Search

The procedure for screening large databases of small chemical compounds to select likely drug candidates by computational means is very time demanding. Here, we present and evaluate a new method for virtual screening (VS) that combines the efficiency of spherical harmonic approximations to accelerate the rotational part of a docking search with multicore and GPU parallelism. To validate these novel parallel algorithms, we used standard benchmark cases. The obtained results are comparable to those generated via state-of-the-art VS docking approximations, but with a considerable gain in efficiency. GPU implementation speedups of more than 30-fold with respect to a single core CPU were achieved, reducing the docking time for a single ligand to only 50 milliseconds. The achieved efficiency and the accuracy on standard blind benchmarks demonstrate the applicability and robustness of this approach.

The distance-dependent knowledge-based DrugScorePPI potentials, previously developed for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes, are evaluated as a scoring and objective function for the structure prediction of protein-protein complexes. When applied for ranking >unbound perturbation> (>unbound docking>) decoys generated by Baker and coworkers a 4-fold (1.5-fold) enrichment of acceptable docking solutions in the top ranks compared to a random selection is found. When applied as an objective function in FRODOCK for bound protein-protein docking on 97 complexes of the ZDOCK benchmark 3.0, DrugScorePPI/FRODOCK finds up to 10% (15%) more high accuracy solutions in the top 1 (top 10) predictions than the original FRODOCK implementation. When used as an objective function for global unbound protein-protein docking, fair docking success rates are obtained, which improve by ∼2-fold to 18% (58%) for an at least acceptable solution in the top 10 (top 100) predictions when performing knowledge-driven unbound docking. This suggests that DrugScorePPI balances well several different types of interactions important for protein-protein recognition. The results are discussed in view of the influence of crystal packing and the type of protein-protein complex docked. Finally, a simple criterion is provided with which to estimate a priori if unbound docking with DrugScorePPI/ FRODOCK will be successful. © 2014 Krüger et al.

Motivation: Prediction of protein–protein complexes from the coordinates of their unbound components usually starts by generating many potential predictions from a rigid-body 6D search followed by a second stage that aims to refine such predictions. Here, we present and evaluate a new method to effectively address the complexity and sampling requirements of the initial exhaustive search. In this approach we combine the projection of the interaction terms into 3D grid-based potentials with the efficiency of spherical harmonics approximations to accelerate the search. The binding energy upon complex formation is approximated as a correlation function composed of van der Waals, electrostatics and desolvation potential terms. The interaction-energy minima are identified by a novel, fast and exhaustive rotational docking search combined with a simple translational scanning. Results obtained on standard protein–protein benchmarks demonstrate its general applicability and robustness. The accuracy is comparable to that of existing state-of-the-art initial exhaustive rigid-body docking tools, but achieving superior efficiency. Moreover, a parallel version of the method performs the docking search in just a few minutes, opening new application opportunities in the current ‘omics’ world. Availability: http://sbg.cib.csic.es/Software/FRODOCK/ Contact: Pablo@cib.csic.es Supplementary information: Supplementary data are available at Bioinformatics online.

Rigid body fitting is the common way to interpret the 3D information contained in a electron microscopy (3DEM) low resolution density map in terms of its available 3D atomic resolution structural components. This fitting process, termed multi-resolution docking, consists in localizing atomic resolution structures into the 3D EM map by means of an exhaustive search of all possible relative rotations and translations. In addition to the cost of a single search, the necessity to carry out multiple searches with many different structures makes this problem appropriate for high performance computing (HPC). The Grid Computing paradigm provides such computing power for this type of resource-intensive scientific applications allowing the access to large resource pools conformed from shared assets of different centres or administration entities. Here, we present an efficient Grid approach for performing the multi-resolution docking searches. This approach has been designed over the GridWay Metascheduler. We show the suitability of the adaptation of the problem to the Grid paradigm. Results showing the high efficiency achieved are discussed together with the analysis of the performance obtained over the Grid testbed employed.

Motivation: Efficient fitting tools are needed to take advantage of a fast growth of atomic models of protein domains from crystallography or comparative modeling, and low-resolution density maps of larger molecular assemblies. Here, we report a novel fitting algorithm for the exhaustive and fast overlay of partial high-resolution models into a low-resolution density map. The method incorporates a fast rotational search based on spherical harmonics (SH) combined with a simple translational scanning. Results: This novel combination makes it possible to accurately dock atomic structures into low-resolution electron-density maps in times ranging from seconds to a few minutes. The high-efficiency achieved with simulated and experimental test cases preserves the exhaustiveness needed in these heterogeneous-resolution merging tools. The results demonstrate its efficiency, robustness and high-throughput coverage. Availability: Contact: pablo@cib.csic.es Supplementary information: Supplementary data are available at Bioinformatics online.

Protein-protein complexes play key roles in all cellular signal transduction processes. Here, we present a fast and accurate computational approach to predict protein-protein interactions. The approach is based on DrugScorePPI, a knowledge-based scoring function for which pair potentials were derived from 851 complex structures and adapted against 309 experimental alanine scanning results. We developed the DrugScorePPI webserver [1], accessible at http://cpclab.uni-duesseldorf.de/dsppi, that is intended for identifying hotspot residues in protein-protein interfaces. For this, it allows performing computational alanine scanning of a protein-protein interface within a few minutes. Our approach has been successfully validated by application to an external test set of 22 alanine mutations in the interface of Ras/RalGDS and outperformed the widely used CC/PBSA, FoldX, and Robetta methods [1]. Next, DrugScorePPI was teamed with FRODOCK [2], a fast FFT-based protein-protein docking tool, in order to predict 3D structures of protein-protein complexes. When applied to datasets of 54 bound-bound (I) and 54 unbound-unbound (II) test cases, convincing results were obtained (docking success rate for complexes with rmsd < 10 A: I: ~80%; II: ~50%). Thus, we set out to evaluate whether our approach of deformable potential grids [3], previously developed for protein-ligand docking, also provides an accurate and efficient means for representing intermolecular interactions in fully-flexible protein-protein docking. The underlying idea is to adapt a 3D grid of potential field values, pre-calculated from an initial protein conformation by DrugScorePPI, to another conformation by moving grid intersection points in space, but keeping the potential field values constant. Protein movements are thereby translated into grid intersection displacements by coupling protein atoms to nearby grid intersection points by means of harmonic springs and modelling the irregular, deformable 3D grid as a homogeneous linear elastic body applying elasticity theory. Thus, new protein conformations can be sampled during a docking run without the need to re-calculate potential field values.

Detailed knowledge of macromolecular structure is essential for the understanding of how the cellular machines work. Rigid body fitting is the common way to interpret the information contained in a 3D electron microscope (3DEM) medium-low resolution map in terms of its available atomic structural components. This fitting process, termed multi-resolution docking, consists in localizing atomic resolution structures into the 3DEM map by means of an exhaustive search of all possible relative rotations and translations. This exhaustive search is a highly computing demanding process and several search queries are also typically needed to select good fitting structures. Here, we present a novel and efficient Grid approach for performing these docking searches. This approach has been designed over the Gridway meta-scheduler. Results showing the high efficiency achieved are discussed together with the corresponding analysis of the performance obtained. The experiments were conducted on a Grid testbed built up from resources inside EGEE (LCG version of the pre-WS Globus components), the European production-level grid infrastructure, and resources from a research testbed based on the Globus Toolkit 4 (Web services components)