The Triprenylated Anthranoid Ferruginin A, a Promising Scaffold for the Development of Novel Antibiotics against Gram-Positive Bacteria

In today’s post-antibiotic era, the search for new antimicrobial compounds is of major importance and nature represents one of the primary sources of bioactive molecules. In this work, through a cheminformatics approach, we clustered an in-house library of natural products and their derivatives based on a combination of fingerprints and substructure search. We identified the prenylated emodine-type anthranoid ferruginin A as a novel antimicrobial compound. We tested its ability to inhibit and kill a panel of Gram-positive and Gram-negative bacteria, and compared its activity with that of two analogues, vismione B and ferruanthrone. Furthermore, the capability of these three anthranoids to disrupt staphylococcal biofilm was investigated, as well as their effect on the viability of human keratinocytes. Ferruginin A showed a potent activity against both the planktonic and biofilm forms of Gram-positive bacteria (i.e., Staphylococcus aureus and S. epidermidis) and had the best therapeutic index compared to vismione B and ferruanthrone. In conclusion, ferruginin A represents a promising scaffold for the further development of valuable antimicrobial agents.

Dealing with the Ambiguity of Glycan Substructure Search

The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large-scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating these data. However, navigation is not sufficient and the model should also enable advanced search and comparison. A new ontology with a tree logical structure is introduced to represent glycan structures irrespective of the precision of molecular details. The model heavily relies on the GlycoCT encoding of glycan structures. Its implementation in the GlySTreeM knowledge base was validated with GlyConnect data and benchmarked with the Glycowork library. GlySTreeM is shown to be fast, consistent, reliable and more flexible than existing solutions for matching parts of or whole glycan structures. The model is also well suited for painless future expansion.

Dealing with the Ambiguity of Glycan Substructure Search

The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating this data. However, navigation is not sufficient and the model should also enable advanced search and comparison. A new ontology with a tree logical structure is introduced to represent glycan structures irrespective of the precision of molecular details. The model heavily relies on the GlycoCT encoding of glycan structures. Its implementation in the GlySTreeM knowledge base was validated with GlyConnect data and benchmarked with the Glycowork library. GlySTreeM is shown to be fast, consistent, reliable and more flexible than existing solutions for matching parts of or whole glycan structures. The model is also well suited to painless future expansion. Availability:https://glyconnect.expasy.org/glystreem/wiki

The need for a new generation of substructure searching software

Advances in synthetic chemistry mean that the molecules now synthesized include increasingly complex entities with mechanical bonds or extensive frameworks. For these complex molecular and supramolecular species, single-crystal X-ray crystallography has proved to be the optimal technique for determining full three-dimensional structures in the solid state. These structures are curated and placed in structural databases, the most comprehensive of which (for organic and metallo–organic structures) is the Cambridge Structural Database. A question of increasing importance is how users can search such databases effectively for these structures. Here some of the classes of complex molecules and supramolecules and the challenges associated with searching for them are highlighted. The idea of substructure searches that involve topological searches as well as searches for molecular fragments is developed, and significant enhancements are proposed to substructure search programs that are both achievable and highly beneficial for both the database user community and the broader chemistry community.

Chemical similarity methods for analyzing secondary metabolite structures

The relation that exists between the structure of a compound and its function is an integral part of chemoinformatics. The similarity principle states that “structurally similar molecules tend to have similar properties and similar molecules exert similar biological activities”. The similarity of the molecules can either be studied at the structure level or at the descriptor level (properties level). Generally, the objective of chemical similarity measures is to enhance prediction of the biological activities of molecules. In this article, an overview of various methods used to compare the similarity between metabolite structures has been provided, including two-dimensional (2D) and three-dimensional (3D) approaches. The focus has been on methods description; e.g. fingerprint-based similarity in which the molecules under study are first fragmented and their fingerprints are computed, 2D structural similarity by comparing the Tanimoto coefficients and Euclidean distances, as well as the use of physiochemical properties descriptor-based similarity methods. The similarity between molecules could also be measured by using data mining (clustering) techniques, e.g. by using virtual screening (VS)-based similarity methods. In this approach, the molecules with the desired descriptors or /and structures are screened from large databases. Lastly, SMILES-based chemical similarity search is an important method for studying the exact structure search, substructure search and also descriptor similarity. The use of a particular method depends upon the requirements of the researcher.

The dynamical state of Abell 2399: a bullet-like cluster

ABSTRACT While there are many ways to identify substructures in galaxy clusters using different wavelengths, each technique has its own caveat. In this paper, we conduct a detailed substructure search and dynamical state characterization of Abell 2399, a galaxy cluster in the local Universe (z ∼ 0.0579), by performing a multiwavelength analysis and testing the results through hydrodynamical simulations. In particular, we apply a Gaussian mixture model to the spectroscopic data from SDSS, WINGS, and OmegaWINGS Surveys to identify substructures. We further use public XMM–Newton data to investigate the intracluster medium (ICM) thermal properties, creating temperature, metallicity, entropy, and pressure maps. Finally, we run hydrodynamical simulations to constrain the merger stage of this system. The ICM is very asymmetrical and has regions of temperature and pressure enhancement that evidence a recent merging process. The optical substructure analysis retrieves the two main X-ray concentrations. The temperature, entropy, and pressure are smaller in the secondary clump than in the main clump. On the other hand, its metallicity is considerably higher. This result can be explained by the scenario found by the hydrodynamical simulations where the secondary clump passed very near to the centre of the main cluster possibly causing the galaxies of that region to release more metals through the increase of ram-pressure stripping.

ChemBioServer 2.0: an advanced web server for filtering, clustering and networking of chemical compounds facilitating both drug discovery and repurposing

Summary ChemBioServer 2.0 is the advanced sequel of a web server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. Availability and implementation http://chembioserver.vi-seem.eu.

ChemBioServer 2.0: An Advanced Web Server for Filtering, Clustering and Networking of Chemical Compounds Facilitating Both Drug Discovery and Repurposing

<p>ChemBioServer 2.0 is the advanced sequel of a web-server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of chemical compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. The updated web server is available in the URL: <a href="http://chembioserver.vi-seem.eu/">http://chembioserver.vi-seem.eu/</a> </p>

ChemBioServer 2.0: An Advanced Web Server for Filtering, Clustering and Networking of Chemical Compounds Facilitating Both Drug Discovery and Repurposing

<p>ChemBioServer 2.0 is the advanced sequel of a web-server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of chemical compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. The updated web server is available in the URL: <a href="http://chembioserver.vi-seem.eu/">http://chembioserver.vi-seem.eu/</a> </p>