Protein–Protein Docking: Past,Present, and Future

The Protein Journal - The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions...     

Protein–Protein and Protein–Membrane Associations in the Lignin Pathway

Supramolecular organization of enzymes is proposed to orchestrate metabolic complexity and help channel intermediates in different pathways. Phenylpropanoid metabolism has to direct up to 30% of the carbon fixed by plants to the biosynthesis of lignin precursors. Effective coupling of the enzymes in the pathway thus seems to be required. Subcellular localization, mobility, protein–protein, and protein–membrane interactions of four consecutive enzymes around the main branch point leading to lignin precursors was investigated in leaf tissues of Nicotiana benthamiana and cells of Arabidopsis thaliana. CYP73A5 and CYP98A3, the two Arabidopsis cytochrome P450s (P450s) catalyzing para- and meta-hydroxylations of the phenolic ring of monolignols were found to colocalize in the endoplasmic reticulum (ER) and to form homo- and heteromers. They moved along with the fast remodeling plant ER, but their lateral diffusion on the ER surface was restricted, likely due to association with other ER proteins. The connecting soluble enzyme hydroxycinnamoyltransferase (HCT), was found partially associated with the ER. Both HCT and the 4-coumaroyl-CoA ligase relocalized closer to the membrane upon P450 expression. Fluorescence lifetime imaging microscopy supports P450 colocalization and interaction with the soluble proteins, enhanced by the expression of the partner proteins. Protein relocalization was further enhanced in tissues undergoing wound repair. CYP98A3 was the most effective in driving protein association.     

Characterization of Protein–Protein Interfaces

We analyze the characteristics of protein–protein interfaces using the largest datasets available from the Protein Data Bank (PDB). We start with a comparison of interfaces with protein cores and non-interface surfaces. The results show that interfaces differ from protein cores and non-interface surfaces in residue composition, sequence entropy, and secondary structure. Since interfaces, protein cores, and non-interface surfaces have different solvent accessibilities, it is important to investigate whether the observed differences are due to the differences in solvent accessibility or differences in functionality. We separate out the effect of solvent accessibility by comparing interfaces with a set of residues having the same solvent accessibility as the interfaces. This strategy reveals residue distribution propensities that are not observable by comparing interfaces with protein cores and non-interface surfaces. Our conclusions are that there are larger numbers of hydrophobic residues, particularly aromatic residues, in interfaces, and the interactions apparently favored in interfaces include the opposite charge pairs and hydrophobic pairs. Surprisingly, Pro-Trp pairs are over represented in interfaces, presumably because of favorable geometries. The analysis is repeated using three datasets having different constraints on sequence similarity and structure quality. Consistent results are obtained across these datasets. We have also investigated separately the characteristics of heteromeric interfaces and homomeric interfaces.     

Computational Prediction of Protein–Protein Interactions

Recently a number of computational approaches have been developed for the prediction of protein–protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.     

Predicting Protein–Protein Interfaces that Bind Intrinsically Disordered Protein Regions

A long-standing goal in biology is the complete annotation of function and structure on all protein–protein interactions, a large fraction of which is mediated by intrinsically disordered protein regions (IDRs). However, knowledge derived from experimental structures of such protein complexes is disproportionately small due, in part, to challenges in studying interactions of IDRs. Here, we introduce IDRBind, a computational method that by combining gradient boosted trees and conditional random field models predicts binding sites of IDRs with performance approaching state-of-the-art globular interface predictions, making it suitable for proteome-wide applications. Although designed and trained with a focus on molecular recognition features, which are long interaction-mediating-elements in IDRs, IDRBind also predicts the binding sites of short peptides more accurately than existing specialized predictors. Consistent with IDRBind's specificity, a comparison of protein interface categories uncovered uniform trends in multiple physicochemical properties, positioning molecular recognition feature interfaces between peptide and globular interfaces.     

