Predicting 3D structures of transient protein-protein complexes by homology

The paper reports a homology based approach for predicting the 3D structures of full length hetero protein complexes. We have created a database of templates that includes structures of hetero protein-protein complexes as well as domain-domain structures (), which allowed us to expand the template pool up to 418 two-chain entries (at 40% sequence identity). Two protocols were tested-a protocol based on position specific Blast search (Protocol-I) and a protocol based on structural similarity of monomers (Protocol-II). All possible combinations of two monomers (350,284 pairs) in the ProtCom database were subjected to both protocols to predict if they form complexes. The predictions were benchmarked against the ProtCom database resulting to false-true positives ratios of approximately 5:1 and approximately 7:1 and recovery of 19% and 86%, respectively for protocols I and II. From 350,284 trials Protocol-I made only approximately 500 wrong predictions resulting to 0.5% error. In addition, though it was shown that artificially created domain-domain structures can in principle be good templates for modeling full length protein complexes, more sensitive methods are needed to detect homology relations. The quality of the models was assessed using two different criteria such as interfacial residues and overall RMSD. It was found that there is no correlation between these two measures. In many cases the interface residues were predicted correctly, but the overall RMSD was over 6 A and vice versa.     

Modeling protein-protein complexes involved in the cytochrome C oxidase copper-delivery pathway

Proper assembly and function of cytochrome c oxidase, which catalyzes the reduction of O2 and generates the proton gradient driving ATP synthesis, depend on correct copper delivery and incorporation. Structural details about the protein-protein complexes involved in this process are still missing. We describe here models of four complexes along this pathway obtained by combining bioinformatics interface predictions with information-driven docking and discuss their relevance with respect to known and pathogenic mutations.     

Electrostatic properties of protein-protein complexes

Statistical electrostatic analysis of 37 protein-protein complexes extracted from the previously developed database of protein complexes (ProtCom, http://www.ces.clemson.edu/compbio/protcom) is presented. It is shown that small interfaces have a higher content of charged and polar groups compared to large interfaces. In a vast majority of the cases the average pKa shifts for acidic residues induced by the complex formation are negative, indicating that complex formation stabilizes their ionizable states, whereas the histidines are predicted to destabilize the complex. The individual pKa shifts show the same tendency since 80% of the interfacial acidic groups were found to lower their pKas, whereas only 25% of histidines raise their pKa upon the complex formation. The interfacial groups have been divided into three sets according to the mechanism of their pKa shift, and statistical analysis of each set was performed. It was shown that the optimum pH values (pH of maximal stability) of the complex tend to be the same as the optimum pH values of the complex components. This finding can be used in the homology-based prediction of the 3D structures of protein complexes, especially when one needs to evaluate and rank putative models. It is more likely for a model to be correct if both components of the model complex and the entire complex have the same or at least similar values of the optimum pH.     

Computational prediction of protein-protein complexes

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

Homology-based modeling of 3D structures of protein-protein complexes using alignments of modified sequence profiles

Customary practice in predicting 3D structures of protein-protein complexes is employment of various docking methods when the structures of separate monomers are known a priori. The alternative approach, i.e. the template-based prediction with pure sequence information as a starting point, is still considered as being inferior mostly due to presumption that the pool of available structures of protein-protein complexes, which can serve as putative templates, is not sufficiently large. Recently, however, several labs have developed databases containing thousands of 3D structures of protein-protein complexes, which enable statistically reliable testing of homology-based algorithms. In this paper we report the results on homology-based modeling of 3D structures of protein complexes using alignments of modified sequence profiles. The method, called HOMology-BAsed COmplex Prediction (HOMBACOP), has two distinctive features: (I) extra weight on aligning interfacial residues in the dynamical programming algorithm, and (II) increased gap penalties for the interfacial segments. The method was tested against our recently developed ProtCom database and against the Boston University protein-protein BENCHMARK. In both cases, models generated were compared to the models built on basis of customarily protein structure initiative (PSI)-BLAST sequence alignments. It was found that existence of homologous (by the means of PSI-BLAST) templates (44% of cases) enables both methods to produce models of good quality, with the profiles method outperforming the PSI-BLAST models (with respect to the percentage of correctly predicted residues on the complex interface and fraction of native interfacial contacts). The models were evaluated according to the CAPRI assessment criteria and about two thirds of the models were found to fall into acceptable and medium-quality categories. The same comparison of a larger set of 463 protein complexes showed again that profiles generate better models. We further demonstrate, using our ProtCom database, the suitability of the profile alignment algorithm in detecting remote homologues between query and template sequences, where the PSI-BLAST method fails.     

