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PrePPI (http://bhapp.c2b2.columbia.edu/PrePPI) is a database that combines predicted and experimentally determined protein–protein interactions (PPIs) using a Bayesian framework. Predicted interactions are assigned probabilities of being correct, which are derived from calculated likelihood ratios (LRs) by combining structural, functional, evolutionary and expression information, with the most important contribution coming from structure. Experimentally determined interactions are compiled from a set of public databases that manually collect PPIs from the literature and are also assigned LRs. A final probability is then assigned to every interaction by combining the LRs for both predicted and experimentally determined interactions. The current version of PrePPI contains ∼2 million PPIs that have a probability more than ∼0.1 of which ∼60 000 PPIs for yeast and ∼370 000 PPIs for human are considered high confidence (probability greater than 0.5). The PrePPI database constitutes an integrated resource that enables users to examine aggregate information on PPIs, including both known and potentially novel interactions, and that provides structural models for many of the PPIs.

Cache techniques are an efficient tool to reduce latency times in transfer operations through Grid systems. Although different approximations to introduce cache facilities into Grid computing have already been studied, they require intrusive modifications of Grid software and hardware. Here, we propose an end-to-end cache system that is implemented over scheduling services. This cache system requires neither changes in the Grid software nor introduction of new software in the Grid resources. Parallel Grid adaptation of many high-throughput computing applications that use the same data intensively could enjoy great benefits from our cache system. The maintenance of cacheable data in the resources of already-executed tasks allows faster executions of future tasks assigned to the same resources. To analyze the performance of our end-to-end cache system, we tested it with a new protein—protein docking application. The obtained results confirm our cache system’s robustness and efficiency gain for this kind of high-throughput application.

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.

8 pags, 2 figs, 4 tabs. -- Supplementary data are available at Bioinformatics online., Motivation: Dynamic simulations of systems with biologically relevant sizes and time scales are critical for understanding macromolecular functioning. Coarse-grained representations combined with normal mode analysis (NMA) have been established as an alternative to atomistic simulations. The versatility and efficiency of current approaches normally based on Cartesian coordinates can be greatly enhanced with internal coordinates (IC). Results: Here, we present a new versatile tool chest to explore conformational flexibility of both protein and nucleic acid structures using NMA in IC. Consideration of dihedral angles as variables reduces the computational cost and non-physical distortions of classical Cartesian NMA methods. Our proposed framework operates at different coarse-grained levels and offers an efficient framework to conduct NMA-based conformational studies, including standard vibrational analysis, Monte-Carlo simulations or pathway exploration. Examples of these approaches are shown to demonstrate its applicability, robustness and efficiency. © The Author 2011. Published by Oxford University Press. All rights reserved., MICNN BFU2009-09552; Human Frontier Science Program RGP0039/2008

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.

Summary: DFprot is a web-based server for predicting main-chain deformability from a single protein conformation. The server automatically performs a normal-mode analysis (NMA) of the uploaded structure and calculates its capability to deform at each of its residues. Non-specialists can easily and rapidly obtain a quantitative first approximation of the flexibility of their structures with a simple and efficient interface. Availability: http://sbg.cib.csic.es/Software/DFprot Contact: pablo@cib.csic.es

Protein-protein interfaces have been evolutionarily-designed to enable transduction between the interacting proteins. Thus, we hypothesize that analysis of the dynamics of the complex can reveal details about the nature of the interaction, and in particular whether it is obligatory, i.e., persists throughout the entire lifetime of the proteins, or not. Indeed, normal mode analysis, using the Gaussian network model, shows that for the most part obligatory and non-obligatory complexes differ in their decomposition into dynamic domains, i.e., the mobile elements of the protein complex. The dynamic domains of obligatory complexes often mix segments from the interacting chains, and the hinges between them do not overlap with the interface between the chains. In contrast, in non-obligatory complexes the interface often hinges between dynamic domains, held together through few anchor residues on one side of the interface that interact with their counterpart grooves in the other end. In automatic analysis, 117 of 139 obligatory (84.2%) and 203 of 246 non-obligatory (82.5%) complexes are correctly classified by our method: DynaFace. We further use DynaFace to predict obligatory and non-obligatory interactions among a set of 300 putative protein complexes. DynaFace is available at: http://safir.prc.boun.edu.tr/dynaface., Author Summary Protein-protein interactions mediate, in essence, all inter- and intra-cellular processes. Thus, understanding their molecular mechanism is of utmost importance. Here we focus on one mechanistic aspect: differentiation between obligatory interactions, which persist throughout the entire lifetime of the protein complex, and non-obligatory, which do not. For proper function, a protein complex should facilitate transduction between the interacting proteins. Therefore the complex’s dynamics should reveal whether it is obligatory or non-obligatory. Indeed, normal mode analysis shows that the dynamic domains of obligatory complexes often mix segments from the interacting chains. In contrast, in non-obligatory complexes the inter-chain interface often hinges between dynamic domains, held together through few anchor residues. An automated methodology based on these observations correctly classifies over 80% of the interfaces in a test set. We use it also to predict obligatory and non-obligatory interactions among putative protein complexes. DynaFace, a web-server implementation of the methodology, is available at: http://safir.prc.boun.edu.tr/dynaface.

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.

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)