Protein-protein Complex Structure Prediction Research Articles

Inclusion of Desolvation Energy into Protein-Protein Docking through Atomic Contact Potentials.

Protein-protein interactions are crucial in many biological processes. Therefore, determining the structure of a protein-protein complex is valuable for understanding its molecular mechanisms and developing drugs. Molecular docking is a powerful computational tool in the prediction of protein-protein complex structures, in which a scoring function with good performance is very important. In this study, we have proposed a hybrid scoring function of atomic contact-based desolvation energies and distance-dependent interatomic potentials for protein-protein interactions, named HITScorePP, where the atomic contact desolvation energies were derived using an iterative method and the distance-dependent potentials were directly taken from our ITScorePP scoring function. Integrating the hybrid scoring function into our fast Fourier transform (FFT) based HDOCK docking scheme, the updated docking program, named HDOCK2.0, significantly improved the docking performance on the 55 newly added complexes in the protein docking benchmark 5.0 and a data set of 19 antibacterial protein complexes. HDOCK2.0 was also compared with four other state-of-the-art docking programs including Rosetta, ZDOCK3.0.2, FRODOCK3.0, ATTRACT, and PatchDock and obtained the overall best performance in binding mode predictions. These results demonstrated the accuracy of our hybrid scoring function and the necessity of included desolvation effects in protein-protein docking.

Performance of human and server prediction in CAPRI rounds 38‐45

CAPRI challenges offer a variety of blind tests for protein-protein interaction prediction. In CAPRI Rounds 38-45, we generated a set of putative binding modes for each target with an FFT-based docking algorithm, and then scored and ranked these binding modes with a proprietary scoring function, ITScorePP. We have also developed a novel web server, Rebipp. The algorithm utilizes information retrieval to identify relevant biological information to significantly reduce the search space for a particular protein. In parallel, we have also constructed a GPU-based docking server, MDockPP, for protein-protein complex structure prediction. Here, the performance of our protocol in CAPRI rounds 38-45 is reported, which include 16 docking and scoring targets. Among them, three targets contain multiple interfaces: Targets 124, 125, and 136 have 2, 4, and 3 interfaces, respectively. In the predictor experiments, we predicted correct binding modes for nine targets, including one high-accuracy interface, six medium-accuracy binding modes, and six acceptable-accuracy binding modes. For the docking server prediction experiments, we predicted correct binding modes for eight targets, including one high-accuracy, three medium-accuracy, and five acceptable-accuracy binding modes.

EvERdock BAI: Machine-learning-guided selection of protein-protein complex structure.

Computational techniques for accurate and efficient prediction of protein-protein complex structures are widely used for elucidating protein-protein interactions, which play important roles in biological systems. Recently, it has been reported that selecting a structure similar to the native structure among generated structure candidates (decoys) is possible by calculating binding free energies of the decoys based on all-atom molecular dynamics (MD) simulations with explicit solvent and the solution theory in the energy representation, which is called evERdock. A recent version of evERdock achieves a higher-accuracy decoy selection by introducing MD relaxation and multiple MD simulations/energy calculations; however, huge computational cost is required. In this paper, we propose an efficient decoy selection method using evERdock and the best arm identification (BAI) framework, which is one of the techniques of reinforcement learning. The BAI framework realizes an efficient selection by suppressing calculations for nonpromising decoys and preferentially calculating for the promising ones. We evaluate the performance of the proposed method for decoy selection problems of three protein-protein complex systems. Their results show that computational costs are successfully reduced by a factor of 4.05 (in the best case) compared to a standard decoy selection approach without sacrificing accuracy.

Improved performance in CAPRI round 37 using LZerD docking and template-based modeling with combined scoring functions.

We report our group's performance for protein-protein complex structure prediction and scoring in Round 37 of the Critical Assessment of PRediction of Interactions (CAPRI), an objective assessment of protein-protein complex modeling. We demonstrated noticeable improvement in both prediction and scoring compared to previous rounds of CAPRI, with our human predictor group near the top of the rankings and our server scorer group at the top. This is the first time in CAPRI that a server has been the top scorer group. To predict protein-protein complex structures, we used both multi-chain template-based modeling (TBM) and our protein-protein docking program, LZerD. LZerD represents protein surfaces using 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. Because 3DZD are a soft representation of the protein surface, LZerD is tolerant to small conformational changes, making it well suited to docking unbound and TBM structures. The key to our improved performance in CAPRI Round 37 was to combine multi-chain TBM and docking. As opposed to our previous strategy of performing docking for all target complexes, we used TBM when multi-chain templates were available and docking otherwise. We also describe the combination of multiple scoring functions used by our server scorer group, which achieved the top rank for the scorer phase.

