Predicting kinetic constants of protein-protein interactions based on structural properties

Elucidating kinetic processes of protein–protein interactions (PPI) helps to understand how basic building blocks affect overall behavior of living systems. In this study, we used structure‐based properties to build predictive models for kinetic constants of PPI. A highly diverse PPI dataset, protein–protein kinetic interaction data and structures (PPKIDS), was built. PPKIDS contains 62 PPI with complex structures and kinetic constants measured experimentally. The influence of structural properties on kinetics of PPI was studied using 35 structure‐based features, describing different aspects of complex structures. Linear models for the prediction of kinetic constants were built by fitting with selected subsets of structure‐based features. The models gave correlation coefficients of 0.801, 0.732, and 0.770 for k_off, k_on, and K_d, respectively, in leave‐one‐out cross validations. The predictive models reported here use only protein complex structures as input and can be generally applied in PPI studies as well as systems biology modeling. Our study confirmed that different properties play different roles in the kinetic process of PPI. For example, k_on was affected by overall structural features of complexes, such as the composition of secondary structures, the change of translational and rotational entropy, and the electrostatic interaction; while k_off was determined by interfacial properties, such as number of contacted atom pairs per 100 Ų. This information provides useful hints for PPI design. Proteins 2010;79:720–734. © 2010 Wiley‐Liss, Inc.     

Properties of polyproline II, a secondary structure element implicated in protein-protein interactions

The polyproline II (PPII) conformation of protein backbone is an important secondary structure type. It is unusual in that, due to steric constraints, its main-chain hydrogen-bond donors and acceptors cannot easily be satisfied. It is unable to make local hydrogen bonds, in a manner similar to that of alpha-helices, and it cannot easily satisfy the hydrogen-bonding potential of neighboring residues in polyproline conformation in a manner analogous to beta-strands. Here we describe an analysis of polyproline conformations using the HOMSTRAD database of structurally aligned proteins. This allows us not only to determine amino acid propensities from a much larger database than previously but also to investigate conservation of amino acids in polyproline conformations, and the conservation of the conformation itself. Although proline is common in polyproline helices, helices without proline represent 46% of the total. No other amino acid appears to be greatly preferred; glycine and aromatic amino acids have low propensities for PPII. Accordingly, the hydrogen-bonding potential of PPII main-chain is mainly satisfied by water molecules and by other parts of the main-chain. Side-chain to main-chain interactions are mostly nonlocal. Interestingly, the increased number of nonsatisfied H-bond donors and acceptors (as compared with alpha-helices and beta-strands) makes PPII conformers well suited to take part in protein-protein interactions.     

Prediction-based fingerprints of protein-protein interactions

The recognition of protein interaction sites is an important intermediate step toward identification of functionally relevant residues and understanding protein function, facilitating experimental efforts in that regard. Toward that goal, the authors propose a novel representation for the recognition of protein-protein interaction sites that integrates enhanced relative solvent accessibility (RSA) predictions with high resolution structural data. An observation that RSA predictions are biased toward the level of surface exposure consistent with protein complexes led the authors to investigate the difference between the predicted and actual (i.e., observed in an unbound structure) RSA of an amino acid residue as a fingerprint of interaction sites. The authors demonstrate that RSA prediction-based fingerprints of protein interactions significantly improve the discrimination between interacting and noninteracting sites, compared with evolutionary conservation, physicochemical characteristics, structure-derived and other features considered before. On the basis of these observations, the authors developed a new method for the prediction of protein-protein interaction sites, using machine learning approaches to combine the most informative features into the final predictor. For training and validation, the authors used several large sets of protein complexes and derived from them nonredundant representative chains, with interaction sites mapped from multiple complexes. Alternative machine learning techniques are used, including Support Vector Machines and Neural Networks, so as to evaluate the relative effects of the choice of a representation and a specific learning algorithm. The effects of induced fit and uncertainty of the negative (noninteracting) class assignment are also evaluated. Several representative methods from the literature are reimplemented to enable direct comparison of the results. Using rigorous validation protocols, the authors estimated that the new method yields the overall classification accuracy of about 74% and Matthews correlation coefficients of 0.42, as opposed to up to 70% classification accuracy and up to 0.3 Matthews correlation coefficient for methods that do not utilize RSA prediction-based fingerprints. The new method is available at http://sppider.cchmc.org.     

