dataesr/scientific-paragraphs-categorization huggingface dataset:
These results indicate that protein-protein interactions may dominate the binding of N-Alsp to FN. The LGI network of the E. coli FimH-LD adhesin with viral envelope proteins and the anti-HIV activity of FimH-LD. Several viral pathogens-such as HIV, influenza virus, SARS virus, hepatitis C virus, MARV, and Ebola virus-contain highmannose glycans attached to the envelope proteins  (40). To predict interactions of FimH-LD with viral envelope glycoproteins, we generated a viral LGI network by employing FimH-LD glycan array data and the viral glycoproteomic data available in the Gly-cosuiteDB database. The network suggested the recognition of different viruses, including three human-pathogenic viruses (Fig. 7 ). One of these predictions, i. e. , the link with HIV, was experimentally and thoroughly validated by using biomolecular and cellular assays.
Conclusions. We developed and successfully employed a novel LGI network in which the carbohydrate-binding properties of the E. coli adhesin FimH and Candida adhesins from the Epa and Als adhesin families were explored. This LGI network strategy, based on glycan array screening results and a glycoprotein database inquiry, allowed the profiling of potential glycoprotein binding targets for the selected adhesins. We confirmed some of these potential targets either experimentally or by reference to the literature. Additionally, this strategy was adapted for the prediction of adhesin-viral glycoprotein interactions, validated by the discovery of anti-HIV activity of FimH. This study shows the potential of the new strategy for the study of some microbial and viral interactions. The LGI networks presented were based on the glycan array results coming from the database of the Consortium for Functional Glycomics (CFG).
Experimental data could also be extracted from other databases (such as the Glycosciences Laboratory Database, Imperial College London). Care should be taken to check the quality of the extracted data. Although the Glycosuite database of UniCarbKB used is a curated glycoproteomic database, it has some limitations, since it is not exhaustive. We expect future improvements in the content of this database and, consequently, an enhancement of the LGI network's prediction quality and accuracy. The LGI network strategy could be connected to additional bioinformatic resources that could support the validation of the LGI network generated, for example, the SugarBind database (http://sugarbind. expasy. org), which provides information on known carbohydrate sequences to which pathogenic organisms or substances (bacteria, toxins, and viruses) specifically adhere to.
For FimH-LD-gp120 kinetic analysis, fitting of experimental curves and calculation of kinetic parameters were performed by using the BIAEvaluation software version 4. 1 (GE Healthcare) and a 1:1 (Langmuir) binding model. In all of the other cases, dissociation constants in the equilibrium state (K D ) were determined. The results were then analyzed with the BIAevaluation software and with Prism 6 software (Graph-Pad) (see the supplemental material). AFM-SMFS. AFM-SMFS experiments to determine the unbinding force between HIV-1(YU2) gp120 and FimH-LD were performed as described in the supplemental material. LGI network construction. The results of the glycan array screenings for FimH-LD (L. Wyns, 2011, CFG database, glycan array version 5. 0) N-Als1p (first screening, glycan array version 3.
2  [22] ; second screening, CFG glycan array version 5. 0 [this work]), N-Als3p, N-Epa1p (28), N-Epa6p, and N-Epa7p (85) were retrieved from the CFG-Core H database (http://www. functionalglycomics. org/) and used to generate the Als/ Epa-glycan interaction networks. The results were filtered by removing the data of three times the standard error of the mean (SEM) and using a signal-to-noise ratio cutoff value that was larger than the average number of relative fluorescence units (RFU). The signal-to-noise ratio cutoff value was visually selected on the glycan array screening graphs. The RFU values were normalized by dividing the values by the maximal RFU value of the screening. The mean RFU value was used when more than one value was detected in different screenings for the same glycan interaction.
