DeepRank: a deep learning framework for data mining 3D protein-protein interfaces.

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).

MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra.

Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics.

Towards FAIR protocols and workflows: the OpenPREDICT use case.

It is essential for the advancement of science that researchers share, reuse and reproduce each other's workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others. The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges.

A community resource for paired genomic and metabolomic data mining.

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

Sleep classification from wrist-worn accelerometer data using random forests.

Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolution accelerometer data primarily rely on heuristic algorithms.

QMflows: A Tool Kit for Interoperable Parallel Workflows in Quantum Chemistry.

We present the QMflows Python package for quantum chemistry workflow automatization. QMflows allows users to write complex workflows in terms of simple Python scripts. It supports the development of interoperable workflows involving multiple quantum chemistry codes and executes them efficiently on large scale parallel computers.

Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra.

Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique for obtaining structural information of complex mixtures. However, just knowing the molecular masses of the mixture's constituents is almost always insufficient for confident assignment of the associated chemical structures.

3D-e-Chem-VM: Structural Cheminformatics Research Infrastructure in a Freely Available Virtual Machine.

3D-e-Chem-VM is an open source, freely available Virtual Machine ( http://3d-e-chem.github.io/3D-e-Chem-VM/ ) that integrates cheminformatics and bioinformatics tools for the analysis of protein-ligand interaction data.

Adaptation of exercise-induced stress in well-trained healthy young men.

What is the central question of this study? Exercise is known to induce stress-related physiological responses, such as changes in intestinal barrier function. Our aim was to determine the test-retest repeatability of these responses in well-trained individuals. What is the main finding and its importance? Responses to strenuous exercise, as indicated by stress-related markers such as intestinal integrity markers and myokines, showed high test-retest variation.

Transcriptional Analysis of serk1 and serk3 Coreceptor Mutants.

Somatic embryogenesis receptor kinases (SERKs) are ligand-binding coreceptors that are able to combine with different ligand-perceiving receptors such as BRASSINOSTEROID INSENSITIVE1 (BRI1) and FLAGELLIN-SENSITIVE2. Phenotypical analysis of serk single mutants is not straightforward because multiple pathways can be affected, while redundancy is observed for a single phenotype. For example, serk1serk3 double mutant roots are insensitive toward brassinosteroids but have a phenotype different from bri1 mutant roots.

Enhanced acylcarnitine annotation in high-resolution mass spectrometry data: fragmentation analysis for the classification and annotation of acylcarnitines.

Metabolite annotation and identification are primary challenges in untargeted metabolomics experiments. Rigorous workflows for reliable annotation of mass features with chemical structures or compound classes are needed to enhance the power of untargeted mass spectrometry. High-resolution mass spectrometry considerably improves the confidence in assigning elemental formulas to mass features in comparison to nominal mass spectrometry, and embedding of fragmentation methods enables more reliable metabolite annotations and facilitates metabolite classification.

In silico prediction and automatic LC-MS(n) annotation of green tea metabolites in urine.

The colonic breakdown and human biotransformation of small molecules present in food can give rise to a large variety of potentially bioactive metabolites in the human body. However, the absence of reference data for many of these components limits their identification in complex biological samples, such as plasma and urine. We present an in silico workflow for automatic chemical annotation of metabolite profiling data from liquid chromatography coupled with multistage accurate mass spectrometry (LC-MS(n)), which we used to systematically screen for the presence of tea-derived metabolites in human urine samples after green tea consumption.

Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa.

The MAGMa software for automatic annotation of mass spectrometry based fragmentation data was applied to 16 MS/MS datasets of the CASMI 2013 contest. Eight solutions were submitted in category 1 (molecular formula assignments) and twelve in category 2 (molecular structure assignment). The MS/MS peaks of each challenge were matched with in silico generated substructures of candidate molecules from PubChem, resulting in penalty scores that were used for candidate ranking.

Identification of drug metabolites in human plasma or serum integrating metabolite prediction, LC-HRMS and untargeted data processing.

Background: Comprehensive identification of human drug metabolites in first-in-man studies is crucial to avoid delays in later stages of drug development. We developed an efficient workflow for systematic identification of human metabolites in plasma or serum that combines metabolite prediction, high-resolution accurate mass LC-MS and MS vendor independent data processing. Retrospective evaluation of predictions for 14 (14)C-ADME studies published in the period 2007-January 2012 indicates that on average 90% of the major metabolites in human plasma can be identified by searching for accurate masses of predicted metabolites.

