scholarly journals Computational Analysis of Chemical Space of Natural Compounds Interacting with Sulfotransferases

Molecules ◽  
2021 ◽  
Vol 26 (21) ◽  
pp. 6360
Author(s):  
Iglika Lessigiarska ◽  
Yunhui Peng ◽  
Ivanka Tsakovska ◽  
Petko Alov ◽  
Nathalie Lagarde ◽  
...  
Keyword(s):  
Natural Products ◽  
Similarity Search ◽  
Computational Analysis ◽  
Chemical Space ◽  
Natural Compounds ◽  
Molecular Structures ◽  
Strong Interactions ◽  
Chemical Structures ◽  
Structures And Properties ◽  
Natural Ligands

The aim of this study was to investigate the chemical space and interactions of natural compounds with sulfotransferases (SULTs) using ligand- and structure-based in silico methods. An in-house library of natural ligands (hormones, neurotransmitters, plant-derived compounds and their metabolites) reported to interact with SULTs was created. Their chemical structures and properties were compared to those of compounds of non-natural (synthetic) origin, known to interact with SULTs. The natural ligands interacting with SULTs were further compared to other natural products for which interactions with SULTs were not known. Various descriptors of the molecular structures were calculated and analyzed. Statistical methods (ANOVA, PCA, and clustering) were used to explore the chemical space of the studied compounds. Similarity search between the compounds in the different groups was performed with the ROCS software. The interactions with SULTs were additionally analyzed by docking into different experimental and modeled conformations of SULT1A1. Natural products with potentially strong interactions with SULTs were outlined. Our results contribute to a better understanding of chemical space and interactions of natural compounds with SULT enzymes and help to outline new potential ligands of these enzymes.

scholarly journals Analysis of a large food chemical database: chemical space, diversity, and complexity

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 993 ◽  
Author(s):  
J. Jesús Naveja ◽  
Mariel P. Rico-Hidalgo ◽  
José L. Medina-Franco
Keyword(s):  
Natural Products ◽  
Chemical Space ◽  
Structural Complexity ◽  
Chemical Diversity ◽  
Chemical Structures ◽  
Novel Approach ◽  
Chemical Database ◽  
Protein Interactome ◽  
Limited Basis ◽  
Future Direction

Background: Food chemicals are a cornerstone in the food industry. However, its chemical diversity has been explored on a limited basis, for instance, previous analysis of food-related databases were done up to 2,200 molecules. The goal of this work was to quantify the chemical diversity of chemical compounds stored in FooDB, a database with nearly 24,000 food chemicals. Methods: The visual representation of the chemical space of FooDB was done with ChemMaps, a novel approach based on the concept of chemical satellites. The large food chemical database was profiled based on physicochemical properties, molecular complexity and scaffold content. The global diversity of FooDB was characterized using Consensus Diversity Plots. Results: It was found that compounds in FooDB are very diverse in terms of properties and structure, with a large structural complexity. It was also found that one third of the food chemicals are acyclic molecules and ring-containing molecules are mostly monocyclic, with several scaffolds common to natural products in other databases. Conclusions: To the best of our knowledge, this is the first analysis of the chemical diversity and complexity of FooDB. This study represents a step further to the emerging field of “Food Informatics”. Future study should compare directly the chemical structures of the molecules in FooDB with other compound databases, for instance, drug-like databases and natural products collections. An additional future direction of this work is to use the list of 3,228 polyphenolic compounds identified in this work to enhance the on-going polyphenol-protein interactome studies.

scholarly journals Origins and Bioactivities of Natural Compounds Derived from Marine Ascidians and Their Symbionts

Marine Drugs ◽  
2019 ◽  
Vol 17 (12) ◽  
pp. 670 ◽  
Author(s):  
Xiaoju Dou ◽  
Bo Dong
Keyword(s):  
Natural Products ◽  
Secondary Metabolites ◽  
Bioactive Compounds ◽  
Natural Compounds ◽  
Marine Animals ◽  
Lead Compounds ◽  
Chemical Structures ◽  
Complex Interactions ◽  
Novel Structures ◽  
Important Drug

Marine ascidians are becoming important drug sources that provide abundant secondary metabolites with novel structures and high bioactivities. As one of the most chemically prolific marine animals, more than 1200 inspirational natural products, such as alkaloids, peptides, and polyketides, with intricate and novel chemical structures have been identified from ascidians. Some of them have been successfully developed as lead compounds or highly efficient drugs. Although numerous compounds that exist in ascidians have been structurally and functionally identified, their origins are not clear. Interestingly, growing evidence has shown that these natural products not only come from ascidians, but they also originate from symbiotic microbes. This review classifies the identified natural products from ascidians and the associated symbionts. Then, we discuss the diversity of ascidian symbiotic microbe communities, which synthesize diverse natural products that are beneficial for the hosts. Identification of the complex interactions between the symbiont and the host is a useful approach to discovering ways that direct the biosynthesis of novel bioactive compounds with pharmaceutical potentials.

