Masahito Ohue

Community-wide assessment of protein-interface modeling suggests improvements to design methodology

The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.

MEGADOCK: an all-to-all protein-protein interaction prediction system using tertiary structure data.

The elucidation of protein-protein interaction (PPI) networks is important for understanding cellular structure and function and structure-based drug design. However, the development of an effective method to conduct exhaustive PPI screening represents a computational challenge. We have been investigating a protein docking approach based on shape complementarity and physicochemical properties. We describe here the development of the protein-protein docking software package “MEGADOCK” that samples an extremely large number of protein dockings at high speed. MEGADOCK reduces the calculation time required for docking by using several techniques such as a novel scoring function called the real Pairwise Shape Complementarity (rPSC) score. We showed that MEGADOCK is capable of exhaustive PPI screening by completing docking calculations 7.5 times faster than the conventional docking software, ZDOCK, while maintaining an acceptable level of accuracy. When MEGADOCK was applied to a subset of a general benchmark dataset to predict 120 relevant interacting pairs from 120 x 120 = 14,400 combinations of proteins, an F-measure value of 0.231 was obtained. Further, we showed that MEGADOCK can be applied to a large-scale protein-protein interaction-screening problem with accuracy better than random. When our approach is combined with parallel high-performance computing systems, it is now feasible to search and analyze protein-protein interactions while taking into account three-dimensional structures at the interactome scale. MEGADOCK is freely available at http://www.bi.cs.titech.ac.jp/megadock.

MEGADOCK 4.0: an ultra-high-performance protein-protein docking software for heterogeneous supercomputers.

Summary: The application of protein–protein docking in large-scale interactome analysis is a major challenge in structural bioinformatics and requires huge computing resources. In this work, we present MEGADOCK 4.0, an FFT-based docking software that makes extensive use of recent heterogeneous supercomputers and shows powerful, scalable performance of >97% strong scaling. Availability and Implementation: MEGADOCK 4.0 is written in C++ with OpenMPI and NVIDIA CUDA 5.0 (or later) and is freely available to all academic and non-profit users at: http://www.bi.cs.titech.ac.jp/megadock. Contact: akiyama@cs.titech.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online

MEGADOCK 3.0: a high-performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments

BackgroundProtein-protein interaction (PPI) plays a core role in cellular functions. Massively parallel supercomputing systems have been actively developed over the past few years, which enable large-scale biological problems to be solved, such as PPI network prediction based on tertiary structures.ResultsWe have developed a high throughput and ultra-fast PPI prediction system based on rigid docking, “MEGADOCK”, by employing a hybrid parallelization (MPI/OpenMP) technique assuming usages on massively parallel supercomputing systems. MEGADOCK displays significantly faster processing speed in the rigid-body docking process that leads to full utilization of protein tertiary structural data for large-scale and network-level problems in systems biology. Moreover, the system was scalable as shown by measurements carried out on two supercomputing environments. We then conducted prediction of biological PPI networks using the post-docking analysis.ConclusionsWe present a new protein-protein docking engine aimed at exhaustive docking of mega-order numbers of protein pairs. The system was shown to be scalable by running on thousands of nodes. The software package is available at: http://www.bi.cs.titech.ac.jp/megadock/k/.

Protein-protein interaction network prediction by using rigid-body docking tools: application to bacterial chemotaxis.

Core elements of cell regulation are made up of protein-protein interaction (PPI) networks. However, many parts of the cell regulatory systems include unknown PPIs. To approach this problem, we have developed a computational method of high-throughput PPI network prediction based on all-to-all rigid-body docking of protein tertiary structures. The prediction system accepts a set of data comprising protein tertiary structures as input and generates a list of possible interacting pairs from all the combinations as output. A crucial advantage of this docking based method is in providing predictions of protein pairs that increases our understanding of biological pathways by analyzing the structures of candidate complex structures, which gives insight into novel interaction mechanisms. Although such exhaustive docking calculation requires massive computational resources, recent advancements in the computational sciences have made such large-scale calculations feasible. different rigid-body docking tools with different scoring models. We found that the predicted interactions were different between the results from the two tools. When the positive predictions from both of the docking tools were combined, all the core signaling interactions were correctly predicted with the exception of interactions activated by protein phosphorylation. Large-scale PPI prediction using tertiary structures is an effective approach that has a wide range of potential applications. This method is especially useful for identifying novel PPIs of new pathways that control cellular behavior.