Automatic chemical structure annotation of an LC-MS(n) based metabolic profile from green tea.

Liquid chromatography coupled with multistage accurate mass spectrometry (LC-MS(n)) can generate comprehensive spectral information of metabolites in crude extracts. To support structural characterization of the many metabolites present in such complex samples, we present a novel method ( http://www.emetabolomics.

Substructure-based annotation of high-resolution multistage MS(n) spectral trees.

Rationale: High-resolution multistage MS(n) data contains detailed information that can be used for structural elucidation of compounds observed in metabolomics studies. However, full exploitation of this complex data requires significant analysis efforts by human experts. In silico methods currently used to support data annotation by assigning substructures of candidate molecules are limited to a single level of MS fragmentation.

Structural elucidation and quantification of phenolic conjugates present in human urine after tea intake.

In dietary polyphenol exposure studies, annotation and identification of urinary metabolites present at low (micromolar) concentrations are major obstacles. To determine the biological activity of specific components, it is necessary to have the correct structures and the quantification of the polyphenol-derived conjugates present in the human body. We present a procedure for identification and quantification of metabolites and conjugates excreted in human urine after single bolus intake of black or green tea.

Determinants of reactivity and selectivity in soluble epoxide hydrolase from quantum mechanics/molecular mechanics modeling.

Soluble epoxide hydrolase (sEH) is an enzyme involved in drug metabolism that catalyzes the hydrolysis of epoxides to form their corresponding diols. sEH has a broad substrate range and shows high regio- and enantioselectivity for nucleophilic ring opening by Asp333. Epoxide hydrolases therefore have potential synthetic applications.

SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites.

Predictions of potential metabolites based on chemical structure are becoming increasingly important in drug discovery to guide medicinal chemistry efforts that address metabolic issues and to support experimental metabolite screening and identification. Herein we present a novel rule-based method, SyGMa (Systematic Generation of potential Metabolites), to predict the potential metabolites of a given parent structure. A set of reaction rules covering a broad range of phase 1 and phase 2 metabolism has been derived from metabolic reactions reported in the Metabolite Database to occur in humans.

Molecular determinants of xenobiotic metabolism: QM/MM simulation of the conversion of 1-chloro-2,4-dinitrobenzene catalyzed by M1-1 glutathione S-transferase.

Modeling methods allow the identification and analysis of determinants of reactivity and specificity in enzymes. The reaction between glutathione and 1-chloro-2,4-dinitrobenzene (CDNB) is widely used as a standard activity assay for glutathione S-transferases (GSTs). It is important to understand the causes of differences between catalytic GST isoenzymes and the effects of mutations and genetic polymorphisms.

Molecular mechanisms of antibiotic resistance: QM/MM modelling of deacylation in a class A beta-lactamase.

Modelling of the first step of the deacylation reaction of benzylpenicillin in the E. coli TEM1 beta-lactamase (with B3LYP/6-31G + (d)//AM1-CHARMM22 quantum mechanics/molecular mechanics methods) shows that a mechanism in which Glu166 acts as the base to deprotonate a conserved water molecule is both energetically and structurally consistent with experimental data; the results may assist the design of new antibiotics and beta-lactamase inhibitors.

Mechanisms of antibiotic resistance: QM/MM modeling of the acylation reaction of a class A beta-lactamase with benzylpenicillin.

Understanding the mechanisms by which beta-lactamases destroy beta-lactam antibiotics is potentially vital in developing effective therapies to overcome bacterial antibiotic resistance. Class A beta-lactamases are the most important and common type of these enzymes. A key process in the reaction mechanism of class A beta-lactamases is the acylation of the active site serine by the antibiotic.

Mechanism and structure-reactivity relationships for aromatic hydroxylation by cytochrome P450.

Cytochrome P450 enzymes play a central role in drug metabolism, and models of their mechanism could contribute significantly to pharmaceutical research and development of new drugs. The mechanism of cytochrome P450 mediated hydroxylation of aromatics and the effects of substituents on reactivity have been investigated using B3LYP density functional theory computations in a realistic porphyrin model system. Two different orientations of substrate approach for addition of Compound I to benzene, and also possible subsequent rearrangement pathways have been explored.