scholarly journals Chemistry and Biochemistry of Sulfur Natural Compounds: Key Intermediates of Metabolism and Redox Biology

2020 ◽  
Vol 2020 ◽  
pp. 1-27 ◽  
Author(s):  
Antonio Francioso ◽  
Alessia Baseggio Conrado ◽  
Luciana Mosca ◽  
Mario Fontana
Keyword(s):  
Natural Products ◽  
Sulfur Metabolism ◽  
Chemical Properties ◽  
Natural Compounds ◽  
Chemical Diversity ◽  
Redox Biology ◽  
Inorganic Sulfur ◽  
Chemical Structures ◽  
Sulfurous Compounds ◽  
Sulfur Containing

Sulfur contributes significantly to nature chemical diversity and thanks to its particular features allows fundamental biological reactions that no other element allows. Sulfur natural compounds are utilized by all living beings and depending on the function are distributed in the different kingdoms. It is no coincidence that marine organisms are one of the most important sources of sulfur natural products since most of the inorganic sulfur is metabolized in ocean environments where this element is abundant. Terrestrial organisms such as plants and microorganisms are also able to incorporate sulfur in organic molecules to produce primary metabolites (e.g., methionine, cysteine) and more complex unique chemical structures with diverse biological roles. Animals are not able to fix inorganic sulfur into biomolecules and are completely dependent on preformed organic sulfurous compounds to satisfy their sulfur needs. However, some higher species such as humans are able to build new sulfur-containing chemical entities starting especially from plants’ organosulfur precursors. Sulfur metabolism in humans is very complicated and plays a central role in redox biochemistry. The chemical properties, the large number of oxidation states, and the versatile reactivity of the oxygen family chalcogens make sulfur ideal for redox biological reactions and electron transfer processes. This review will explore sulfur metabolism related to redox biochemistry and will describe the various classes of sulfur-containing compounds spread all over the natural kingdoms. We will describe the chemistry and the biochemistry of well-known metabolites and also of the unknown and poorly studied sulfur natural products which are still in search for a biological role.

scholarly journals Genomic charting of ribosomally synthesized natural product chemical space facilitates targeted mining

2016 ◽  
Vol 113 (42) ◽  
pp. E6343-E6351 ◽  
Author(s):  
Michael A. Skinnider ◽  
Chad W. Johnston ◽  
Robyn E. Edgar ◽  
Chris A. Dejong ◽  
Nishanth J. Merwin ◽  
...  
Keyword(s):  
Natural Products ◽  
Genome Sequencing ◽  
Sequence Data ◽  
Chemical Space ◽  
Biological Activities ◽  
Global Analysis ◽  
Gene Clusters ◽  
Systematic Investigation ◽  
Chemical Structures ◽  
Modified Peptides

Microbial natural products are an evolved resource of bioactive small molecules, which form the foundation of many modern therapeutic regimes. Ribosomally synthesized and posttranslationally modified peptides (RiPPs) represent a class of natural products which have attracted extensive interest for their diverse chemical structures and potent biological activities. Genome sequencing has revealed that the vast majority of genetically encoded natural products remain unknown. Many bioinformatic resources have therefore been developed to predict the chemical structures of natural products, particularly nonribosomal peptides and polyketides, from sequence data. However, the diversity and complexity of RiPPs have challenged systematic investigation of RiPP diversity, and consequently the vast majority of genetically encoded RiPPs remain chemical “dark matter.” Here, we introduce an algorithm to catalog RiPP biosynthetic gene clusters and chart genetically encoded RiPP chemical space. A global analysis of 65,421 prokaryotic genomes revealed 30,261 RiPP clusters, encoding 2,231 unique products. We further leverage the structure predictions generated by our algorithm to facilitate the genome-guided discovery of a molecule from a rare family of RiPPs. Our results provide the systematic investigation of RiPP genetic and chemical space, revealing the widespread distribution of RiPP biosynthesis throughout the prokaryotic tree of life, and provide a platform for the targeted discovery of RiPPs based on genome sequencing.

scholarly journals Learning Continuous and Data-Driven Molecular Descriptors by Translating Equivalent Chemical Representations

