Prediction of protein-protein interactions by combining structure and sequence conservation in protein interfaces

MOTIVATION: Elucidation of the full network of protein-protein interactions is crucial for understanding of the principles of biological systems and processes. Thus, there is a need for in silico methods for predicting interactions. We present a novel algorithm for automated prediction of protein-protein interactions that employs a unique bottom-up approach combining structure and sequence conservation in protein interfaces. RESULTS: Running the algorithm on a template dataset of 67 interfaces and a sequentially non-redundant dataset of 6170 protein structures, 62 616 potential interactions are predicted. These interactions are compared with the ones in two publicly available interaction databases (Database of Interacting Proteins and Biomolecular Interaction Network Database) and also the Protein Data Bank. A significant number of predictions are verified in these databases. The unverified ones may correspond to (1) interactions that are not covered in these databases but known in literature, (2) unknown interactions that actually occur in nature and (3) interactions that do not occur naturally but may possibly be realized synthetically in laboratory conditions. Some unverified interactions, supported significantly with studies found in the literature, are discussed. AVAILABILITY: http://gordion.hpc.eng.ku.edu.tr/prism CONTACT: agursoy@ku.edu.tr; okeskin@ku.edu.tr.     

Alignment of non-covalent interactions at protein-protein interfaces

Background

The study and comparison of protein-protein interfaces is essential for the understanding of the mechanisms of interaction between proteins. While there are many methods for comparing protein structures and protein binding sites, so far no methods have been reported for comparing the geometry of non-covalent interactions occurring at protein-protein interfaces.

Methodology/Principal Findings

Here we present a method for aligning non-covalent interactions between different protein-protein interfaces. The method aligns the vector representations of van der Waals interactions and hydrogen bonds based on their geometry. The method has been applied to a dataset which comprises a variety of protein-protein interfaces. The alignments are consistent to a large extent with the results obtained using two other complementary approaches. In addition, we apply the method to three examples of protein mimicry. The method successfully aligns respective interfaces and allows for recognizing conserved interface regions.

Conclusions/Significance

The Galinter method has been validated in the comparison of interfaces in which homologous subunits are involved, including cases of mimicry. The method is also applicable to comparing interfaces involving non-peptidic compounds. Galinter assists users in identifying local interface regions with similar patterns of non-covalent interactions. This is particularly relevant to the investigation of the molecular basis of interaction mimicry.     

Prediction of protein-protein interactions using protein signature profiling

Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain interactions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as "interacting". In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis elegans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area under the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs increased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on average 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.     

Oligomeric protein structure networks: insights into protein-protein interactions

Background  

Protein-protein association is essential for a variety of cellular processes and hence a large number of investigations are being carried out to understand the principles of protein-protein interactions. In this study, oligomeric protein structures are viewed from a network perspective to obtain new insights into protein association. Structure graphs of proteins have been constructed from a non-redundant set of protein oligomer crystal structures by considering amino acid residues as nodes and the edges are based on the strength of the non-covalent interactions between the residues. The analysis of such networks has been carried out in terms of amino acid clusters and hubs (highly connected residues) with special emphasis to protein interfaces.     

Prediction of physical protein-protein interactions

Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. In recent years, new experimental techniques have been developed to discover the protein-protein interaction networks of several organisms. However, the accuracy and coverage of these techniques have proven to be limited, and computational approaches remain essential both to assist in the design and validation of experimental studies and for the prediction of interaction partners and detailed structures of protein complexes. Here, we provide a critical overview of existing structure-independent and structure-based computational methods. Although these techniques have significantly advanced in the past few years, we find that most of them are still in their infancy. We also provide an overview of experimental techniques for the detection of protein-protein interactions. Although the developments are promising, false positive and false negative results are common, and reliable detection is possible only by taking a consensus of different experimental approaches. The shortcomings of experimental techniques affect both the further development and the fair evaluation of computational prediction methods. For an adequate comparative evaluation of prediction and high-throughput experimental methods, an appropriately large benchmark set of biophysically characterized protein complexes would be needed, but is sorely lacking.     

