein interface structures can serve as a resource for the community. The dataset can be accessed at http://prism.ccbb.ku.edu.tr/piface.     

PPI_SVM: Prediction of protein-protein interactions using machine learning,domain-domain affinities and frequency tables

Protein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: .     

Predicting Protein-Protein Interaction by the Mirrortree Method: Possibilities and Limitations

Molecular co-evolution analysis as a sequence-only based method has been used to predict protein-protein interactions. In co-evolution analysis, Pearson''s correlation within the mirrortree method is a well-known way of quantifying the correlation between protein pairs. Here we studied the mirrortree method on both known interacting protein pairs and sets of presumed non-interacting protein pairs, to evaluate the utility of this correlation analysis method for predicting protein-protein interactions within eukaryotes. We varied metrics for computing evolutionary distance and evolutionary span of the species analyzed. We found the differences between co-evolutionary correlation scores of the interacting and non-interacting proteins, normalized for evolutionary span, to be significantly predictive for proteins conserved over a wide range of eukaryotic clades (from mammals to fungi). On the other hand, for narrower ranges of evolutionary span, the predictive power was much weaker.     

High-precision FLIM-FRET in fixed and living cells reveals heterogeneity in a simple CFP-YFP fusion protein

We have used widefield photon-counting FLIM to study FRET in fixed and living cells using control FRET pairs. We have studied fixed mammalian cells expressing either cyan fluorescent protein (CFP) or a fusion of CFP and yellow fluorescent protein (YFP), and living fungal cells expressing either Cerulean or a Cerulean-Venus fusion protein. We have found the fluorescence behaviour to be essentially identical in the mammalian and fungal cells. Importantly, the high-precision FLIM data is able to reproducibly resolve multiple fluorescence decays, thereby revealing new information about the fraction of the protein population that undergoes FRET and reducing error in the measurement of donor-acceptor distances. Our results for this simple control system indicate that the in vivo FLIM-FRET studies of more complex protein-protein interactions would benefit greatly from such quantitative measurements.