2018 ◽  
Author(s):  
Robin Winter ◽  
Floriane Montanari ◽  
Frank Noé ◽  
Djork-Arné Clevert
Keyword(s):  
Chemical Space ◽  
Idea Generation ◽  
Molecular Structures ◽  
Feature Representation ◽  
Molecular Fingerprints ◽  
Neural Machine Translation ◽  
Feature Representations ◽  
Chemical Structures ◽  
Low Dimensional ◽  
Made In

<p></p><p>There has been a recent surge of interest in using machine learning across chemical space in order to predict properties of molecules or design molecules and materials with desired properties. Most of this work relies on defining clever feature representations, in which the chemical graph structure is encoded in a uniform way such that predictions across chemical space can be made. In this work, we propose to exploit the powerful ability of deep neural networks to learn a feature representation from low-level encodings of a huge corpus of chemical structures. Our model borrows ideas from neural machine translation: it translates between two semantically equivalent but syntactically different representations of molecular structures, compressing the meaningful information both representations have in common in a low-dimensional representation vector. Once the model is trained, this representation can be extracted for any new molecule and utilized as descriptor. In fair benchmarks with respect to various human-engineered molecular fingerprints and graph-convolution models, our method shows competitive performance in modelling quantitative structure-activity relationships in all analyzed datasets. Additionally, we show that our descriptor significantly outperforms all baseline molecular fingerprints in two ligand-based virtual screening tasks. Overall, our descriptors show the most consistent performances over all experiments. The continuity of the descriptor space and the existence of the decoder that permits to deduce a chemical structure from an embedding vector allows for exploration of the space and opens up new opportunities for compound optimization and idea generation.</p><br><p></p>

scholarly journals Open Natural Products Research: Curation and Dissemination of Biological Occurrences of Chemical Structures through Wikidata

2021 ◽  
Author(s):  
Adriano Rutz ◽  
Maria Sorokina ◽  
Jakub Galgonek ◽  
Daniel Mietchen ◽  
Egon Willighagen ◽  
...  
Keyword(s):  
Natural Products ◽  
Data Collection ◽  
Data Quality ◽  
Knowledge Base ◽  
Molecular Structures ◽  
Chemical Data ◽  
Chemical Structures ◽  
Heterogeneous Chemical ◽  
Unlimited Access ◽  
Living Organisms

As contemporary bioinformatic and chemoinformatic capabilities are reshaping natural products research, major benefits could result from an open database of referenced structure-organism pairs. Those pairs allow the identification of distinct molecular structures found as components of heterogeneous chemical matrices originating from living organisms. Current databases with such information suffer from paywall restrictions, limited taxonomic scope, poorly standardized fields, and lack of interoperability. To ensure data quality, references to the work that describes the structure-organism relationship are mandatory. To fill this void, we collected and curated a set of structure-organism pairs from publicly available natural products databases to yield LOTUS (naturaL prOducTs occUrrences databaSe), which contains over 500,000 curated and referenced structure-organism pairs. All the programs developed for data collection, curation, and dissemination are publicly available. To provide unlimited access as well as standardized linking to other resources, LOTUS data is both hosted on Wikidata and regularly mirrored on https://lotus.naturalproducts.net. The diffusion of these referenced structure-organism pairs within the Wikidata framework addresses many of the limitations of currently-available databases and facilitates linkage to existing biological and chemical data resources. This resource represents an important advancement in the design and deployment of a comprehensive and collaborative natural products knowledge base.

scholarly journals Cheminformatics to Characterize Pharmacologically Active Natural Products

Biomolecules ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1566 ◽  
Author(s):  
José L. Medina-Franco ◽  
Fernanda I. Saldívar-González
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Natural Product ◽  
Chemical Space ◽  
Data Sets ◽  
Fragment Analysis ◽  
Chemical Structures ◽  
Speed Up ◽  
Pharmacologically Active ◽  
Design Experiments

Natural products have a significant role in drug discovery. Natural products have distinctive chemical structures that have contributed to identifying and developing drugs for different therapeutic areas. Moreover, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. Natural products such as peptides and macrocycles, and other compounds with unique features represent attractive sources to address complex diseases. Computational approaches that use chemoinformatics and molecular modeling methods contribute to speed up natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. This review discusses a broad range of chemoinformatics applications to support natural product-based drug discovery. We emphasize profiling natural product data sets in terms of diversity; complexity; acid/base; absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties; and fragment analysis. Novel techniques for the visual representation of the chemical space are also discussed.

scholarly journals Learning Continuous and Data-Driven Molecular Descriptors by Translating Equivalent Chemical Representations

