Protein-protein Docking Predictions Research Articles

Analysis of the Relationship Between Metalloprotease-9 and Tau Protein in Alzheimer's Disease.

Neurofibrillary tangles (NFTs) and amyloid plaques are the neuropathological hallmarks in brains with Alzheimer's disease (AD). Post-translational modifications of tau, such as phosphorylation and truncation, have been proposed as initiators in the assembly of the abnormal paired helical filaments that constitute the NFTs. Neurons and NFTs are sites of matrix metalloproteinases (MMPs). The aim of this study was to analyze the relationship of MMP-9 and tau protein in brain samples with AD. This study was performed on brain tissue samples from patients with early, moderate, and late AD. MMPs and tau levels were analyzed by western blot and gelatin-substrate zymography. Immunofluorescence techniques and confocal microscopy were used to analyze the presence of both proteins in NFTs. Further, molecular dynamics simulations (MDS) and protein-protein docking were conducted to predict interaction between MMP-9 and tau protein. MMP-9 expression was greatest in moderate and late AD, whereas MMP-2 expression was only increased in late-stage AD. Interestingly, confocal microscopy revealed NFTs in which there was co-localization of MMP-9 and tau protein. MDS and protein-protein docking predictions indicate that a high-affinity complex can be formed between MMP-9 and full-length tau protein. These observations provide preliminary evidence of an interaction between these two proteins. Post-translational modifications of tau protein, such as C-terminal truncation or phosphorylation of amino acid residues in the MMP-9 recognition site and conformational changes in the protein, such as folding of the N-terminal sequence over the three-repeat domain, could preclude the interaction between MMP-9 and tau protein during stages of NFT development.

A Model for the Homotypic Interaction between Na+,K+-ATPase β1 Subunits Reveals the Role of Extracellular Residues 221-229 in Its Ig-Like Domain.

The Na+, K+-ATPase transports Na+ and K+ across the membrane of all animal cells. In addition to its ion transporting function, the Na+, K+-ATPase acts as a homotypic epithelial cell adhesion molecule via its β1 subunit. The extracellular region of the Na+, K+-ATPase β1 subunit includes a single globular immunoglobulin-like domain. We performed Molecular Dynamics simulations of the ectodomain of the β1 subunit and a refined protein-protein docking prediction. Our results show that the β1 subunit Ig-like domain maintains an independent structure and dimerizes in an antiparallel fashion. Analysis of the putative interface identified segment Lys221-Tyr229. We generated triple mutations on YFP-β1 subunit fusion proteins to assess the contribution of these residues. CHO fibroblasts transfected with mutant β1 subunits showed a significantly decreased cell-cell adhesion. Association of β1 subunits in vitro was also reduced, as determined by pull-down assays. Altogether, we conclude that two Na+, K+-ATPase molecules recognize each other by a large interface spanning residues 221–229 and 198–207 on their β1 subunits.

Human and server docking prediction for CAPRI round 30-35 using LZerD with combined scoring functions.

We report the performance of protein-protein docking predictions by our group for recent rounds of the Critical Assessment of Prediction of Interactions (CAPRI), a community-wide assessment of state-of-the-art docking methods. Our prediction procedure uses a protein-protein docking program named LZerD developed in our group. LZerD represents a protein surface with 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. The appropriate soft representation of protein surface with 3DZD makes the method more tolerant to conformational change of proteins upon docking, which adds an advantage for unbound docking. Docking was guided by interface residue prediction performed with BindML and cons-PPISP as well as literature information when available. The generated docking models were ranked by a combination of scoring functions, including PRESCO, which evaluates the native-likeness of residues' spatial environments in structure models. First, we discuss the overall performance of our group in the CAPRI prediction rounds and investigate the reasons for unsuccessful cases. Then, we examine the performance of several knowledge-based scoring functions and their combinations for ranking docking models. It was found that the quality of a pool of docking models generated by LZerD, that is whether or not the pool includes near-native models, can be predicted by the correlation of multiple scores. Although the current analysis used docking models generated by LZerD, findings on scoring functions are expected to be universally applicable to other docking methods. Proteins 2017; 85:513-527. © 2016 Wiley Periodicals, Inc.

Ranking protein-protein docking results using steered molecular dynamics and potential of mean force calculations.

Crystallization of protein-protein complexes can often be problematic and therefore computational structural models are often relied on. Such models are often generated using protein-protein docking algorithms, where one of the main challenges is selecting which of several thousand potential predictions represents the most near-native complex. We have developed a novel technique that involves the use of steered molecular dynamics (sMD) and umbrella sampling to identify near-native complexes among protein-protein docking predictions. Using this technique, we have found a strong correlation between our predictions and the interface RMSD (iRMSD) in ten diverse test systems. On two of the systems, we investigated if the prediction results could be further improved using potential of mean force calculations. We demonstrated that a near-native (<2.0 Å iRMSD) structure could be identified in the top-1 ranked position for both systems. © 2016 Wiley Periodicals, Inc.