Energy minimization in low-frequency normal modes to efficiently allow for global flexibility during systematic protein-protein docking

Protein-protein association can frequently involve significant backbone conformational changes of the protein partners. A computationally rapid method has been developed that allows to approximately account for global conformational changes during systematic protein-protein docking starting from many thousands of start configurations. The approach employs precalculated collective degrees of freedom as additional variables during protein-protein docking minimization. The global collective degrees of freedom are obtained from normal mode analysis using a Gaussian network model for the protein. Systematic docking searches were performed on 10 test systems that differed in the degree of conformational change associated with complex formation and in the degree of overlap between observed conformational changes and precalculated flexible degrees of freedom. The results indicate that in case of docking searches that minimize the influence of local side chain conformational changes inclusion of global flexibility can significantly improve the agreement of the near-native docking solutions with the corresponding experimental structures. For docking of unbound protein partners in several cases an improved ranking of near native docking solutions was observed. This was achieved at a very modest ( approximately 2-fold) increase of computational demands compared to rigid docking. For several test cases the number of docking solutions close to experiment was also significantly enhanced upon inclusion of soft collective degrees of freedom. This result indicates that inclusion of global flexibility can facilitate in silico protein-protein association such that a greater number of different start configurations results in favorable complex formation.     

Cluster conservation as a novel tool for studying protein-protein interactions evolution

Protein-protein interactions networks has come to be a buzzword associated with nets containing edges that represent a pair of interacting proteins (e.g. hormone-receptor, enzyme-inhibitor, antigen-antibody, and a subset of multichain biological machines). Yet, each such interaction composes its own unique network, in which vertices represent amino acid residues, and edges represent atomic contacts. Recent studies have shown that analyses of the data encapsulated in these detailed networks may impact predictions of structure-function correlation. Here, we study homologous families of protein-protein interfaces, which share the same fold but vary in sequence. In this context, we address what properties of the network are shared among relatives with different sequences (and hence different atomic interactions) and which are not. Herein, we develop the general mathematical framework needed to compare the modularity of homologous networks. We then apply this analysis to the structural data of a few interface families, including hemoglobin alpha-beta, growth hormone-receptor, and Serine protease-inhibitor. Our results suggest that interface modularity is an evolutionarily conserved property. Hence, protein-protein interfaces can be clustered down to a few modules, with the boundaries being evolutionarily conserved along homologous complexes. This suggests that protein engineering of protein-protein binding sites may be simplified by varying each module, but retaining the overall modularity of the interface.     

Crystal structural analysis of protein-protein interactions drastically destabilized by a single mutation

The complex of barnase (bn) and barstar (bs), which has been widely studied as a model for quantitative analysis of protein-protein interactions, is significantly destabilized by a single mutation, namely, bs Asp39 --> Ala, which corresponds to a change of 7.7 kcal x mol(-1) in the free energy of binding. However, there has been no structural information available to explain such a drastic destabilization. In the present study, we determined the structure of the mutant complex at 1.58 A resolution by X-ray crystallography. The complex was similar to the wild-type complex in terms of overall and interface structures; however, the hydrogen bond network mediated by water molecules at the interface was significantly different. Several water molecules filled the cavity created by the mutation and consequently caused rearrangement of the hydrated water molecules at the interface. The water molecules were redistributed into a channel-like structure that penetrated into the complex. Furthermore, molecular dynamics simulations showed that the mutation increased the mobility of water molecules at the interface. Since such a drastic change in hydration was not observed in other mutant complexes of bn and bs, the significant destabilization of the interaction may be due to this channel-like structure of hydrated water molecules.     

A novel method for protein-protein interaction site prediction using phylogenetic substitution models

Protein-protein binding events mediate many critical biological functions in the cell. Typically, functionally important sites in proteins can be well identified by considering sequence conservation. However, protein-protein interaction sites exhibit higher sequence variation than other functional regions, such as catalytic sites of enzymes. Consequently, the mutational behavior leading to weak sequence conservation poses significant challenges to the protein-protein interaction site prediction. Here, we present a phylogenetic framework to capture critical sequence variations that favor the selection of residues essential for protein-protein binding. Through the comprehensive analysis of diverse protein families, we show that protein binding interfaces exhibit distinct amino acid substitution as compared with other surface residues. On the basis of this analysis, we have developed a novel method, BindML, which utilizes the substitution models to predict protein-protein binding sites of protein with unknown interacting partners. BindML estimates the likelihood that a phylogenetic tree of a local surface region in a query protein structure follows the substitution patterns of protein binding interface and nonbinding surfaces. BindML is shown to perform well compared to alternative methods for protein binding interface prediction. The methodology developed in this study is very versatile in the sense that it can be generally applied for predicting other types of functional sites, such as DNA, RNA, and membrane binding sites in proteins.     