Glycanbinding protein binding sites may accommodate glycan determinants made up of two to six linear monosaccharides together with their potential side chains containing other sugars and modifications (4). Therefore, glycan determinant structures containing oligosaccharides composed of 2, 3, 4, 5, and 6 carbohydrate residues present at the nonreducing end were submitted to the GlycoSuiteDB database in the UniCarbKB platform  (46, 47). In order to obtain the glycoproteomic data, we developed a set of three Perl scripts that collaborated to extract the data from the GlycosuiteDB website (see the supplemental material). LGI network modeling and visualization. The interactions were modeled as a weighted undirected graph G(V, E, W), where V is the set of vertices, E is the set of edges connecting the pairs of vertices, and W is the set of weights associated with each edge.
The edges' weights were calcu-lated according to the interaction type (see the supplemental material). The Gephi open-source graph visualization and manipulation software was used  (86). Within Gephi, the force-directed visualization method of Hu  (49) , which is known to be accurate when visualizing local clustering and symmetry  (87) , was used. This approach allowed us to obtain relatively well-defined spatial distributions and local clusters according to the network structure (Fig. 2  and 5 ). The LGI networks were also visualized as a hierarchical structure by using Cytoscape 2. 8  (88)  and the Cerebral app  (89)  (see the supplemental material). MTS-PES antiviral assays. The anti-HIV-1 activity of each compound, both alone and in combination, in MT-4 cell cultures was determined by a tetrazolium-based colorimetric assay.
Next, the plates were incubated in the dark for 10 min in a closed plate shaker. Finally, cell lysis was scored microscopically and aliquots were transferred to white Lumitrac 96-well plates (Greiner Bio-One) to measure the relative luminescence units with a SpectraMax L microplate reader and SoftMax Pro Software (Molecular Devices), an integration time of 0. 6 s, and a dark adaptation time of 5 min.
ACKNOWLEDGMENTS The  Belgian Federal Science Policy Office ,  European Space Agency PRO-DEX program  projects  ILSRA-2009-1156  and  ILSRA-2004-222/AO-2004-69 ,  KU Leuven  projects  GOA 10/014  and  PF/10/018 , and the  Agency for Innovation by Science and Technology (IWT, Belgium) (Ph. D. grant funding for D. D. and F. S. I. ) supported this work. We acknowledge  Serafeim Kourlos  ( Vrije Universiteit Brussel, Belgium ) for his help with data analyses,  Henri De Greve  ( Vrije Universiteit Brussel, Belgium ) for his help with FimH-LD production,  Sandor Kasas  ( École Polytechnique Fédérale de Lausanne [EPFL], Switzerland ) for advice on AFM force spectroscopy, and the CFG (http://www. functionalglycomics. org) for glycan analysis.
Since in drug discovery screening of many thousands if not millions of compounds is the rule, the all-determining factor for the success of the approach is the fast automation of fragmentation and ensuing parameterization (Automated Fragmentation Parameterization, AFP). We have developed an AFP protocol that combines elements from chemical informatics (for finding optimal fragments) and engineering thermodynamics (for parameterization through surface charge interactions). The first results look promising. We have calculated fragments for ~100k molecules (PUBCHEM data base) by rule based simulated annealing. Classification of fragments shows a distribution with a typical Zipf regression. We estimate that five to ten thousand unique fragments are enough to cover all organic molecules in the CAS database.
As for the parameterization: we rely heavily on non-biological engineering thermodynamics data, such as Vapor-Liquid Equlibrium diagrams from the NIST database (Thermodynamics Data Engine). This unique set of data that covers excess Gibbs energies for a wide range of binary fluid systems, and is an ultimate test for any simulation that aims to produce results of thermodynamic accuracy. So far, this data has been the bread and butter of ultrafast and accurate equation based engineering thermodynamics models, such as UNIFAC or COSMO-RS. Simulation methods up till now have been to slow and inaccurate for massive validation. Here our mesoscale simulation approach reaches an accuracy of a fraction of a kT per fragment (1-3 kT per molecule), this is on par, or better than the engineering methods, with the enormous advantage that our approach is intrinsically 3-dimensional.