Stereochemical metrics of lectin-carbohydrate interactions: comparison with protein-protein interfaces

A global census of stereochemical metrics including interface size, hydropathy, amino acid propensities, packing and hydrogen bonding was carried out on 32 x-ray-elucidated structures of lectin-carbohydrate complexes covering eight different lectin families. It is shown that the interactions at primary binding subsites are more efficient than at other subsites. Another salient behavior found for primary subsites was a marked negative correlation between the interface size and the polar surface content. It is noteworthy that this demographic rule is delineated by lectins with unrelated phylogenetic origin, indicating that independent interface architectures have evolved through common optimization paths. The structural properties of lectin-carbohydrate interfaces were compared with those characterizing a set of 32 protein homodimers. Overall, the analysis shows that the stereochemical bases of lectin-carbohydrate and protein-protein interfaces differ drastically from each other. In comparison with protein-protein complexes, lectin-carbohydrate interfaces have superior packing efficiency, better hydrogen bonding stereochemistry, and higher interaction cooperativity. A similar conclusion holds in the comparison with protein-protein heterocomplexes. We propose that the energetic consequence of this better interaction geometry is a larger decrease in free energy per unit of area buried, feature that enables lectins and carbohydrates to form stable complexes with relatively small interface areas. These observations lend support to the emerging notion that systems differing from each other in their stereochemical metrics may rely on different energetic bases.     

Secondary structure based analysis and classification of biological interfaces: identification of binding motifs in protein-protein interactions

MOTIVATION: The increasing amount of data on protein-protein interaction needs to be rationalized for deriving guidelines for the alteration or design of an interface between two proteins. RESULTS: We present a detaild structural analysis and comparison of homo- versus heterodimeric protein-protein interfaces. Regular secondary structures (helices and strands) are the main components of the former, whereas non-regular structures (turns, loops, etc.) frequently mediate interactions in the latter. Interface helices get longer with increasing interface area, but only in heterocomplexes. On average, the homodimers have longer helical segments and prominent helix-helix pairs. There is a surprising distinction in the relative orientation of interface helices, with a tendency for aligned packing in homodimers and a clear preference for packing at 90 degrees in heterodimers. Arg and the aromatic residues have a higher preference to occur in all secondary structural elements (SSEs) in the interface. Based on the dominant SSE, the interfaces have been grouped into four classes: alpha, beta, alphabeta and non-regular. Identity between protein and interface classes is the maximum for alpha proteins, but rather mediocre for the other protein classes. The interface classes of the two chains forming a heterodimer are often dissimilar. Eleven binding motifs can capture the prominent architectural features of most of the interfaces.     

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.     

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background  

Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (ΔΔG) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.     

Dissecting protein-protein interactions using directed evolution

Protein-protein interactions are essential for life. They are responsible for most cellular functions and when they go awry often lead to disease. Proteins are inherently complex. They are flexible macromolecules whose constituent amino acid components act in combinatorial and networked ways when they engage one another in binding interactions. It is just this complexity that allows them to conduct such a broad array of biological functions. Despite decades of intense study of the molecular basis of protein-protein interactions, key gaps in our understanding remain, hindering our ability to accurately predict the specificities and affinities of their interactions. Until recently, most protein-protein investigations have been probed experimentally at the single-amino acid level, making them, by definition, incapable of capturing the combinatorial nature of, and networked communications between, the numerous residues within and outside of the protein-protein interface. This aspect of protein-protein interactions, however, is emerging as a major driving force for protein affinity and specificity. Understanding a combinatorial process necessarily requires a combinatorial experimental tool. Much like the organisms in which they reside, proteins naturally evolve over time, through a combinatorial process of mutagenesis and selection, to functionally associate. Elucidating the process by which proteins have evolved may be one of the keys to deciphering the molecular rules that govern their interactions with one another. Directed evolution is a technique performed in the laboratory that mimics natural evolution on a tractable time scale that has been utilized widely to engineer proteins with novel capabilities, including altered binding properties. In this review, we discuss directed evolution as an emerging tool for dissecting protein-protein interactions.     