Prediction of interface residues in protein-protein complexes by a consensus neural network method: test against NMR data

The number of structures of protein-protein complexes deposited to the Protein Data Bank is growing rapidly. These structures embed important information for predicting structures of new protein complexes. This motivated us to develop the PPISP method for predicting interface residues in protein-protein complexes. In PPISP, sequence profiles and solvent accessibility of spatially neighboring surface residues were used as input to a neural network. The network was trained on native interface residues collected from the Protein Data Bank. The prediction accuracy at the time was 70% with 47% coverage of native interface residues. Now we have extensively improved PPISP. The training set now consisted of 1156 nonhomologous protein chains. Test on a set of 100 nonhomologous protein chains showed that the prediction accuracy is now increased to 80% with 51% coverage. To solve the problem of over-prediction and under-prediction associated with individual neural network models, we developed a consensus method that combines predictions from multiple models with different levels of accuracy and coverage. Applied on a benchmark set of 68 proteins for protein-protein docking, the consensus approach outperformed the best individual models by 3-8 percentage points in accuracy. To demonstrate the predictive power of cons-PPISP, eight complex-forming proteins with interfaces characterized by NMR were tested. These proteins are nonhomologous to the training set and have a total of 144 interface residues identified by chemical shift perturbation. cons-PPISP predicted 174 interface residues with 69% accuracy and 47% coverage and promises to complement experimental techniques in characterizing protein-protein interfaces. .     

Specificity of molecular interactions in transient protein-protein interaction interfaces

In this study, we investigate what types of interactions are specific to their biological function, and what types of interactions are persistent regardless of their functional category in transient protein-protein heterocomplexes. This is the first approach to analyze protein-protein interfaces systematically at the molecular interaction level in the context of protein functions. We perform systematic analysis at the molecular interaction level using classification and feature subset selection technique prevalent in the field of pattern recognition. To represent the physicochemical properties of protein-protein interfaces, we design 18 molecular interaction types using canonical and noncanonical interactions. Then, we construct input vector using the frequency of each interaction type in protein-protein interface. We analyze the 131 interfaces of transient protein-protein heterocomplexes in PDB: 33 protease-inhibitors, 52 antibody-antigens, 46 signaling proteins including 4 cyclin dependent kinase and 26 G-protein. Using kNN classification and feature subset selection technique, we show that there are specific interaction types based on their functional category, and such interaction types are conserved through the common binding mechanism, rather than through the sequence or structure conservation. The extracted interaction types are C(alpha)-- H...O==C interaction, cation...anion interaction, amine...amine interaction, and amine...cation interaction. With these four interaction types, we achieve the classification success rate up to 83.2% with leave-one-out cross-validation at k = 15. Of these four interaction types, C(alpha)--H...O==C shows binding specificity for protease-inhibitor complexes, while cation-anion interaction is predominant in signaling complexes. The amine ... amine and amine...cation interaction give a minor contribution to the classification accuracy. When combined with these two interactions, they increase the accuracy by 3.8%. In the case of antibody-antigen complexes, the sign is somewhat ambiguous. From the evolutionary perspective, while protease-inhibitors and sig-naling proteins have optimized their interfaces to suit their biological functions, antibody-antigen interactions are the happenstance, implying that antibody-antigen complexes do not show distinctive interaction types. Persistent interaction types such as pi...pi, amide-carbonyl, and hydroxyl-carbonyl interaction, are also investigated. Analyzing the structural orientations of the pi...pi stacking interactions, we find that herringbone shape is a major configuration in transient protein-protein interfaces. This result is different from that of protein core, where parallel-displaced configurations are the major configuration. We also analyze overall trend of amide-carbonyl and hydroxyl-carbonyl interactions. It is noticeable that nearly 82% of the interfaces have at least one hydroxyl-carbonyl interactions.     

Small-world network approach to identify key residues in protein-protein interaction

We show that protein complexes can be represented as small-world networks, exhibiting a relatively small number of highly central amino-acid residues occurring frequently at protein-protein interfaces. We further base our analysis on a set of different biological examples of protein-protein interactions with experimentally validated hot spots, and show that 83% of these predicted highly central residues, which are conserved in sequence alignments and nonexposed to the solvent in the protein complex, correspond to or are in direct contact with an experimentally annotated hot spot. The remaining 17% show a general tendency to be close to an annotated hot spot. On the other hand, although there is no available experimental information on their contribution to the binding free energy, detailed analysis of their properties shows that they are good candidates for being hot spots. Thus, highly central residues have a clear tendency to be located in regions that include hot spots. We also show that some of the central residues in the protein complex interfaces are central in the monomeric structures before dimerization and that possible information relating to hot spots of binding free energy could be obtained from the unbound structures.     

