Zdock Server

[Crisoforo Schuhmacher]

Amongthe many recent advances in bioinformatics is the rapid accumulation of 3D structures of protein complexes through X-ray crystallization. These structures provide many insights on protein-protein interactions, facilitating rational drug development and the treatment of disease. However, not all protein complexes have been crystallized, so various computational techniques have been developed to address this situation. One of the most promising approaches is protein docking, where the structure of a complex between two proteins is predicted based on the independently crystallized structures of the components.

We have developed several protein docking algorithms. These include:

ZDOCK: Performs a full rigid-body search of docking orientations between two proteins. The current version, 3.0.2, includes performance optimization and a novel pairwise statistical energy potential.M-ZDOCK: A modification of ZDOCK to predict symmetric assemblies using the structure of a subunit.ZRANK: A docking refinement program developed to provide fast and accurate rescoring of models from initial-stage docking (e.g. from ZDOCK), as well as refined docking models (e.g. from RosettaDock).RDOCK: An older docking refinement protocol that utilizes structural minimization and rescoring of initial-stage docking models.All software is freely available to academic users at this site.

We additionally have several resources available to the community:

Benchmark: We have assembled a protein-protein docking benchmark, now in its fourth version with 176 test cases. More information here.ZDOCK Server: We maintain a protein docking server, permitting users to run the latest versions of ZDOCK.Decoy Sets: To facilitate the development and testing of docking algorithms, sets of ZDOCK predictions are available for the most recent version of the docking benchmark.In addition to the docking benchmark, superior performance of ZDOCK and ZRANK has been demonstrated in a community-wide protein docking blind test, CAPRI. Check this out for more details. For basic information on running ZDOCK, see this site.

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Summary: Protein-protein interactions are essential to cellular and immune function, and in many cases, because of the absence of an experimentally determined structure of the complex, these interactions must be modeled to obtain an understanding of their molecular basis. We present a user-friendly protein docking server, based on the rigid-body docking programs ZDOCK and M-ZDOCK, to predict structures of protein-protein complexes and symmetric multimers. With a goal of providing an accessible and intuitive interface, we provide options for users to guide the scoring and the selection of output models, in addition to dynamic visualization of input structures and output docking models. This server enables the research community to easily and quickly produce structural models of protein-protein complexes and symmetric multimers for their own analysis.

Computational prediction of the 3D structures of molecular interactions is a challenging area, often requiring significant computational resources to produce structural predictions with atomic-level accuracy. This can be particularly burdensome when modeling large sets of interactions, macromolecular assemblies, or interactions between flexible proteins. We previously developed a protein docking program, ZDOCK, which uses a fast Fourier transform to perform a 3D search of the spatial degrees of freedom between two molecules. By utilizing a pairwise statistical potential in the ZDOCK scoring function, there were notable gains in docking accuracy over previous versions, but this improvement in accuracy came at a substantial computational cost. In this study, we incorporated a recently developed 3D convolution library into ZDOCK, and additionally modified ZDOCK to dynamically orient the input proteins for more efficient convolution. These modifications resulted in an average of over 8.5-fold improvement in running time when tested on 176 cases in a newly released protein docking benchmark, as well as substantially less memory usage, with no loss in docking accuracy. We also applied these improvements to a previous version of ZDOCK that uses a simpler non-pairwise atomic potential, yielding an average speed improvement of over 5-fold on the docking benchmark, while maintaining predictive success. This permits the utilization of ZDOCK for more intensive tasks such as docking flexible molecules and modeling of interactomes, and can be run more readily by those with limited computational resources.

Copyright: 2011 Pierce et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was funded by National Institutes of Health grant GM084884. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Interactions between biomolecules are crucial to the function of biological systems, forming the basis of normal and aberrant cellular behavior, as well as defense against external pathogens. To fully understand these interactions, atomic-level descriptions of the structures of their binding interfaces are essential. While the structures of many protein-protein complexes have been characterized experimentally via x-ray crystallography and deposited in the Protein Data Bank (PDB; [1]), the majority of known complexes have not, providing an opportunity for predictive computational techniques to help elucidate these structures. Molecular docking approaches, which take two (or more) structures as input and predict the structure of their complex, are increasingly being used for this purpose [2].