All is still within very reasonable calculation times on modest computational resources. We complement the presentation with results on logP prediction and lignine, keratine and kinase modelling. The software development is sponsored by CULGI, a large consortium from oil, personal care and chemical industries. Solvation plays an essential role for all kinds of physiologically relevant systems and phenomena. Not only water as the key solvent for life but also electrolytes and osmo-lytes modulate molecular interactions with implications for structure, dynamics, and thermodynamics. Computational modeling of (bio-)molecular systems in solution re-quires taking into account the much larger number of solvent particles in comparison with solute species. This results in vast technical effort, e. g.
Finally, a program is introduced, which allows for ultra-fast similarity comparisons, as protein binding sites are represented by sets of distance histograms that are both generated and compared with linear complexity  [4]. This method attains a speed of more than 20,000 comparisons per second, which makes screenings across large datasets and even entire databases easily feasible. The practical use of the new methods is proven by a successful prospective virtual screening study that aimed at the identification of novel inhibitors of the NMDA receptor. Fragment-based methodologies have become an alternative to conventional High Throughput Screening during the last decade  [1]. One of the advantages is the higher hit rate due to the much smaller fragment space. Identified hits not only serve as starting points for drug design but also represent sweet spots in the chemical space.
In order to simplify the usage of protein ensembles, we developed a fully automated preprocessing workflow for the enrichment and the analysis of protein flexibility information  [1]. In a first step, alternative conformations of a given binding site are extracted from an arbitrary selection of protein structures, e. g. the current version of the Protein Data Base (PDB). Using an indexed database that has been specially geared to this purpose makes this step highly efficient. Afterwards, the resulting structures are being aligned with our new active site alignment algorithm ASCONA  [2]  which has been developed with a focus on an accurate detection of protein backbone variations. Based on the active site alignment, a further step detects flexible regions within the binding site and uses a remaining rigid core for a superimposition of the generated ensemble.
We have previously developed a 3D virtual screening library of >3,000 compounds derived from Central African flora  [1, 2]  and evaluated the pharmacokinetics profile  [3]. This work has been extended to include West Africa  [4] , North Africa and Southern Africa, as well as a small library of compounds with remarkable potencies from African flora  [5] , including anti-mycobacterial infections, malaria, onchocerciasis and cancer  [6]. In this work, we present an SQL platform for searching natural products for drug discovery from African medicinal plants. Data has been collected from major natural products journals and PhD theses from university libraries. The unified library contains > 5,000 unique structures from all regions of the African continent. The known uses of the plant species in African Traditional Medicine (ATM) have been previously related with the measured biological activities of the isolated plant metabolites  [7].
Our goal is to develop a small potent and less toxic NP library for virtual screening within the African setting. Computer-aided drug design methods, has become a promising part of the drug discovery process nowadays. This study was focused on generating a 3D structural library of potential anticancer compounds from the African flora and to evaluate the "drug-likeness" and toxicity of the new compound library. Virtually screening and in silico toxicity assessment were carried in comparison with the dataset of Naturally Occurring Plant-based Anticancer Compound-Activity-Target (NPACT), comprising ~1,500 published naturally occurring plant-based compounds from around the world  [3]. From this study, about 400 compounds have been identified from African flora and their drug-likeness properties evaluated in comparison with NPACT and DNP.
A successful docking attempt was carried out for some fourteen selected known anticancer drug targets  [4]. Pharmacophore-based virtual screening and in silico toxicity assessment have also been done. Pharmacophore models were validated through receiver operating characteristic (ROC) and Güner-Henry (GH) scoring methods  [5] , indicating that several of the models generated could be useful for the identification of potential anticancer agents from natural product databases. The validated pharmacophore models were used as 3D search queries for virtual screening of the AfroCancer, along with the NPACT. Additionally, the in silico assessment of toxicity of the two datasets was carried out by use of eighty eight (88) toxicity end points predicted by Lhasa's expert knowledge-based system (Derek)  [6] , showing that only an insignificant proportion of the promising anticancer agents would be likely to show high toxicity profiles.