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。     

Cross-links of quadruplex structures from human telomeric DNA by dinuclear platinum complexes show the flexibility of both structures

The folding of AG(3)(T(2)AG(3))(3) was investigated in the presence of Na(+) or K(+) ions, by using the dinuclear platinum complexes [{trans-PtCl(NH(3))(2)}(2)H(2)N(CH(2))(n)NH(2)]Cl(2) (n = 2 or 6). AG(3)(T(2)AG(3))(3) has been previously found to adopt two different quadruplex structures: the antiparallel one in a solution containing Na(+) and the parallel one in a K(+)-containing crystal. The two structures are strikingly distinct and are not expected to form the same platinum cross-links. Therefore, characterization of the cross-links formed with platinum complexes in solution allowed the predominant conformation(s) to be identified. The bases coordinating the platinum atoms were identified by chemical and 3'-exonuclease digestions. The observed cross-links showed that the parallel structure exists in solution whatever the cation and confirmed the existence of the antiparallel structure in the presence of both cations as previously reported from cross-linking experiments of AG(3)(T(2)AG(3))(3) by mononuclear platinum complexes. Furthermore, the major platinum cross-links were unexpectedly formed between two guanines belonging to the same G-quartet. Their formation was rationalized using molecular dynamics simulations in implicit solvent of the two quadruplex structures. It was shown that they were flexible, allowing some guanines to leave reversibly the top G-quartet and thus rendering their N(7) atom accessible to platinum complexes. Our results also suggest that the human telomere sequence could be a target for such platinum complexes.     

Specificity determinants of human cathepsin s revealed by crystal structures of complexes

Cathepsin S, a lysosomal cysteine protease of the papain superfamily, has been implicated in the preparation of MHC class II alphabeta-heterodimers for antigen presentation to CD4+ T lymphocytes and is considered a potential target for autoimmune-disease therapy. Selective inhibition of this enzyme may be therapeutically useful for attenuating the hyperimmune responses in a number of disorders. We determined the three-dimensional crystal structures of human cathepsin S in complex with potent covalent inhibitors, the aldehyde inhibitor 4-morpholinecarbonyl-Phe-(S-benzyl)Cys-Psi(CH=O), and the vinyl sulfone irreversible inhibitor 4-morpholinecarbonyl-Leu-Hph-Psi(CH=CH-SO(2)-phenyl) at resolutions of 1.8 and 2.0 A, respectively. In the structure of the cathepsin S-aldehyde complex, the tetrahedral thiohemiacetal adduct favors the S-configuration, in which the oxygen atom interacts with the imidazole group of the active site His164 rather than with the oxyanion hole. The present structures provide a detailed map of noncovalent intermolecular interactions established in the substrate-binding subsites S3 to S1' of cathepsin S. In the S2 pocket, which is the binding affinity hot spot of cathepsin S, the Phe211 side chain can assume two stable conformations that accommodate either the P2-Leu or a bulkier P2-Phe side chain. This structural plasticity of the S2 pocket in cathepsin S explains the selective inhibition of cathepsin S over cathepsin K afforded by inhibitors with the P2-Phe side chain. Comparison with the structures of cathepsins K, V, and L allows delineation of local intermolecular contacts that are unique to cathepsin S.     