Protein–Protein Interactions in DNA Base Excision Repair

The system of base excision repair (BER) ensures correction of the most abundant DNA damages in mammalian cells and plays an important role in maintaining genome stability. Enzymes and protein factors participate in the multistage BER in a coordinated fashion, which ensures repair efficiency. The suggested coordination mechanisms are based on formation of protein complexes stabilized via either direct or indirect DNA-mediated interactions. The results of investigation of direct interactions of the proteins participating in BER with each other and with other proteins are outlined in this review. The known protein partners and sites responsible for their interaction are presented for the main participants as well as quantitative characteristics of their affinity. Information on the mechanisms of regulation of protein–protein interactions mediated by DNA intermediates and posttranslational modification is presented. It can be suggested based on all available data that the multiprotein complexes are formed on chromatin independent of the DNA damage with the help of key regulators of the BER process – scaffold protein XRCC1 and poly(ADP-ribose) polymerase 1. The composition of multiprotein complexes changes dynamically depending on the DNA damage and the stage of BER process.     

Uncovering New Pathogen–Host Protein–Protein Interactions by Pairwise Structure Similarity

Pathogens usually evade and manipulate host-immune pathways through pathogen–host protein–protein interactions (PPIs) to avoid being killed by the host immune system. Therefore, uncovering pathogen–host PPIs is critical for determining the mechanisms underlying pathogen infection and survival. In this study, we developed a computational method, which we named pairwise structure similarity (PSS)-PPI, to predict pathogen–host PPIs. First, a high-quality and non-redundant structure–structure interaction (SSI) template library was constructed by exhaustively exploring heteromeric protein complex structures in the PDB database. New interactions were then predicted by searching for PSS with complex structures in the SSI template library. A quantitative score named the PSS score, which integrated structure similarity and residue–residue contact-coverage information, was used to describe the overall similarity of each predicted interaction with the corresponding SSI template. Notably, PSS-PPI yielded experimentally confirmed pathogen–host PPIs of human immunodeficiency virus type 1 (HIV-1) with performance close to that of in vitro high-throughput screening approaches. Finally, a pathogen–host PPI network of human pathogen Mycobacterium tuberculosis, the causative agent of tuberculosis, was constructed using PSS-PPI and refined using filtration steps based on cellular localization information. Analysis of the resulting network indicated that secreted proteins of the STPK, ESX-1, and PE/PPE family in M. tuberculosis targeted human proteins involved in immune response and phagocytosis. M. tuberculosis also targeted host factors known to regulate HIV replication. Taken together, our findings provide insights into the survival mechanisms of M. tuberculosis in human hosts, as well as co-infection of tuberculosis and HIV. With the rapid pace of three-dimensional protein structure discovery, the SSI template library we constructed and the PSS-PPI method we devised can be used to uncover new pathogen–host PPIs in the future.     

A Phage Display Technique for a Fast, Sensitive, and Systematic Investigation of Protein–Protein Interactions

Phage display is a technique in which a foreign protein or peptide is presented at the surface of a (filamentous) bacteriophage. This system, developed by Smith [(1985), Science 228, 1315–1317], was originally used to create large libraries of antibodies for the purpose of selecting those that strongly bound a particular antigen. More recently it was also employed to present peptides, domains of proteins, or intact proteins at the surface of phages, again to identify high-affinity interactions with ligands. Here we want to illustrate the use of phage display, in combination with PCR saturation mutagenesis, for the study of protein–protein interactions. Rather than selecting for mutants having high affinity, we systematically investigate the binding of every variant with its natural ligand. Via a modified ELISA we can calculate a relative affinity. As a model system we chose to display thymosin β4 on the phage surface in order to study its interaction with actin.     