Knowledge base development for the prediction of acute aquatic toxicity of chemicals Fidele Ntie-Kang There is widespread concern about the toxicological effects of man-made chemicals in the environment, including those of pharmaceutical products and their metabolites. There are databases of, and computer models to predict, aquatic narcosis and, in some cases, excess aquatic toxicity such as ECOSAR  [1]  but there remains a need for better sharing of human knowledge about the full range of environmental and human toxicological hazards. Computer programs such as Derek  [2]  are used to share human knowledge about mammalian toxicity and a proof of concept application, Eco-Derek, using the same technology to share knowledge about toxicity to the ciliate Tetrahymena pyriformis and human skin sensitisation has been described  [3].
It is intended that the system will report both potency and confidence in predictions (e. g. "Predicted toxicity to fish: Moderate. Confidence in the prediction: High"). Nexus currently only supports reporting of confidence (likelihood). So, for the present, knowledge about toxicity to T. pyriformis contained in eco-Derek has been reworked and assessed in terms of confidence about its predictive reliability and incorporated into the new system. In addition to the knowledge base, a database covering the environmental toxicity of chemicals is being built, emphasising, but not restricted to, aquatic toxicity, with effort concentrated on data from African sources not previously easily-accessible electronically. The data are being studied to develop new rules for the knowledge base of the prediction system.
Prediction of the functional properties of electroceramic materials using chemoinformatics approaches Natalia Kireeva 1,2 , Alexandre Petrov 1,2,3 , Denis Ostroumov The representation of the relevant conformational space of small molecules (e. g. drug-like molecules) is a thoroughly studied, nontrivial problem. A wide variety of different methodologies have been explored, with the aim to devise an algorithm that achieves optimum performance with respect to accuracy, conformational ensemble size, and computing time. Here, we present a comprehensive benchmarking study, in which we assess and compare the performance of a variety of free and commercial tools for computing conformer ensembles. Inspired by the Iridium-HT benchmarking dataset  [1] , we have devised a new, high-quality library of protein-bound ligand conformations from the PDB, with the aim to improve statistical significance by use of a substantially larger dataset, while adhering to strict quality criteria.
Our benchmarking tests show that several freely available conformer ensemble generators (e. g. RDKit  [2] , Balloon  [3] , CONFECT  [4] ) achieve a level of accuracy which is comparable to that of commercial software (e. g. MOE  [5] ). However, substantial differences with respect to runtimes and the percentage of molecules for which the algorithms fail to produce any solutions can be observed. Also, longer computing times do not per se lead to better conformer ensembles. From this statistical analysis we devised guidelines on how to parameterize tools for best performance in different application scenarios (e. g. computation of small data sets with maximum accuracy vs. large-scale virtual screening applications). Matched Molecular Pairs is a well-established concept in Medicinal Chemistry which was successfully employed for optimization of pharmacokinetic or physicochemical properties  [1].
Several recent publications from different groups suggest the extension of the MMP towards structure-based lead optimization in context of protein environment  [2, 3]. We introduce a strategy to relate the substitution effect within MMPs to the atom environment within the co-crystallized proteinligand complex implemented within the VAMMPIRE database and the supplementary web interface (http://vammpire. pharmchem. uni-frankfurt. de) [4]. In this presentation we discuss different applications of the VAMMPIRE database. VAMMPIRE-LORD (lead optimization by rational design) describes an innovative strategy to improve the binding affinity of a defined lead compound using 3D-MMPs  [5]. We demonstrate that the created model is able to extrapolate the knowledge of a chemical transformation and the associated effect on ligand affinity to any similar system.
In a second application we discuss the use of a subset of the VAMMPIRE database for validation of scoring functions. Co-crystallized 3D-MMPs with measured affinity data are suitable for evaluation of scoring functions independent from the underlying docking algorithm. We present our findings considering the performance of scoring functions on the validation dataset derived from the VAMMPIRE database. One of the most widely used molecular similarity metrics to cluster large compounds sets is 2D fingerprint-based Tanimoto distance, due the overall good balance between speed and effectiveness  [1]. However, there are significant limitations in the ability of a 2D fingerprintbased method to capture the biological similarity between molecules, especially when conformationally flexible structures are involved.