IDQD算法预测人类蛋白质相互作用

蛋白质是一切生命活动的物质基础,研究蛋白质的相互作用有助于理解生物过程的分子机制,阐明疾病的分子机理。本文依据蛋白质序列组分特征,应用基于多样性增量的二次判别分析方法,对人类的1 963对蛋白质相互作用进行了预测。自洽检验的各项预测指标均在79%以上,且交叉检验的总精度也大于60%,表明本算法可以用于蛋白质相互作用预测。     

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.     

A simple dependence between protein evolution rate and the number of protein-protein interactions

Background  

It has been shown for an evolutionarily distant genomic comparison that the number of protein-protein interactions a protein has correlates negatively with their rates of evolution. However, the generality of this observation has recently been challenged. Here we examine the problem using protein-protein interaction data from the yeast Saccharomyces cerevisiae and genome sequences from two other yeast species.     

Understanding mechanisms governing protein-protein interactions from synthetic binding interfaces

Recent advances in methodologies and design of combinatorial library selection have enabled comprehensive characterization of sequence space for protein-protein interaction interfaces and generation of fully synthetic binding interfaces. By exhaustively introducing and quantitatively analyzing mutations in natural interfaces, new insights into their molecular architecture and plasticity have emerged. Minimalist combinatorial libraries based on a restricted amino acid code have produced synthetic interfaces that rival natural ones using a different set of rules. A two amino acid code composed of just tyrosine and serine in the context of antibody CDR loops is sufficient to produce high affinity and specific interactions with different classes of protein targets. Structural analyses highlight the dominant role of Tyr in forming productive interactions and demonstrate the dominance of conformational diversity over chemical diversity in producing na?ve binding interfaces. Synthetic binding proteins are beginning to be used as a powerful crystallization tool to attack important structural biology problems that are recalcitrant to crystallization using traditional methods.     

Residue frequencies and pairing preferences at protein-protein interfaces

We used a nonredundant set of 621 protein-protein interfaces of known high-resolution structure to derive residue composition and residue-residue contact preferences. The residue composition at the interfaces, in entire proteins and in whole genomes correlates well, indicating the statistical strength of the data set. Differences between amino acid distributions were observed for interfaces with buried surface area of less than 1,000 A(2) versus interfaces with area of more than 5,000 A(2). Hydrophobic residues were abundant in large interfaces while polar residues were more abundant in small interfaces. The largest residue-residue preferences at the interface were recorded for interactions between pairs of large hydrophobic residues, such as Trp and Leu, and the smallest preferences for pairs of small residues, such as Gly and Ala. On average, contacts between pairs of hydrophobic and polar residues were unfavorable, and the charged residues tended to pair subject to charge complementarity, in agreement with previous reports. A bootstrap procedure, lacking from previous studies, was used for error estimation. It showed that the statistical errors in the set of pairing preferences are generally small; the average standard error is approximately 0.2, i.e., about 8% of the average value of the pairwise index (2.9). However, for a few pairs (e.g., Ser-Ser and Glu-Asp) the standard error is larger in magnitude than the pairing index, which makes it impossible to tell whether contact formation is favorable or unfavorable. The results are interpreted using physicochemical factors and their implications for the energetics of complex formation and for protein docking are discussed. Proteins 2001;43:89-102.     