Previously our laboratory developed the program ZDOCK, which uses a grid-based representation of two proteins and a 3-dimensional (3D) fast Fourier transform (FFT) to efficiently explore the rigid-body search space of docking positions [10]. The most recent version, ZDOCK 3.0, has a scoring function that includes shape complementarity, electrostatics, and a pairwise atomic statistical potential developed using contact propensities of transient protein complexes [11]. ZDOCK 3.0 showed vast improvements in its predictive ability versus the previous version when tested on a protein-protein docking benchmark [11], and has led to highly successful performance in the blind protein docking experiment, CAPRI [12], [13]. However, with the improved accuracy due to the pairwise statistical potential, the running time and memory usage of ZDOCK increased significantly, as seven FFTs (rather than two in the previous version, ZDOCK 2.3 [14]) needed to be computed per docking orientation.

To reduce this computational burden and make proteomic scale docking and ensemble docking approaches more tractable, we have developed a new version of ZDOCK that retains the predictive accuracy of ZDOCK 3.0 while vastly improving its computational performance. This was achieved by integrating an FFT library that was designed to improve 3D FFT performance [15], as well as several improvements to the molecular discretization to further reduce the grid size required to represent the input proteins. These optimizations were evaluated against 176 test cases in a newly released version of a protein docking benchmark [16], resulting in over 8.5-fold average improvement in running time. We also implemented these updates on ZDOCK 2.3, for those users who have pipelines or protocols in place with this tool (e.g. protein/DNA docking [17]), which resulted in 5.5-fold improvement in running time. Examining the test cases with the highest levels of running time improvement showed that the primary factor in improving performance is the reduced grid size due to the new FFT library.

Here we present major improvements to ZDOCK's initial orientation and FFT procedures (bold steps above), while not modifying the discretization protocols that embody the ZDOCK scoring function. Previous ZDOCK versions and their scoring terms include: ZDOCK 1.3 [10]: Grid-based shape complementarity, atomic contact energy (ACE; [20]), electrostatics: ZDOCK 2.1 [21]: Pairwise shape complementarity (PSC); ZDOCK 2.3 [14]: PSC, ACE, electrostatics; ZDOCK 3.0 [11]: PSC, interface atomic contact energy (IFACE), electrostatics.

After updating ZDOCK versions 3.0 and 2.3 with the Conv3D FFT library and improved molecular representation (detailed in the Implementation section), we tested these new versions (3.0.2 and 2.3.2) for their computational efficiency using all 176 unbound test cases of protein-protein docking Benchmark 4.0 [16]; results are given in Table 1. Each run of ZDOCK used default angular sampling (3,600 ligand rotations), and a single 2.8 GHz 64-bit Opteron processor with 8 GB available RAM. To test the improvements due to specific modifications, we also measured the performance of ZDOCK with Conv3D only (Step 1 in Implementation; 3.0.1 and 2.3.1), and Conv3D with improved centering (Steps 1 and 2; 3.0.2f and 2.3.2f).

The most dramatic improvements were seen for ZDOCK 3.0.2, with 18.9 minutes average running time for the docking benchmark, from an original average running time of 167.1 minutes. This is nearly three times less than the average running time for ZDOCK 2.3 on the docking benchmark. On average, this version had an 8.6-fold improvement in running time versus ZDOCK 3.0; this was significantly higher than the 6.4-fold improvement from Conv3D alone (ZDOCK 3.0.1), though integrating Conv3D was evidently responsible for the majority of the running time improvement. Required memory concomitantly was reduced for these ZDOCK improvements, with less than half of the memory for ZDOCK 3.0 required, on average, by ZDOCK 3.0.2 (256 MB, versus 700 MB for ZDOCK 3.0).

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