Blind prediction of interfacial water positions in CAPRI.

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions.

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

Rapid identification of protein-protein interfaces for the construction of a complex model based on multiple unassigned signals by using time-sharing NMR measurements.

Protein-protein interactions are necessary for various cellular processes, and therefore, information related to protein-protein interactions and structural information of complexes is invaluable. To identify protein-protein interfaces using NMR, resonance assignments are generally necessary to analyze the data; however, they are time consuming to collect, especially for large proteins. In this paper, we present a rapid, effective, and unbiased approach for the identification of a protein-protein interface without resonance assignments. This approach requires only a single set of 2D titration experiments of a single protein sample, labeled with a unique combination of an (15)N-labeled amino acid and several amino acids (13)C-labeled on specific atoms. To rapidly obtain high resolution data, we applied a new pulse sequence for time-shared NMR measurements that allowed simultaneous detection of a ω(1)-TROSY-type backbone (1)H-(15)N and aromatic (1)H-(13)C shift correlations together with single quantum methyl (1)H-(13)C shift correlations. We developed a structure-based computational approach, that uses our experimental data to search the protein surfaces in an unbiased manner to identify the residues involved in the protein-protein interface. Finally, we demonstrated that the obtained information of the molecular interface could be directly leveraged to support protein-protein docking studies. Such rapid construction of a complex model provides valuable information and enables more efficient biochemical characterization of a protein-protein complex, for instance, as the first step in structure-guided drug development.

Structure determination of a protein assembly by amino acid selective cross-saturation.

Amino acid selective cross-saturation (ASCS) method not only provides information about the interface of a protein assembly by the spin relaxation experiment, but also identifies the amino acid residues in the acceptor protein, which are located close to the selectively labeled amino acid residues in the donor protein. Here, a new method was developed to build a precise structural model of a protein assembly, which satisfies the experimental ASCS values, using simulated annealing computation. This method was applied to the ubiquitin-yeast ubiquitin hydrolase 1 (Ub-YUH1) complex to build a precise complex structure compatible with that determined by X-ray crystallography.

Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes.

We developed a new protocol for in silico drug screening for G-protein-coupled receptors (GPCRs) using a set of "universal active probes" (UAPs) with an ensemble docking procedure. UAPs are drug-like compounds, which are actual active compounds of a variety of known proteins. The current targets were nine human GPCRs whose three-dimensional (3D) structures are unknown, plus three GPCRs, namely ß(2)-adrenergic receptor (ADRB2), A(2A) adenosine receptor (A(2A)), and dopamine D3 receptor (D(3)), whose 3D structures are known. Homology-based models of the GPCRs were constructed based on the crystal structures with careful sequence inspection. After subsequent molecular dynamics (MD) simulation taking into account the explicit lipid membrane molecules with periodic boundary conditions, we obtained multiple model structures of the GPCRs. For each target structure, docking-screening calculations were carried out via the ensemble docking procedure, using both true active compounds of the target proteins and the UAPs with the multiple target screening (MTS) method. Consequently, the multiple model structures showed various screening results with both poor and high hit ratios, the latter of which could be identified as promising for use in in silico screening to find candidate compounds to interact with the proteins. We found that the hit ratio of true active compounds showed a positive correlation to that of the UAPs. Thus, we could retrieve appropriate target structures from the GPCR models by applying the UAPs, even if no active compound is known for the GPCRs. Namely, the screening result that showed a high hit ratio for the UAPs could be used to identify actual hit compounds for the target GPCRs.

Docking of protein molecular surfaces with evolutionary trace analysis.

We have developed a new method to predict protein- protein complexes based on the shape complementarity of the molecular surfaces, along with sequence conservation obtained by evolutionary trace (ET) analysis. The docking is achieved by optimization of an object function that evaluates the degree of shape complementarity weighted by the conservation of the interacting residues. The optimization is carried out using a genetic algorithm in combination with Monte Carlo sampling. We applied this method to CAPRI targets and evaluated the performance systematically. Consequently, our method could achieve native-like predictions in several cases. In addition, we have analyzed the feasibility of the ET method for docking simulations, and found that the conservation information was useful only in a limited category of proteins (signal related proteins and enzymes).

Structural comparison between the open and closed forms of citrate synthase from Thermus thermophilus HB8.

The crystal structures of citrate synthase from the thermophilic eubacteria Thermus thermophilus HB8 (TtCS) were determined for an open form at 1.5 Å resolution and for closed form at 2.3 Å resolution, respectively. In the absence of ligands TtCS in the open form was crystalized into a tetragonal form with a single subunit in the asymmetric unit. TtCS was also co-crystallized with citrate and coenzyme-A to form an orthorhombic crystal with two homodimers in the asymmetric unit. Citrate and CoA are found in the active site situated between the large domain and the small domain in all subunit whereas the complex shows two distinct closed conformations, the fully closed form and partially closed form. Structural comparisons are performed to describe conformational changes associated with binding of products of TtCS. Upon binding of citrate, basic residues in the active site move toward citrate and make a hydrogen bond network in the active site, inducing a large-scale rotation of the small domain relative to the large domain. CoA is sandwiched between the small and large domains and then the cysteamine tail is inserted into the active site with a cooperative rotation around mainchain dihedrals in the hinge region connecting helices M and N. According to this rotation these helices are extended to close the active site completely. The considerable flexibility and structural rearrangements in the hinge region are crucial for an ordered bibi reaction in catalysis for microbial CSs.

Classification of heterodimer interfaces using docking models and construction of scoring functions for the complex structure prediction.

Protein-protein docking simulations can provide the predicted complex structural models. In a docking simulation, several putative structural models are selected by scoring functions from an ensemble of many complex models. Scoring functions based on statistical analyses of heterodimers are usually designed to select the complex model with the most abundant interaction mode found among the known complexes, as the correct model. However, because the formation schemes of heterodimers are extremely diverse, a single scoring function does not seem to be sufficient to describe the fitness of the predicted models other than the most abundant interaction mode. Thus, it is necessary to classify the heterodimers in terms of their individual interaction modes, and then to construct multiple scoring functions for each heterodimer type. In this study, we constructed the classification method of heterodimers based on the discriminative characters between near-native and decoy models, which were found in the comparison of the interfaces in terms of the complementarities for the hydrophobicity, the electrostatic potential and the shape. Consequently, we found four heterodimer clusters, and then constructed the multiple scoring functions, each of which was optimized for each cluster. Our multiple scoring functions were applied to the predictions in the unbound docking.

An icosahedral assembly of the light-harvesting chlorophyll a/b protein complex from pea chloroplast thylakoid membranes.

When the light-harvesting chlorophyll a/b protein complex (LHC-II) from pea thylakoid membranes is co-crystallized with native lipids, an octahedral crystal that exhibits no birefringence is obtained. Cryogenic electron micrographs of a crystal edge showed the crystal to be made up of hollow spherical assemblies with a diameter of 250 A. X-ray diffraction data at 9.5 A resolution revealed the spherical shell of LHC-II to have icosahedral symmetry. A T = 1 icosahedral model of LHC-II, in which the stromal surface of the protein faces outward, was constructed using the previously reported structure of the LHC-II trimer [Kühlbrandt et al. (1994), Nature (London), 367, 614-621]. The present result shows the first example of a well ordered three-dimensional crystal of icosahedral proteoliposomes.