Highly precise protein-protein interaction prediction based on consensus between template-based and de novo docking methods

BackgroundElucidation of protein-protein interaction (PPI) networks is important for understanding disease mechanisms and for drug discovery. Tertiary-structure-based in silico PPI prediction methods have been developed with two typical approaches: a method based on template matching with known protein structures and a method based on de novo protein docking. However, the template-based method has a narrow applicable range because of its use of template information, and the de novo docking based method does not have good prediction performance. In addition, both of these in silico prediction methods have insufficient precision, and require validation of the predicted PPIs by biological experiments, leading to considerable expenditure; therefore, PPI prediction methods with greater precision are needed.ResultsWe have proposed a new structure-based PPI prediction method by combining template-based prediction and de novo docking prediction. When we applied the method to the human apoptosis signaling pathway, we obtained a precision value of 0.333, which is higher than that achieved using conventional methods (0.231 for PRISM, a template-based method, and 0.145 for MEGADOCK, a non-template-based method), while maintaining an F-measure value (0.285) comparable to that obtained using conventional methods (0.296 for PRISM, and 0.220 for MEGADOCK).ConclusionsOur consensus method successfully predicted a PPI network with greater precision than conventional template/non-template methods, which may thus reduce the cost of validation by laboratory experiments for confirming novel PPIs from predicted PPIs. Therefore, our method may serve as an aid for promoting interactome analysis.

Improvement of the protein---protein docking prediction by introducing a simple hydrophobic interaction model: an application to interaction pathway analysis

We propose a new hydrophobic interaction model that applies atomic contact energy for our protein---protein docking software, MEGADOCK. Previously, this software used only two score terms, shape complementarity and electrostatic interaction. We develop a modified score function incorporating the hydrophobic interaction effect. Using the proposed score function, MEGADOCK can calculate three physico-chemical effects with only one correlation function. We evaluate the proposed system against three other protein---protein docking score models, and we confirm that our method displays better performance than the original MEGADOCK system and is faster than both ZDOCK systems. Thus, we successfully improve accuracy without loosing speed.

Protein-protein docking on hardware accelerators: comparison of GPU and MIC architectures

BackgroundThe hardware accelerators will provide solutions to computationally complex problems in bioinformatics fields. However, the effect of acceleration depends on the nature of the application, thus selection of an appropriate accelerator requires some consideration.ResultsIn the present study, we compared the effects of acceleration using graphics processing unit (GPU) and many integrated core (MIC) on the speed of fast Fourier transform (FFT)-based protein-protein docking calculation. The GPU implementation performed the protein-protein docking calculations approximately five times faster than the MIC offload mode implementation. The MIC native mode implementation has the advantage in the implementation costs. However, the performance was worse with larger protein pairs because of memory limitations.ConclusionThe results suggest that GPU is more suitable than MIC for accelerating FFT-based protein-protein docking applications.

MEGADOCK-GPU: Acceleration of Protein-Protein Docking Calculation on GPUs

Protein-protein docking is a method for predicting the protein complex structure from monomeric protein structures. Because protein structural information has been increased and the application field has been expanded to more difficult ones such as interactome prediction, a faster protein-protein docking method has been eagerly demanded. MEGADOCK is fast protein-protein docking software but more acceleration is demanded for an interactome prediction, which is composed of millions of protein pairs. In this paper, we developed an ultra-fast protein-protein docking software named MEGADOCK-GPU by using general purpose GPU computing techniques. We implemented a system that utilizes all CPU cores and GPUs in a computation node. As results, MEGADOCK-GPU on 12 CPU cores and 3 GPUs achieved a calculation speed that was 37.0 times faster than MEGADOCK on 1 CPU core. The novel docking software will facilitate the application of docking techniques to assist large-scale protein interaction network analyses. MEGADOCK-GPU is freely available at http://www.bi.cs.titech.ac.jp/megadock/gpu/.

Exploring the selectivity of inhibitor complexes with Bcl-2 and Bcl-XL: A molecular dynamics simulation approach

B-cell lymphoma 2 (Bcl-2) family proteins are potential drug targets in cancer and have a relatively flat and flexible binding site. ABT-199 is one of the most promising selective Bcl-2 inhibitors, and A-1155463 selectively inhibits Bcl-XL. Although the amino acid sequences of the binding sites of these two inhibitors are similar, the inhibitors selectively bind the target protein. In order to determine the origin of the selectivity of these inhibitors, we conducted molecular dynamics simulations using protein-inhibitor modeling. We confirmed that ASP103 of Bcl-2 is a key residue and that hydrogen bonding between ASP103 and ABT-199 confers the Bcl-2 selectivity of this inhibitor. For Bcl-XL selectivity, the secondary structure of α-helix 3 is a key factor. PHE105, SER106, and LEU108 in the loose α-helix 3 interact with A-1155463 to confer Bcl-XL selectivity. These findings provide important insights into the molecular mechanisms of selective inhibitors of Bcl-2 family proteins.