A Critical Assessment of Information-guided Protein–Protein Docking Predictions

The structures of protein complexes are increasingly predicted via protein–protein docking (PPD) using ambiguous interaction data to help guide the docking. These data often are incomplete and contain errors and therefore could lead to incorrect docking predictions. In this study, we performed a series of PPD simulations to examine the effects of incompletely and incorrectly assigned interface residues on the success rate of PPD predictions. The results for a widely used PPD benchmark dataset obtained using a new interface information-driven PPD (IPPD) method developed in this work showed that the success rate for an acceptable top-ranked model varied, depending on the information content used, from as high as 95% when contact relationships (though not contact distances) were known for all residues to 78% when only the interface/non-interface state of the residues was known. However, the success rates decreased rapidly to ∼40% when the interface/non-interface state of 20% of the residues was assigned incorrectly, and to less than 5% for a 40% incorrect assignment. Comparisons with results obtained by re-ranking a global search and with those reported for other data-guided PPD methods showed that, in general, IPPD performed better than re-ranking when the information used was more complete and more accurate, but worse when it was not, and that when using bioinformatics-predicted information on interface residues, IPPD and other data-guided PPD methods performed poorly, at a level similar to simulations with a 40% incorrect assignment. These results provide guidelines for using information about interface residues to improve PPD predictions and reveal a bottleneck for such improvement imposed by the low accuracy of current bioinformatic interface residue predictions.Proteins work in close association with other proteins to mediate the intricate functions of a cell. The atomic resolution of the structure of a protein complex can therefore help one understand a protein''s function in detail. Protein–protein docking (PPD),¹ a computational approach that complements experimental structure determinations, has attracted increasing research interest (1, 2), in part because it remains challenging to determine most structures of protein complexes via experimental techniques (3).To improve the performance of PPD predictions, experimentally derived data (e.g. distances) and information (e.g. the identity of interface residues) have been used either as a filter allowing less plausible docking solutions to be disregarded (4–9) or as a constraint to guide the docking process (10, 11). Various types of data and information have been used to aid PPD (12); these range from distances between, or the relative orientation of, the two interacting proteins to simple identification of the amino acid residues directly involved in the binding of the two proteins (13). Despite considerable success, the caveat for all these data-guided PPD predictions is that the data or information used must be correct in order to avoid spurious results caused by misguiding (12). It is therefore pertinent and important to evaluate the effects of errors in the incorporated data or information on the quality of PPD solutions.We have recently shown that the use of just a few distance constraints can improve the success rates of PPD such that they rival, or are even better than, those of a global search ranked using a sophisticated energy function, and that errors in the distance data significantly decrease the success rates of prediction (11). However, because distance data for interacting proteins are usually hard to obtain, other types of data or information, even if “ambiguous” (10), are increasingly used in PPD predictions (12, 14). In this study, we investigated the effects of incompletely and incorrectly assigned interface/non-interface residues, a major source of the so-called ambiguous data, on information-guided PPD predictions.As illustrated in Fig. 1, the information content of interface/non-interface residues can be rich enough to reveal the identity of every pair of residues in contact, but not their contact distances, or so poor as to reveal the interface/non-interface state of these residues but not their pairing relationship, for one or both of the two interacting proteins. To determine how these different levels of residue information content can help PPD predictions and the extent to which the use of incorrectly assigned residues degrades prediction success rates, we have developed a new interface information-driven PPD method (IPPD) and carried out a series of PPD simulations on a well-tested benchmark dataset. The results showed that when the information content was rich, excellent predictions (success rates for producing an acceptable top-ranked model > 70%) could be made via IPPD or by re-ranking a global search''s solutions using the same interface information, and that, encouragingly, the success of predictions remained respectable (top-ranked success rates > 15%) when the content was poor. However, when enough of the interface residues were incorrectly assigned, as would be the case when using interface residues predicted by a state-of-the-art bioinformatics method such as CPORT (15), few models ranked first by IPPD or other PPD methods, including HADDOCK (10), a popular ambiguous data-driven PPD method, came close to being acceptable. These results suggest that we can greatly increase the power of PPD predictions for practical applications only if the accuracy of current bioinformatics methods for predicting the interface residues of protein complexes can be significantly improved.Open in a separate windowFig. 1.Contact matrix of two interacting proteins, A and B, and the contact vectors of their residues. In the contact matrix, M_ij = 1 or 0, respectively, denotes contact or a lack of contact between residue i in protein A and residue j in protein B. In the contact vectors, V_Ai = 1 or 0, respectively, when residue Ai has, or does not have, at least one contact with any residue of protein B.     

