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Contains fulltext : 244215.pdf (Publisher’s version ) (Open Access), Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.

Contains fulltext : 220863.pdf (publisher's version ) (Open Access), Computational docking is a promising tool to model three-dimensional (3D) structures of protein–protein complexes, which provides fundamental insights of protein functions in the cellular life. Singling out near-native models from the huge pool of generated docking models (referred to as the scoring problem) remains as a major challenge in computational docking. We recently published iScore, a novel graph kernel based scoring function. iScore ranks docking models based on their interface graph similarities to the training interface graph set. iScore uses a support vector machine approach with random-walk graph kernels to classify and rank protein–protein interfaces. Here, we present the software for iScore. The software provides executable scripts that fully automate the computational workflow. In addition, the creation and analysis of the interface graph can be distributed across different processes using Message Passing interface (MPI) and can be offloaded to GPUs thanks to dedicated CUDA kernels.

Item does not contain fulltext, The protocols currently used for protein structure determination by nuclear magnetic resonance (NMR) depend on the determination of a large number of upper distance limits for proton-proton pairs. Typically, this task is performed manually by an experienced researcher rather than automatically by using a specific computer program. To assess whether it is indeed possible to generate in a fully automated manner NMR structures adequate for deposition in the Protein Data Bank, we gathered 10 experimental data sets with unassigned nuclear Overhauser effect spectroscopy (NOESY) peak lists for various proteins of unknown structure, computed structures for each of them using different, fully automatic programs, and compared the results to each other and to the manually solved reference structures that were not available at the time the data were provided. This constitutes a stringent "blind" assessment similar to the CASP and CAPRI initiatives. This study demonstrates the feasibility of routine, fully automated protein structure determination by NMR.

Background Traditional antibiotics are increasingly suffering from the emergence of multidrug resistance amongst pathogenic bacteria leading to a range of novel approaches to control microbial infections being investigated as potential alternative treatments. One plausible antimicrobial alternative could be the combination of conventional antimicrobial agents/antibiotics with small molecules which block multidrug efflux systems known as efflux pump inhibitors. Bioassay-driven purification and structural determination of compounds from plant sources have yielded a number of pump inhibitors which acted against gram positive bacteria. Methodology/Principal Findings In this study we report the identification and characterization of 4',5'-O-dicaffeoylquinic acid (4',5'-ODCQA) from Artemisia absinthium as a pump inhibitor with a potential of targeting efflux systems in a wide panel of Gram-positive human pathogenic bacteria. Separation and identification of phenolic compounds (chlorogenic acid, 3',5'-ODCQA, 4',5'-ODCQA) was based on hyphenated chromatographic techniques such as liquid chromatography with post column solid-phase extraction coupled with nuclear magnetic resonance spectroscopy and mass spectroscopy. Microbial susceptibility testing and potentiation of well know pump substrates revealed at least two active compounds; chlorogenic acid with weak antimicrobial activity and 4',5'-ODCQA with pump inhibitory activity whereas 3',5'-ODCQA was ineffective. These intitial findings were further validated with checkerboard, berberine accumulation efflux assays using efflux-related phenotypes and clinical isolates as well as molecular modeling methodology. Conclusions/Significance These techniques facilitated the direct analysis of the active components from plant extracts, as well as dramatically reduced the time needed to analyze the compounds, without the need for prior isolation. The calculated energetics of the docking poses supported the biological information for t

Contains fulltext : 75259.pdf (publisher's version ) (Closed access)

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Contains fulltext : 57416.pdf (publisher's version ) (Closed access), Several studies have shown that biomolecular NMR structures are often of lower quality when compared to crystal structures, and consequently they are often excluded from structural analyses. We present a publicly available database of re-refined NMR structures, exhibiting significantly improved quality. This database (available at http://www.cmbi.kun.nl/dress/) presents a uniformly refined and validated set of structural models that improves the value of these NMR structures as input for experimental and theoretical studies in many fields of research.

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A set of computer programs called DINOSAUR has been developed, which allows the refinement of biomolecular structures directly from 2D NOE intensities. The NOE restraining potential implemented emphasises the weak intensities corresponding to larger distances which are more likely to determine the three-dimensional structure. An approximation based on a two-spin approximation is proposed for the gradient of the NOE intensities instead of the exact solution which is extremely time-consuming. The DINOSAUR routines have been implemented in various refinement programs (Distance bound Driven Dynamics, Molecular Dynamics and Energy Minimisation) and tested on an eight-residue model peptide.