2018 ◽  
Author(s):  
Robin Winter ◽  
Floriane Montanari ◽  
Frank Noé ◽  
Djork-Arné Clevert
Keyword(s):  
Chemical Space ◽  
Idea Generation ◽  
Molecular Structures ◽  
Feature Representation ◽  
Molecular Fingerprints ◽  
Neural Machine Translation ◽  
Feature Representations ◽  
Chemical Structures ◽  
Low Dimensional ◽  
Made In

<p></p><p>There has been a recent surge of interest in using machine learning across chemical space in order to predict properties of molecules or design molecules and materials with desired properties. Most of this work relies on defining clever feature representations, in which the chemical graph structure is encoded in a uniform way such that predictions across chemical space can be made. In this work, we propose to exploit the powerful ability of deep neural networks to learn a feature representation from low-level encodings of a huge corpus of chemical structures. Our model borrows ideas from neural machine translation: it translates between two semantically equivalent but syntactically different representations of molecular structures, compressing the meaningful information both representations have in common in a low-dimensional representation vector. Once the model is trained, this representation can be extracted for any new molecule and utilized as descriptor. In fair benchmarks with respect to various human-engineered molecular fingerprints and graph-convolution models, our method shows competitive performance in modelling quantitative structure-activity relationships in all analyzed datasets. Additionally, we show that our descriptor significantly outperforms all baseline molecular fingerprints in two ligand-based virtual screening tasks. Overall, our descriptors show the most consistent performances over all experiments. The continuity of the descriptor space and the existence of the decoder that permits to deduce a chemical structure from an embedding vector allows for exploration of the space and opens up new opportunities for compound optimization and idea generation.</p><br><p></p>

scholarly journals Natural Compounds as Guides for the Discovery of Drugs Targeting G-Protein-Coupled Receptors

Molecules ◽  
2020 ◽  
Vol 25 (21) ◽  
pp. 5060
Author(s):  
Joan Serrano-Marín ◽  
Irene Reyes-Resina ◽  
Eva Martínez-Pinilla ◽  
Gemma Navarro ◽  
Rafael Franco
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
G Protein ◽  
Natural Compounds ◽  
G Protein Coupled Receptors ◽  
Taste Receptors ◽  
Allosteric Modulators ◽  
Chemical Structures ◽  
Therapeutic Drugs ◽  
G Protein Coupled

G protein-coupled receptors (GPCRs), which constitute the most populous family of the human proteome, are the target of 35–45% of approved therapeutic drugs. This review focuses on natural products (excluding peptides) that target GPCRs. Natural compounds identified so far as agonists, antagonists or allosteric modulators of GPCRs have been found in all groups of existing living beings according to Whittaker’s Five Kingdom Classification, i.e., bacteria (monera), fungi, protoctists, plants and animals. Terpenoids, alkaloids and flavonoids are the most common chemical structures that target GPCRs whose endogenous ligands range from lipids to epinephrine, from molecules that activate taste receptors to molecules that activate smell receptors. Virtually all of the compounds whose formula is displayed in this review are pharmacophores with potential for drug discovery; furthermore, they are expected to help expand the number of GPCRs that can be considered as therapeutic targets.

scholarly journals Analysis of a large food chemical database: chemical space, diversity, and complexity

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 993 ◽  
Author(s):  
J. Jesús Naveja ◽  
Mariel P. Rico-Hidalgo ◽  
José L. Medina-Franco
Keyword(s):  
Natural Products ◽  
Chemical Space ◽  
Structural Complexity ◽  
Chemical Compounds ◽  
Chemical Diversity ◽  
Chemical Structures ◽  
Novel Approach ◽  
Chemical Database ◽  
Molecular Complexity ◽  
Limited Basis

Background: Food chemicals are a cornerstone in the food industry. However, its chemical diversity has been explored on a limited basis, for instance, previous analysis of food-related databases were done up to 2,200 molecules. The goal of this work was to quantify the chemical diversity of chemical compounds stored in FooDB, a database with nearly 24,000 food chemicals. Methods: The visual representation of the chemical space of FooDB was done with ChemMaps, a novel approach based on the concept of chemical satellites. The large food chemical database was profiled based on physicochemical properties, molecular complexity and scaffold content. The global diversity of FoodDB was characterized using Consensus Diversity Plots. Results: It was found that compounds in FooDB are very diverse in terms of properties and structure, with a large structural complexity. It was also found that one third of the food chemicals are acyclic molecules and ring-containing molecules are mostly monocyclic, with several scaffolds common to natural products in other databases. Conclusions: To the best of our knowledge, this is the first analysis of the chemical diversity and complexity of FooDB. This study represents a step further to the emerging field of “Food Informatics”. Future study should compare directly the chemical structures of the molecules in FooDB with other compound databases, for instance, drug-like databases and natural products collections.

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