Protein-Protein Complex Structure Prediction using the Solution Theory in the Energy Representation

Proteins conduct their functions through interactions with other molecules. Thus, accurate prediction of protein complex is a key to understand functional mechanisms. We have developed a method to evaluate binding free energy differences of complex models generated by docking prediction using the all-atom molecular dynamics simulation and solution theory in the energy representation. By evaluating binding free energy difference with the method, we previously show that “near-native” models similar to crystal structure are successfully selected as the lowest energy structures in two protein-protein complexes, bovine trypsin with CMTI-1 squash inhibitor and RNase SA with barstar [Takemura, J. Chem. Phys. 2012]. The method requires relatively short MD simulations (2 ns) and can calculate binding free energy difference among several hundreds of complex models. As a protein-protein complex structure prediction method, we combined the evaluation method with CyClus [Omori, Proteins, 2013] that performs fast clustering/reranking using a cylindrical approximation of interface and improves the results of the rigid-body docking.The initial complex models were prepared using the protein-protein rigid body docking program, ZDOCK. The generated complex models were then clustered and reranked using CyClus. Free energy analysis for top 100 or 300 complex models returned by CyClus were conducted using conformational energies, solute entropies, and solvation free energies calculated with the solution theory in the energy representation. Our analysis improved the results of CyClus and the complex models similar to the native structure have the lowest binding free energies, suggesting that this procedure is effective in protein-protein complex structure predictions. We further improved the procedure by placing interface waters into protein-protein interface before free energy evaluations.

1P007 エネルギー表示溶液理論を用いた蛋白質複合体構造予測(蛋白質 : 構造,ポスター,第52回日本生物物理学会年会(2014年度))

1P007 エネルギー表示溶液理論を用いた蛋白質複合体構造予測(蛋白質 : 構造,ポスター,第52回日本生物物理学会年会(2014年度))

Evaluating template-based and template-free protein-protein complex structure prediction

We compared the performance of template-free (docking) and template-based methods for the prediction of protein-protein complex structures. We found similar performance for a template-based method based on threading (COTH) and another template-based method based on structural alignment (PRISM). The template-based methods showed similar performance to a docking method (ZDOCK) when the latter was allowed one prediction for each complex, but when the same number of predictions was allowed for each method, the docking approach outperformed template-based approaches. We identified strengths and weaknesses in each method. Template-based approaches were better able to handle complexes that involved conformational changes upon binding. Furthermore, the threading-based and docking methods were better than the structural-alignment-based method for enzyme-inhibitor complex prediction. Finally, we show that the near-native (correct) predictions were generally not shared by the various approaches, suggesting that integrating their results could be the superior strategy.

PyDockWEB: a web server for rigid-body protein–protein docking using electrostatics and desolvation scoring

pyDockWEB is a web server for the rigid-body docking prediction of protein-protein complex structures using a new version of the pyDock scoring algorithm. We use here a new custom parallel FTDock implementation, with adjusted grid size for optimal FFT calculations, and a new version of pyDock, which dramatically speeds up calculations while keeping the same predictive accuracy. Given the 3D coordinates of two interacting proteins, pyDockWEB returns the best docking orientations as scored mainly by electrostatics and desolvation energy. The server does not require registration by the user and is freely accessible for academics at http://life.bsc.es/servlet/pydock. Supplementary data are available at Bioinformatics online.

Refining near‐native protein–protein docking decoys by local resampling and energy minimization

How to refine a near-native structure to make it closer to its native conformation is an unsolved problem in protein-structure and protein-protein complex-structure prediction. In this article, we first test several scoring functions for selecting locally resampled near-native protein-protein docking conformations and then propose a computationally efficient protocol for structure refinement via local resampling and energy minimization. The proposed method employs a statistical energy function based on a Distance-scaled Ideal-gas REference state (DFIRE) as an initial filter and an empirical energy function EMPIRE (EMpirical Protein-InteRaction Energy) for optimization and re-ranking. Significant improvement of final top-1 ranked structures over initial near-native structures is observed in the ZDOCK 2.3 decoy set for Benchmark 1.0 (74% whose global rmsd reduced by 0.5 A or more and only 7% increased by 0.5 A or more). Less significant improvement is observed for Benchmark 2.0 (38% versus 33%). Possible reasons are discussed.