Residue Environment Score for Selecting Protein Structure Models and Protein-Protein Docking Models

In protein structure modeling area, it is critical to have an accurate scoring function that can select near-native structure models from a pool of alternative models. I have developed a novel scoring function that captures multi-residue environment in a protein model. The method called PRESCO (Protein Residue Environment SCO) uses a new representation of local amino acid environments in protein structures called the Side-chain Depth Environment (SDE). An SDE defines a local structural environment of a residue considering the coordinates and the depth of amino acids that locate in the vicinity of the side-chain centroid of the residue. The good performance of PRESCO was also proved in the last Critical Assessment of Techniques in Protein Structure Prediction (CASP 11), a world-wide protein structure prediction experiment. PRESCO method was ranked the top in the free modeling category among over 200 participated groups. (http://predictioncenter.org/casp11). In this presentation, I will further discuss that PRESCO performs well in recognizing near native complex structure models among other models in protein-protein docking prediction. I will show that PRESCO recognizes specific residue environment of residues at protein docking interface, which leads to successful distinction of near-native complex structure models from wrong models.

NPPD: A Protein-Protein Docking Scoring Function Based on Dyadic Differences in Networks of Hydrophobic and Hydrophilic Amino Acid Residues

Protein-protein docking (PPD) predictions usually rely on the use of a scoring function to rank docking models generated by exhaustive sampling. To rank good models higher than bad ones, a large number of scoring functions have been developed and evaluated, but the methods used for the computation of PPD predictions remain largely unsatisfactory. Here, we report a network-based PPD scoring function, the NPPD, in which the network consists of two types of network nodes, one for hydrophobic and the other for hydrophilic amino acid residues, and the nodes are connected when the residues they represent are within a certain contact distance. We showed that network parameters that compute dyadic interactions and those that compute heterophilic interactions of the amino acid networks thus constructed allowed NPPD to perform well in a benchmark evaluation of 115 PPD scoring functions, most of which, unlike NPPD, are based on some sort of protein-protein interaction energy. We also showed that NPPD was highly complementary to these energy-based scoring functions, suggesting that the combined use of conventional scoring functions and NPPD might significantly improve the accuracy of current PPD predictions.

On the use of distance constraints in protein-protein docking computations

Protein-protein docking (PPD) is a computational process that predicts the structure of a complex of two interacting proteins from their unbound structures. The accuracy of PPD predictions is low, but can be greatly enhanced if experimentally determined distance data are available for incorporation into the prediction. However, the specific effects of distance constraints on PPD predictions are largely uncharacterized. In this study, we systematically simulated the effects of using distance constraints both on a new distance constraint-driven PPD approach, called DPPD, and also, by re-ranking, on a well-established grid-based global search approach. Our results for a PPD benchmark dataset of 84 protein complexes of known structures showed that near 100% docking success rates could be obtained when the number of distance constraints exceeded six, the degrees of freedom of the system, but the success rate was significantly reduced by long distance constraints, large binding-induced conformational changes, and large errors in the distance data. Our results also showed that, under most conditions simulated, even two or three distance constraints were sufficient to achieve a much better success rate than those using a sophisticated physicochemical function to re-rank the results of the global search. Our study provides guidelines for the practical incorporation of experimental distance data to aid PPD predictions.

DECK: Distance and environment-dependent, coarse-grained, knowledge-based potentials for protein-protein docking

BackgroundComputational approaches to protein-protein docking typically include scoring aimed at improving the rank of the near-native structure relative to the false-positive matches. Knowledge-based potentials improve modeling of protein complexes by taking advantage of the rapidly increasing amount of experimentally derived information on protein-protein association. An essential element of knowledge-based potentials is defining the reference state for an optimal description of the residue-residue (or atom-atom) pairs in the non-interaction state.ResultsThe study presents a new Distance- and Environment-dependent, Coarse-grained, Knowledge-based (DECK) potential for scoring of protein-protein docking predictions. Training sets of protein-protein matches were generated based on bound and unbound forms of proteins taken from the DOCKGROUND resource. Each residue was represented by a pseudo-atom in the geometric center of the side chain. To capture the long-range and the multi-body interactions, residues in different secondary structure elements at protein-protein interfaces were considered as different residue types. Five reference states for the potentials were defined and tested. The optimal reference state was selected and the cutoff effect on the distance-dependent potentials investigated. The potentials were validated on the docking decoys sets, showing better performance than the existing potentials used in scoring of protein-protein docking results.ConclusionsA novel residue-based statistical potential for protein-protein docking was developed and validated on docking decoy sets. The results show that the scoring function DECK can successfully identify near-native protein-protein matches and thus is useful in protein docking. In addition to the practical application of the potentials, the study provides insights into the relative utility of the reference states, the scope of the distance dependence, and the coarse-graining of the potentials.