Optimization of electrostatic interactions in protein-protein complexes

In this article, we present a statistical analysis of the electrostatic properties of 298 protein-protein complexes and 356 domain-domain structures extracted from the previously developed database of protein complexes (ProtCom, http://www.ces.clemson.edu/compbio/protcom). For each structure in the dataset we calculated the total electrostatic energy of the binding and its two components, Coulombic and reaction field energy. It was found that in a vast majority of the cases (>90%), the total electrostatic component of the binding energy was unfavorable. At the same time, the Coulombic component of the binding energy was found to favor the complex formation while the reaction field component of the binding energy opposed the binding. It was also demonstrated that the components in a wild-type (WT) structure are optimized/anti-optimized with respect to the corresponding distributions, arising from random shuffling of the charged side chains. The degree of this optimization was assessed through the Z-score of WT energy in respect to the random distribution. It was found that the Z-scores of Coulombic interactions peak at a considerably negative value for all 654 cases considered while the Z-score of the reaction field energy varied among different types of complexes. All these findings indicate that the Coulombic interactions within WT protein-protein complexes are optimized to favor the complex formation while the total electrostatic energy predominantly opposes the binding. This observation was used to discriminate WT structures among sets of structural decoys and showed that the electrostatic component of the binding energy is not a good discriminator of the WT; while, Coulombic or reaction field energies perform better depending upon the decoy set used.     

Improving the performance of an SVM-based method for predicting protein-protein interactions

Predicting the interactions between all the possible pairs of proteins in a given organism (making a protein-protein interaction map) is a crucial subject in bioinformatics. Most of the previous methods based on supervised machine learning use datasets containing approximately the same number of interacting pairs of proteins (positives) and non-interacting pairs of proteins (negatives) for training a classifier and are estimated to yield a large number of false positives. Thinking that the negatives used in previous studies cannot adequately represent all the negatives that need to be taken into account, we have developed a method based on multiple Support Vector Machines (SVMs) that uses more negatives than positives for predicting interactions between pairs of yeast proteins and pairs of human proteins. We show that the performance of a single SVM improved as we increased the number of negatives used for training and that, if more than one CPU is available, an approach using multiple SVMs is useful not only for improving the performance of classifiers but also for reducing the time required for training them. Our approach can also be applied to assessing the reliability of high-throughput interactions.     

An ensemble of K-local hyperplanes for predicting protein-protein interactions

Prediction of protein-protein interaction is a difficult and important problem in biology. In this paper, we propose a new method based on an ensemble of K-local hyperplane distance nearest neighbor (HKNN) classifiers, where each HKNN is trained using a different physicochemical property of the amino acids. Moreover, we propose a new encoding technique that combines the amino acid indices together with the 2-Grams amino acid composition. A fusion of HKNN classifiers combined with the 'Sum rule' enables us to obtain an improvement over other state-of-the-art methods. The approach is demonstrated by building a learning system based on experimentally validated protein-protein interactions in human gastric bacterium Helicobacter pylori and in Human dataset.     

Thalidomide induces apoptosis in human monocytes by using a cytochrome c-dependent pathway

Thalidomide has been shown to be an effective treatment in various immunologic diseases such as Crohn's disease and rheumatoid arthritis. Its major effect is thought to be mediated by the inhibition of TNF-alpha, but the exact mechanism of action is still uncertain. Recent observations could demonstrate that the induction of monocyte apoptosis is a common feature of a variety of anti-inflammatory agents. Therefore, we investigated the role of thalidomide on monocyte apoptosis. Treatment with thalidomide resulted in apoptosis of human peripheral blood monocytes in a time- and dose-dependent manner as demonstrated by annexin V staining. Monocyte apoptosis required the activation of caspases, as combined stimulation by thalidomide together with the broad caspase inhibitor benzyloxycarbonyl-Val-Ala-Asp-fluoromethyl ketone markedly prevented monocyte cell death. Apoptosis was triggered by a CD95/CD95 ligand, TNF-RI, and TRAIL-R1 independent pathway with an inhibition of AKT-1 kinase and consecutive mitochondrial release of cytochrome c, followed by the proteolytic activation of initiator caspase-9 and effector caspase-3. Our data suggest that thalidomide-induced monocyte apoptosis is at least partially mediated by a mitochondrial signaling pathway and might contribute to the complex immunomodulatory properties of the drug.     

Large-scale prediction of protein-protein interactions from structures

Background  

The prediction of protein-protein interactions is an important step toward the elucidation of protein functions and the understanding of the molecular mechanisms inside the cell. While experimental methods for identifying these interactions remain costly and often noisy, the increasing quantity of solved 3D protein structures suggests that in silico methods to predict interactions between two protein structures will play an increasingly important role in screening candidate interacting pairs. Approaches using the knowledge of the structure are presumably more accurate than those based on sequence only. Approaches based on docking protein structures solve a variant of this problem, but these methods remain very computationally intensive and will not scale in the near future to the detection of interactions at the level of an interactome, involving millions of candidate pairs of proteins.     