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

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

MULTIPROSPECTOR: an algorithm for the prediction of protein-protein interactions by multimeric threading

In this postgenomic era, the ability to identify protein-protein interactions on a genomic scale is very important to assist in the assignment of physiological function. Because of the increasing number of solved structures involving protein complexes, the time is ripe to extend threading to the prediction of quaternary structure. In this spirit, a multimeric threading approach has been developed. The approach is comprised of two phases. In the first phase, traditional threading on a single chain is applied to generate a set of potential structures for the query sequences. In particular, we use our recently developed threading algorithm, PROSPECTOR. Then, for those proteins whose template structures are part of a known complex, we rethread on both partners in the complex and now include a protein-protein interfacial energy. To perform this analysis, a database of multimeric protein structures has been constructed, the necessary interfacial pairwise potentials have been derived, and a set of empirical indicators to identify true multimers based on the threading Z-score and the magnitude of the interfacial energy have been established. The algorithm has been tested on a benchmark set comprised of 40 homodimers, 15 heterodimers, and 69 monomers that were scanned against a protein library of 2478 structures that comprise a representative set of structures in the Protein Data Bank. Of these, the method correctly recognized and assigned 36 homodimers, 15 heterodimers, and 65 monomers. This protocol was applied to identify partners and assign quaternary structures of proteins found in the yeast database of interacting proteins. Our multimeric threading algorithm correctly predicts 144 interacting proteins, compared to the 56 (26) cases assigned by PSI-BLAST using a (less) permissive E-value of 1 (0.01). Next, all possible pairs of yeast proteins have been examined. Predictions (n = 2865) of protein-protein interactions are made; 1138 of these 2865 interactions have counterparts in the Database of Interacting Proteins. In contrast, PSI-BLAST made 1781 predictions, and 1215 have counterparts in DIP. An estimation of the false-negative rate for yeast-predicted interactions has also been provided. Thus, a promising approach to help assist in the assignment of protein-protein interactions on a genomic scale has been developed.     

Quantitation of protein-protein interactions by thermal stability shift analysis

Thermal stability shift analysis is a powerful method for examining binding interactions in proteins. We demonstrate that under certain circumstances, protein-protein interactions can be quantitated by monitoring shifts in thermal stability using thermodynamic models and data analysis methods presented in this work. This method relies on the determination of protein stabilities from thermal unfolding experiments using fluorescent dyes such as SYPRO Orange that report on protein denaturation. Data collection is rapid and straightforward using readily available real-time polymerase chain reaction instrumentation. We present an approach for the analysis of the unfolding transitions corresponding to each partner to extract the affinity of the interaction between the proteins. This method does not require the construction of a titration series that brackets the dissociation constant. In thermal shift experiments, protein stability data are obtained at different temperatures according to the affinity- and concentration-dependent shifts in unfolding transition midpoints. Treatment of the temperature dependence of affinity is, therefore, intrinsic to this method and is developed in this study. We used the interaction between maltose-binding protein (MBP) and a thermostable synthetic ankyrin repeat protein (Off7) as an experimental test case because their unfolding transitions overlap minimally. We found that MBP is significantly stabilized by Off7. High experimental throughput is enabled by sample parallelization, and the ability to extract quantitative binding information at a single partner concentration. In a single experiment, we were able to quantify the affinities of a series of alanine mutants, covering a wide range of affinities (～ 100 nM to ～ 100 μM).     

Potential of mean force for protein-protein interaction studies.

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

A structure-based benchmark for protein-protein binding affinity

We have assembled a nonredundant set of 144 protein-protein complexes that have high-resolution structures available for both the complexes and their unbound components, and for which dissociation constants have been measured by biophysical methods. The set is diverse in terms of the biological functions it represents, with complexes that involve G-proteins and receptor extracellular domains, as well as antigen/antibody, enzyme/inhibitor, and enzyme/substrate complexes. It is also diverse in terms of the partners' affinity for each other, with K(d) ranging between 10(-5) and 10(-14) M. Nine pairs of entries represent closely related complexes that have a similar structure, but a very different affinity, each pair comprising a cognate and a noncognate assembly. The unbound structures of the component proteins being available, conformation changes can be assessed. They are significant in most of the complexes, and large movements or disorder-to-order transitions are frequently observed. The set may be used to benchmark biophysical models aiming to relate affinity to structure in protein-protein interactions, taking into account the reactants and the conformation changes that accompany the association reaction, instead of just the final product.     