Due to the practice of sampling huge amounts of plastics, no specific sample preparation is performed. The basis of the new method is the correlation between physico-chemical properties, molecular structure and patterns of vibration bands  [2]. In data processing all spectra are baseline corrected and fitted with asymmetric Voigt functions to obtain all the vibration band areas. All areas of one spectrum are divided by each other, which leads to a highly characteristic dataset. Measurement statistics allows filtering these data to extract just analyte information and to separate matrix information. For characterization, these data are compared to references which are treated as well by multidimensional scaling combined with a novel distance function and k-Means clustering. By this method all measured samples could be classified accurately by their similarities and differences.
Even small effects as those between HD-and LD-PE are identified correctly, which can be confirmed by reference literature  [3]. The new method recognizes fundamental differences of synthetic and natural polymers and classifies even unknown synthetic and bio polyamides correctly. Cheminformatics is becoming an essential asset in the chemist's toolbox, especially when it comes to guiding experiments. However, the compendium of chemical biological datasets generated by experimentalists and available in publicly-accessible repositories is skyrocketing  [1]. As a consequence, there is a general need for new cheminformatics technologies capable of handling, analyzing, and modeling Big Chemical Data  [1]. In this presentation, we introduce three next-generation cheminformatics approaches that can: (i) Enumerate extremely large virtual libraries: we will introduce the PKS Enumerator technology that generate millions of macrolides with user-defined building blocks and constraints.
This research is relevant for the development of novel antibiotics. (ii) Screen very large libraries of virtual compounds using a GPUaccelerated platform: we will show how a GPU-powered ligand-based screening workflow can perform ultra-fast similarity searches among one billion molecules. (iii) Use machine-learning techniques for analyzing molecular dynamics trajectories: we will discuss the rationale of examining both full-atom and coarse-grained molecular dynamics trajectories using machinelearning techniques, what additional knowledge can be extracted, and how next-generation QSAR models could benefit of such technology. Reaction driven de novo design uses a library of reactions and a database of reactants to perform a stochastic walk through "synthetic chemistry space. " Guided by appropriate drug design "scoring" such as docking or ligand shape similarity, such an approach provides a means to generate novel ideas for drug candidates that includes a proposed synthesis pathway.
P14 High-throughput docking using constraints: Development and application of a GOLD python wrapper for automated virtual screening workflows Hitesh Patel, Oliver Koch Department of Chemistry and Chemical Biology, TU Dortmund, Germany Correspondence: Hitesh Patel -hitesh. patel@tu-dortmund. de Journal of Cheminformatics 2016, 8(Suppl 1):P14 Virtual screening based on high-throughput docking of millions of compounds is still not feasible due to required computational power and time. In a constrained based docking important protein-ligand interactions can be used to include additional knowledge and leading to a possible alternative for pharmacophore based pre-filtering of huge libraries. This was successfully shown by Koch et al. who identified the first inhibitors of an important protein-protein interaction in M.
tuberculosis  [1]  based on a high-throughput constraint docking campaign using GOLD  [2]. The underlying workflow contains several steps like constrained docking with very fast settings, pose filtering and redocking using exhaustive settings. Up to now this processing steps had to be performed manually, since GOLD lacks a programming language API. Therefore, we developed a python wrapper for processing the gold configuration file and creating python-based automated virtual screening workflows. Using this wrapper an automated constrained docking workflow was implemented and used to analyze the performance of this approach in comparison to pharmacophore mapping-based virtual screening. First studies showed promising results on Poly [ADP-ribose] polymerase 1 (PARP1), Janus Kinase 3 (JAK3) and Phosphoinositid-3-Kinase gamma (PI3Kg) from the DEKOIS2.