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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.

We describe a method to predict protein-protein interactions (PPIs) formed between structured domains and short peptide motifs. We take an integrative approach based on consensus patterns of known motifs in databases, structures of domain-motif complexes from the PDB and various sources of non-structural evidence. We combine this set of clues using a Bayesian classifier that reports the likelihood of an interaction and obtain significantly improved prediction performance when compared to individual sources of evidence and to previously reported algorithms. Our Bayesian approach was integrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to interactions formed between two structured domains. Around 80,000 new domain-motif mediated interactions were predicted, thus enhancing PrePPI’s coverage of the human protein interactome., Author Summary Complexes formed between a structured domain on one protein and an unstructured peptide on another are ubiquitous. However, they are often quite difficult to detect experimentally. The development of computational approaches to predict domain-motif interactions is therefore an important goal. We report a method to predict domain-motif interactions using a Bayesian approach to integrate evidence from a variety of sources, including three-dimensional structural and non-structural information. The method was applied to the entire human proteome and showed significant improvement over existing methods. The method was incorporated into PrePPI, a computational pipeline for the prediction of protein-protein interactions that relies heavily on structural information. Approximately 80,000 new interactions were detected. The new PrePPI database provides easy access to about 400,000 human protein-protein interactions and should thus constitute a valuable resource in a variety of biological applications including the characterization of molecular interaction networks and, more generally, in the study of interactions mediated by proteins in families that may not be extensively studied experimentally.

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.

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.

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.