CH...O hydrogen bonds at protein-protein interfaces

For the first time, a statistical potential has been developed to quantitatively describe the CH.O hydrogen bonding interaction at the protein-protein interface. The calculated energies of the CH.O pair interaction show a favorable valley at approximately 3.3 A, exhibiting a feature typical of an H-bond and similar to the ab initio quantum calculation result (Scheiner, S., Kar, T., and Gu, Y. (2001) J. Biol. Chem. 276, 9832-9837). The potentials have been applied to a set of 469 protein-protein complexes to calculate the contribution of different types of interactions to each protein complex: the average energy contribution of a conventional H-bond is approximately 30%; that of a CH.O H-bond is 17%; and that of a hydrophobic interaction is 50%. In some protein-protein complexes, the contribution of the CH.O H-bond can reach as high as approximately 40-50%, indicating the importance of the CH.O H-bond at the protein interface. At the interfaces of these complexes, C(alpha)H.O H-bonds frequently occur between adjacent strands in both parallel and antiparallel orientations, having the obvious structural motif of bifurcated H-bonds. Our study suggests that the weak CH.O H-bond makes an important contribution to the association and stability of protein complexes and needs more attention in protein-protein interaction studies.     

Predicting protein interaction sites: binding hot-spots in protein-protein and protein-ligand interfaces

MOTIVATION: Protein assemblies are currently poorly represented in structural databases and their structural elucidation is a key goal in biology. Here we analyse clefts in protein surfaces, likely to correspond to binding 'hot-spots', and rank them according to sequence conservation and simple measures of physical properties including hydrophobicity, desolvation, electrostatic and van der Waals potentials, to predict which are involved in binding in the native complex. RESULTS: The resulting differences between predicting binding-sites at protein-protein and protein-ligand interfaces are striking. There is a high level of prediction accuracy (< or =93%) for protein-ligand interactions, based on the following attributes: van der Waals potential, electrostatic potential, desolvation and surface conservation. Generally, the prediction accuracy for protein-protein interactions is lower, with the exception of enzymes. Our results show that the ease of cleft desolvation is strongly predictive of interfaces and strongly maintained across all classes of protein-binding interface.     

Highly specific protein-protein interactions, evolution and negative design

We consider highly specific protein-protein interactions in proteomes of simple model proteins. We are inspired by the work of Zarrinpar et al (2003 Nature 426 676). They took a binding domain in a signalling pathway in yeast and replaced it with domains of the same class but from different organisms. They found that the probability of a protein binding to a protein from the proteome of a different organism is rather high, around one half. We calculate the probability of a model protein from one proteome binding to the protein of a different proteome. These proteomes are obtained by sampling the space of functional proteomes uniformly. In agreement with Zarrinpar et al we find that the probability of a protein binding a protein from another proteome is rather high, of order one tenth. Our results, together with those of Zarrinpar et al, suggest that designing, say, a peptide to block or reconstitute a single signalling pathway, without affecting any other pathways, requires knowledge of all the partners of the class of binding domains the peptide is designed to mimic. This knowledge is required to use negative design to explicitly design out interactions of the peptide with proteins other than its target. We also found that patches that are required to bind with high specificity evolve more slowly than those that are required only to not bind to any other patch. This is consistent with some analysis of sequence data for proteins engaged in highly specific interactions.     

Prediction of protein-protein interactions between Ralstonia solanacearum and Arabidopsis thaliana

Ralstonia solanacearum is a devastating bacterial pathogen that has an unusually wide host range. R. solanacearum, together with Arabidopsis thaliana, has become a model system for studying the molecular basis of plant-pathogen interactions. Protein-protein interactions (PPIs) play a critical role in the infection process, and some PPIs can initiate a plant defense response. However, experimental investigations have rarely addressed such PPIs. Using two computational methods, the interolog and the domain-based methods, we predicted 3,074 potential PPIs between 119 R. solanacearum and 1,442 A. thaliana proteins. Interestingly, we found that the potential pathogen-targeted proteins are more important in the A. thaliana PPI network. To facilitate further studies, all predicted PPI data were compiled into a database server called PPIRA (http://protein.cau.edu.cn/ppira/). We hope that our work will provide new insights for future research addressing the pathogenesis of R. solanacearum.