A Second-generation Protein–Protein Interaction Network of Helicobacter pylori

Helicobacter pylori infections cause gastric ulcers and play a major role in the development of gastric cancer. In 2001, the first protein interactome was published for this species, revealing over 1500 binary protein interactions resulting from 261 yeast two-hybrid screens. Here we roughly double the number of previously published interactions using an ORFeome-based, proteome-wide yeast two-hybrid screening strategy. We identified a total of 1515 protein–protein interactions, of which 1461 are new. The integration of all the interactions reported in H. pylori results in 3004 unique interactions that connect about 70% of its proteome. Excluding interactions of promiscuous proteins we derived from our new data a core network consisting of 908 interactions. We compared our data set to several other bacterial interactomes and experimentally benchmarked the conservation of interactions using 365 protein pairs (interologs) of E. coli of which one third turned out to be conserved in both species.Helicobacter pylori is a Gram-negative, microaerophilic bacterium that colonizes the stomach, an unusual highly acidic niche for microorganisms. In 1983, Warren and Marshall found it to be associated with gastric inflammation and duodenal ulcer disease (1, 2). A chronic infection with H. pylori can lead to development of stomach carcinoma and MALT lymphoma (reviewed in (3)). Hence, the World Health Organization has classified H. pylori as a class I carcinogen (4). It is estimated that half of the world′s population harbors H. pylori but with large variations in the geographical and socioeconomic distribution while causing annually 700,000 deaths worldwide (reviewed in (5)).The pathogenesis of H. pylori has been extensively studied, including the effector CagA, cytotoxin VacA, its adhesins and urease (reviewed in (3, 5–7)). The latter allows the bacterium to neutralize the stomach acid through ammonia production. However, H. pylori is not a classical model organism and thus many gaps in our knowledge still exist.The genome of H. pylori reference strain 26695 was completely sequenced in 1997 (8) and encodes 1587 proteins of which about 950 (61%) have been assigned functions (excluding “putatives”; Uniprot, CMR (9)). These numbers indicate that a large fraction of the proteins of H. pylori has not been functionally characterized.Protein–protein interactions (PPIs)¹ are required for nearly all biological processes. Unbiased interactomes are helpful to understand proteins or pathways and how they are linking poorly or uncharacterized proteins via their interactions. For instance, our study of the Treponema pallidum interactome (10) has led to the characterization of several previously “unknown” proteins such as YbeB, a ribosomal silencing factor (11), or TP0658, a regulator of flagellar translation and assembly (12, 13). However, only a few other comprehensive bacterial interactome studies have been published to date, including Campylobacter jejuni (14), Synechocystis sp. (15), Mycobacterium tuberculosis (16), Mesorhizobium loti (17), and recently Escherichia coli (18). In addition, partial interactomes are available for Bacillus subtilis (19) and H. pylori (20). Most of them used the yeast two-hybrid (Y2H) screening technology (21) which allows the pairwise detection of PPIs. Furthermore, a few other studies (22–25) systematically identified protein complexes and their compositions in bacteria.In 2001, Rain and colleagues have established a partial interactome of H. pylori, the first published protein interaction network of a bacterium (20). In this study, 261 bait constructs were screened against a random prey pool library resulting in the detection of over 1500 PPIs. Although this network likely represents a small fraction of all PPIs that occur in H. pylori, many downstream studies were motivated by these results (see below).Recent studies have disproved the notion that Y2H data sets are of poor quality (26, 27). Similarly, a high false-negative rate can be avoided by multiple Y2H expression vector systems (28–30) or protein fragments as opposed to full-length constructs (31). The aim of this study was to systematically screen the H. pylori proteome for binary protein interactions using a complementary approach to that of Rain et al. to produce an extended protein–protein interaction map of H. pylori. As a result, we have roughly doubled the number of known binary protein–protein interactions for H. pylori in this study.     

Multiple Modes of Protein–Protein Interactions Promote RNP Granule Assembly

Eukaryotic cells are known to contain a wide variety of RNA–protein assemblies, collectively referred to as RNP granules. RNP granules form from a combination of RNA–RNA, protein–RNA, and protein–protein interactions. In addition, RNP granules are enriched in proteins with intrinsically disordered regions (IDRs), which are frequently appended to a well-folded domain of the same protein. This structural organization of RNP granule components allows for a diverse set of protein–protein interactions including traditional structured interactions between well-folded domains, interactions of short linear motifs in IDRs with the surface of well-folded domains, interactions of short motifs within IDRs that weakly interact with related motifs, and weak interactions involving at most transient ordering of IDRs and folded domains with other components. In addition, both well-folded domains and IDRs in granule components frequently interact with RNA and thereby can contribute to RNP granule assembly. We discuss the contribution of these interactions to liquid–liquid phase separation and the possible role of phase separation in the assembly of RNP granules. We expect that these principles also apply to other non-membrane bound organelles and large assemblies in the cell.     

Computational Prediction of Protein–Protein Interaction Networks: Algo-rithms and Resources

Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.