Crystal structures of complexes with cobalt-reconstituted human arginase I

The binuclear manganese metalloenzyme human arginase I (HAI) is a potential protein drug for cancer chemotherapy, in that it is capable of depleting extracellular l-Arg levels in the microenvironment of tumor cells that require this nutrient to thrive. Substitution of the native Mn(2+)(2) cluster with a Co(2+)(2) cluster in the active site yields an enzyme with enhanced catalytic activity at physiological pH (～7.4) that could serve as an improved protein drug for L-Arg depletion therapy [Stone, E. M., Glazer, E. S., Chantranupong, L., Cherukuri, P., Breece, R. M., Tierney, D. L., Curley, S. A., Iverson, B. L., and Georgiou, G. (2010) ACS Chem. Biol. 5, 333-342]. A different catalytic mechanism is proposed for Co(2+)(2)-HAI compared with that of Mn(2+)(2)-HAI, including an unusual Nε-Co(2+) coordination mode, to rationalize the lower K(M) value of L-Arg and the lower K(i) value of L-Orn. However, we now report that no unusual metal coordination modes are observed in the cobalt-reconstituted enzyme. The X-ray crystal structures of unliganded Co(2+)(2)-HAI determined at 2.10 ? resolution (pH 7.0) and 1.97 ? resolution (pH 8.5), as well as the structures of Co(2+)(2)-HAI complexed with the reactive substrate analogue 2(S)-amino-6-boronohexanoic acid (ABH, pH 7.0) and the catalytic product L-Orn (pH 7.0) determined at 1.85 and 1.50 ? resolution, respectively, are essentially identical to the corresponding structures of Mn(2+)(2)-HAI. Therefore, in the absence of significant structural differences between Co(2+)(2)-HAI and Mn(2+)(2)-HAI, we suggest that a higher concentration of metal-bridging hydroxide ion at physiological pH for Co(2+)(2)-HAI, a consequence of the lower pK(a) of a Co(2+)-bound water molecule compared with a Mn(2+)-bound water molecule, strengthens electrostatic interactions with cationic amino acids and accounts for enhanced affinity as reflected in the lower K(M) value of L-Arg and the lower K(i) value of L-Orn.     

Topology of small-world networks of protein-protein complex structures

The majority of real examples of small-world networks exhibit a power law distribution of edges among the nodes, therefore not fitting into the wiring model proposed by Watts and Strogatz. However, protein structures can be modeled as small-world networks, with a distribution of the number of links decaying exponentially as in the case of this wiring model. We approach the protein-protein interaction mechanism by viewing it as a particular rewiring occurring in the system of two small-world networks represented by the monomers, where a re-arrangement of links takes place upon dimerization leaving the small-world character in the dimer network. Due to this rewiring, the most central residues at the complex interfaces tend to form clusters, which are not homogenously distributed. We show that these highly central residues are strongly correlated with the presence of hot spots of binding free energy. CONTACT: ao-mesa@fujirebio.co.jp SUPPLEMENTARY INFORMATION: http://www.fujirebio.co.jp/support/index.php (under construction).     

A protein-protein docking algorithm dependent on the type of complexes

An efficient 'soft docking' algorithm is described to assist the prediction of protein-protein association using three-dimensional structures of molecules. The basic tools are the 'simplified protein' model and the docking algorithm of Wodak and Janin. The side chain flexibility of Arg, Lys, Asp, Glu and Met residues at the protein surface is taken into account. The complex type-dependent filtering technique on the basis of the geometric matching, hydrophobicity and electrostatic complementarity is used to select candidate binding modes. Subsequently, we calculate a scoring function which includes electrostatic and desolvation energy terms. In the 44 complexes tested including enzyme-inhibitor, antibody-antigen and other complexes, native-like structures were all found, of which 30 were ranked in the top 20. Thus, our soft docking algorithm has the potential to predict protein-protein recognition.