Crystal structure of rhodocytin, a ligand for the platelet‐activating receptor CLEC‐2

Binding of the snake venom protein rhodocytin to CLEC-2, a receptor on the surface of human platelets, initiates a signaling cascade leading to platelet activation and aggregation. We have previously solved the structure of CLEC-2. The 2.4 A resolution crystal structure of rhodocytin presented here demonstrates that it is the first snake venom or other C-type lectin-like protein to assemble as a non-disulfide linked (alphabeta)(2) tetramer. Rhodocytin is highly adapted for interaction with CLEC-2 and displays a concave binding surface, which is highly complementary to the experimentally determined binding interface on CLEC-2. Using computational dynamic methods, surface electrostatic charge and hydrophobicity analyses, and protein-protein docking predictions, we propose that the (alphabeta)(2) rhodocytin tetramer induces clustering of CLEC-2 receptors on the platelet surface, which will trigger major signaling events resulting in platelet activation and aggregation.

PyDock: Electrostatics and desolvation for effective scoring of rigid‐body protein–protein docking

The accurate scoring of rigid-body docking orientations represents one of the major difficulties in protein-protein docking prediction. Other challenges are the development of faster and more efficient sampling methods and the introduction of receptor and ligand flexibility during simulations. Overall, good discrimination of near-native docking poses from the very early stages of rigid-body protein docking is essential step before applying more costly interface refinement to the correct docking solutions. Here we explore a simple approach to scoring of rigid-body docking poses, which has been implemented in a program called pyDock. The scheme is based on Coulombic electrostatics with distance dependent dielectric constant, and implicit desolvation energy with atomic solvation parameters previously adjusted for rigid-body protein-protein docking. This scoring function is not highly dependent on specific geometry of the docking poses and therefore can be used in rigid-body docking sets generated by a variety of methods. We have tested the procedure in a large benchmark set of 80 unbound docking cases. The method is able to detect a near-native solution from 12,000 docking poses and place it within the 100 lowest-energy docking solutions in 56% of the cases, in a completely unrestricted manner and without any other additional information. More specifically, a near-native solution will lie within the top 20 solutions in 37% of the cases. The simplicity of the approach allows for a better understanding of the physical principles behind protein-protein association, and provides a fast tool for the evaluation of large sets of rigid-body docking poses in search of the near-native orientation.

Optimization of multiple‐sequence alignment based on multiple‐structure alignment

Routinely used multiple-sequence alignment methods use only sequence information. Consequently, they may produce inaccurate alignments. Multiple-structure alignment methods, on the other hand, optimize structural alignment by ignoring sequence information. Here, we present an optimization method that unifies sequence and structure information. The alignment score is based on standard amino acid substitution probabilities combined with newly computed three-dimensional structure alignment probabilities. The advantage of our alignment scheme is in its ability to produce more accurate multiple alignments. We demonstrate the usefulness of the method in three applications: 1) computing more accurate multiple-sequence alignments, 2) analyzing protein conformational changes, and 3) computation of amino acid structure-sequence conservation with application to protein-protein docking prediction. The method is available at http://bioinfo3d.cs.tau.ac.il/staccato/.

Improving CAPRI predictions: Optimized desolvation for rigid‐body docking

The ICM Docking and Interface Side-Chain Optimization (ICM-DISCO) showed promising predictive results during the first CAPRI experiment by successfully finding medium- or high-accuracy models in 3 of the 7 targets. A key factor was the ability to recognize near-native rigid-body geometries in a relatively low number of alternative docking poses, together with the successful refinement of the rigid-body docking interfaces. Since then, we have focused on improving the scoring function to optimally discriminate the near-native rigid-body conformations. For that, we have defined a new desolvation descriptor for rigid-body docking, based on atomic solvation parameters (ASPs) derived from octanol-water transfer experiments. This and other new approaches have been gradually incorporated into our docking procedure during our participation on the second CAPRI experiment. Overall, we produced reasonable models for 8 of the 9 official targets. Especially encouraging were those cases in which a homology model of 1 of the subunits had to be used during the docking simulations. And not less gratifying has been the successful prediction of antibody-antigen targets in a completely automatic, unrestrained fashion. In summary, our success rate (89%) shows a consistent improvement over the previous CAPRI rounds, and suggests that a correct desolvation description is key for improved protein-protein docking predictions.

Structural Characterization of Assemblies from Overall Shape and Subcomplex Compositions

We suggest structure characterization of macromolecular assemblies by combining assembly shape determined by electron cyromicroscopy with information about subunit proximity determined by affinity purification. To achieve this aim, structure characterization is expressed as a problem in satisfaction of spatial restraints that (1) represents subunits as spheres, (2) encodes information about the subunit excluded volume, assembly shape, and pulldowns in a scoring function, and (3) finds subunit configurations that satisfy the input restraints by an optimization of the scoring function. Testing of the approach with model systems suggests its feasibility.

3P009 探索空間の削減によるタンパク質-タンパク質ドッキング予測手法の高速化(蛋白質 A) 構造))

3P009 探索空間の削減によるタンパク質-タンパク質ドッキング予測手法の高速化(蛋白質 A) 構造))

2P053 球面調和関数に基づく基底関数による級数展開を用いた高速ドッキング予測法(蛋白質 A) 構造)

2P053 球面調和関数に基づく基底関数による級数展開を用いた高速ドッキング予測法(蛋白質 A) 構造)