Chip-based protein-protein interaction studied by atomic force microscopy

In this article, a technique for accurate direct measurement of protein‐to‐protein interactions before and after the introduction of a drug candidate is developed using atomic force microscopy (AFM). The method is applied to known immunosuppressant drug candidate Echinacea purpurea derived cynarin. T‐cell/CD28 is on‐chip immobilized and B‐cell/CD80 is immobilized on an AFM tip. The difference in unbinding force between these two proteins before and after the introduction of cynarin is measured. The method is described in detail including determination of the loading rates, maximum probability of bindings, and average unbinding forces. At an AFM loading rate of 1.44 × 10⁴ pN/s, binding events were largely reduced from 61 ± 5% to 47 ± 6% after cynarin introduction. Similarly, maximum probability of bindings reduced from 70% to 35% with a blocking effect of about 35% for a fixed contact time of 0.5 s or greater. Furthermore, average unbinding forces were reduced from 61.4 to 38.9 pN with a blocking effect of ～37% as compared with ～9% by SPR. AFM, which can provide accurate quantitative measures, is shown to be a good method for drug screening. The method could be applied to a wider variety of drug candidates with advances in bio‐chip technology and a more comprehensive AFM database of protein‐to‐protein interactions. Biotechnol. Bioeng. 2012; 109: 2460–2467. © 2012 Wiley Periodicals, Inc.     

Prediction of protein-protein binding free energies

We present an energy function for predicting binding free energies of protein-protein complexes, using the three-dimensional structures of the complex and unbound proteins as input. Our function is a linear combination of nine terms and achieves a correlation coefficient of 0.63 with experimental measurements when tested on a benchmark of 144 complexes using leave-one-out cross validation. Although we systematically tested both atomic and residue-based scoring functions, the selected function is dominated by residue-based terms. Our function is stable for subsets of the benchmark stratified by experimental pH and extent of conformational change upon complex formation, with correlation coefficients ranging from 0.61 to 0.66.     

Generation and analysis of a protein-protein interface data set with similar chemical and spatial patterns of interactions

Protein-protein interfaces are regions between 2 polypeptide chains that are not covalently connected. Here, we have created a nonredundant interface data set generated from all 2-chain interfaces in the Protein Data Bank. This data set is unique, since it contains clusters of interfaces with similar shapes and spatial organization of chemical functional groups. The data set allows statistical investigation of similar interfaces, as well as the identification and analysis of the chemical forces that account for the protein-protein associations. Toward this goal, we have developed I2I-SiteEngine (Interface-to-Interface SiteEngine) [Data set available at http://bioinfo3d.cs.tau.ac.il/Interfaces; Web server: http://bioinfo3d.cs.tau.ac.il/I2I-SiteEngine]. The algorithm recognizes similarities between protein-protein binding surfaces. I2I-SiteEngine is independent of the sequence or the fold of the proteins that comprise the interfaces. In addition to geometry, the method takes into account both the backbone and the side-chain physicochemical properties of the interacting atom groups. Its high efficiency makes it suitable for large-scale database searches and classifications. Below, we briefly describe the I2I-SiteEngine method. We focus on the classification process and the obtained nonredundant protein-protein interface data set. In particular, we analyze the biological significance of the clusters and present examples which illustrate that given constellations of chemical groups in protein-protein binding sites may be preferred, and are observed in proteins with different structures and different functions. We expect that these would yield further information regarding the forces stabilizing protein-protein interactions.     

Enhancing systematic protein-protein docking methods using ray casting: application to ATTRACT

Systematic protein-protein docking methods need to evaluate a huge number of different probe configurations, thus leading to high computational cost. We present an efficient filter-ray casting filter (RCF)-that enables a notable speed-up of systematic protein-protein docking. The high efficiency of RCF is the outcome of the following factors: (i) extracting of pockets and protrusions on the surfaces of the proteins using visibilities; (ii) a ray casting method that finds aligned receptor pocket/probe protrusion pairs without explicit similarity computations. The RCF method enables the integration of systematic methods and local shape feature matching methods. To verify the efficiency and the accuracy of RCF, we integrated it with a systematic protein-protein docking approach (ATTRACT) based on a reduced protein representation. The test results show that the integrated docking approach is much faster. At the same time, it ranks the lowest ligand root-mean-square deviation (RMSD) (L_rms) solutions higher when docking enzyme-enzyme inhibitor complexes. Consequently, RCF not only enables much faster execution of systematic docking runs but also improves the qualities of docking predictions.