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.
Recent developments in in-cell NMR techniques have allowed us to study proteins in detail inside living eukaryotic cells. In order to complement the existing protocols, and to extend the range of possible applications, we introduce a novel approach for observing in-cell NMR spectra using the sf9 cell/baculovirus system. High-resolution 2D (1)H-(15)N correlation spectra were observed for four model proteins expressed in sf9 cells. Furthermore, 3D triple-resonance NMR spectra of the Streptococcus protein G B1 domain were observed in sf9 cells by using nonlinear sampling to overcome the short lifetime of the samples and the low abundance of the labeled protein. The data were processed with a quantitative maximum entropy algorithm. These were assigned ab initio, yielding approximately 80% of the expected backbone NMR resonances. Well-resolved NOE cross peaks could be identified in the 3D (15)N-separated NOESY spectrum, suggesting that structural analysis of this size of protein will be feasible in sf9 cells.
To elucidate the partners in protein-protein interactions (PPIs), we previously proposed an affinity prediction method called affinity evaluation and prediction (AEP), which is based on the shape complementarity characteristics between proteins. The structures of the protein complexes obtained in our shape complementarity evaluation were selected by a newly developed clustering method called grouping. Our previous experiments showed that AEP gave accuracies that differed with the data composition and scale. In this study, we set a data scale (84 x 84 = 7056 protein pairs) including 84 biologically relevant complexes and then designed 225 parameter sets based on four key parameters related to the grouping and the calculation of affinity scores. As a result of receiver operating characteristic analysis, we obtained 27.4% sensitivity (= recall), 91.0% specificity, 3.5% precision, 90.2% accuracy, 6.3% F-measure(max), and an area under the curve of 0.585. Chiefly by optimization of the grouping, AEP was able to provide prediction accuracy for a maximum F-measure that statistically distinguished 23 target complexes among 84 protein pairs. Moreover, the active sites of these complexes were successfully predicted with high accuracy (i.e., 2.37 angstroms in 1CGI and 2.38 angstroms in 1PPE) of interface RMSD. To assess the improvement in accuracy we compared the results of AEP of different data sets and of tentative methods using ZDOCK 3.0.1 or ZRANK scores.
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