Accelerating Protein Docking in ZDOCK Using an Advanced 3D Convolution Library

By Brian G. Pierce, Yuichiro Hourai, Zhiping Weng. PLoS ONE, 2011

608 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a303df452657be308c0f8885ae733e0900bce295

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Citations in the corpus

Improving peptide-protein docking with AlphaFold-Multimer using forced sampling (2021, background): “ZDOCK is a fast and accurate FFT-based rigid-body docking method which utilizes pairwise statistical potentials; it is a staple method for benchmarking protein – protein docking performance (Pierce and Weng, 2008; Moal et al., 2013; Pierce et al., 2014; Wallner and Mirabello, 2017; Yan and Huang, 20”
InterEvDock2: an expanded server for protein docking using evolutionary and biological information from homology models and multimeric inputs (2018, background): “2 (45), assessed on the same 812 complexes from the PPI4DOCK benchmark.”
Software for molecular docking: a review (2017, methodology): “…transform-based algorithms is further accelerated computationally with the help of advanced software packages, such as the 3D convolution library (Pierce et al. 2011), and new hardware technologies, such as the graphics processing unit (GPU) (Ritchie and Venkatraman 2010) and Cell BE processor…”
Automatic structure prediction of oligomeric assemblies using Robetta in CASP12 (2017, background): “…and oligomeric state assignment simultaneously; many efforts subdivide the problem into 2 tasks 1 : structure prediction, 2 – 5 which considers determination of the constituent monomers, and protein docking, 6 – 9 which considers predicting the structure of assemblies from largely rigid monomers.”
HawkDock: a web server to predict and analyze the protein–protein complex based on computational docking and MM/GBSA (2019, methodology): “2, a widely used rigid-body docking program (46), based on the analysis of Dataset I.”
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants (2021, methodology): “The integration of residue-and atom-based potentials for docking (IRAD) 39 scoring function was used to rank the ZDOCK output models.”
ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers (2014, background): “…on protein–protein docking benchmarks, with 4 70% success in the top 1000 predictions for rigid-body cases in the most recent benchmark version (Pierce et al ., 2011), and consistent success (acceptable or better predictions for 22 of the last 35 submitted targets) in the international…”
Template-based structure modeling of protein-protein interactions (2013, methodology): “During the testing of SPRING, a TBM method, comparisons were made with the latest version of the template-free docking algorithm ZDOCK [63] on the docking benchmark 3.”
Efficient Flexible Backbone Protein-Protein Docking for Challenging Targets (2017, methodology): “Table 2 summarizes recent published results from five leading docking methods: HADDOCK (Vangone et al. , 2017), iATTRACT (Schindler et al. , 2015), ClusPro (Kozakov et al. , 2017), ZDOCK (Pierce et al. , 2011) and RosettaDock 3.2 (Chaudhury et al. , 2011).”
Protein Docking Model Evaluation by Graph Neural Networks (2020, methodology): “In ab initio methods, various approaches were used for molecular structure representations (Venkatraman et al., 2009; Pierce et al., 2011).”
Antibody i-Patch prediction of the antibody binding site improves rigid local antibody-antigen docking. (2013, methodology): “The Antibody i-Patch ZDOCK pipeline can produce results for our targets in minutes rather than the hundreds of CPU hours needed for the flexible methods (Sircar and Gray, 2010; Pierce et al., 2011).”