Target Prediction Model for Natural Products Using Transfer Learning

A Multi-Label Learning Framework for Predicting Chemical Classes and Biological Activities of Natural Products from Biosynthetic Gene Clusters

Natural products (NP) or secondary metabolites, as a class of small chemical molecules that are naturally synthesized by chromosomally clustered biosynthesis genes (also called biosynthetic gene clusters, BGCs) encoded enzymes or enzyme complexes, mediates the bioecological interactions between host and microbiota and provides a natural reservoir for screening drug-like therapeutic pharmaceuticals. In this work, we propose a multi-label learning framework to functionally annotate natural products or secondary metabolites solely from their catalytical biosynthetic gene clusters without experimentally conducting NP structural resolutions. All chemical classes and bioactivities constitute the label space, and the sequence domains of biosynthetic gene clusters that catalyse the biosynthesis of natural products constitute the feature space. In this multi-label learning framework, a joint representation of features (BGCs domains) and labels (natural products annotations) is efficiently learnt in an integral and low-dimensional space to accurately define the inter-class boundaries and scale to the learning problem of many imbalanced labels. Computational results on experimental data show that the proposed framework achieves satisfactory multi-label learning performance, and the learnt patterns of BGCs domains are transferrable across bacteria, or even across kingdom, for instance, from bacteria to Arabidopsis thaliana. Lastly, take Arabidopsis thaliana and its rhizosphere microbiome for example, we propose a pipeline combining existing BGCs identification tools and this proposed framework to find and functionally annotate novel natural products for downstream bioecological studies in terms of plant-microbiota-soil interactions and plant environmental adaption.

Combining multidimensional chromatography-mass spectrometry and feature-based molecular networking methods for the systematic characterization of compounds in the supercritical fluid extract of Tripterygium wilfordii Hook F

Tripterygium wilfordii Hook F from the family Celastraceae is a traditional Chinese medicine (TCM) whose principal chemical constituents are terpenoids, including sesquiterpene alkaloids and diterpenoids, which have unique and diverse structures and remarkable biological activities. In order to advance pharmacological research and guide the preparation of monomer compounds derived from T. wilfordii, a systematic approach to efficiently discover new compounds or their derivatives is needed. Herein, compound separation and identification were performed by offline reversed-phase × supercritical fluid chromatography coupled mass spectrometry (RP × SFC-Q-TOF-MS/MS) and Global Natural Product Social (GNPS) molecular networking. The 2D chromatography system exhibited a high degree of orthogonality and significant peak capacity, and SFC has an advantage during the separation of sesquiterpene alkaloid isomers. Feature-based molecular networking offers the great advantage of quickly detecting and clustering unknown compounds, which greatly assists in intuitively judging the type of compound, and this networking technique has the potential to dramatically accelerate the identification and characterization of compounds from natural sources. A total of 324 compounds were identified and quantitated, including 284 alkaloids, 22 diterpenoids and 18 triterpenoids, which means that there are numerous potential new compounds with novel structures to be further explored. Overall, feature-based molecular networking provides an effective method for discovering and characterizing novel compounds and guides the separation and preparation of targeted natural products.

How to Completely Squeeze a Fungus-Advanced Genome Mining Tools for Novel Bioactive Substances

Fungal species have the capability of producing an overwhelming diversity of bioactive substances that can have beneficial but also detrimental effects on human health. These so-called secondary metabolites naturally serve as antimicrobial "weapon systems", signaling molecules or developmental effectors for fungi and hence are produced only under very specific environmental conditions or stages in their life cycle. However, as these complex conditions are difficult or even impossible to mimic in laboratory settings, only a small fraction of the true chemical diversity of fungi is known so far. This also implies that a large space for potentially new pharmaceuticals remains unexplored. We here present an overview on current developments in advanced methods that can be used to explore this chemical space. We focus on genetic and genomic methods, how to detect genes that harbor the blueprints for the production of these compounds (i.e., biosynthetic gene clusters, BGCs), and ways to activate these silent chromosomal regions. We provide an in-depth view of the chromatin-level regulation of BGCs and of the potential to use the CRISPR/Cas technology as an activation tool.

Future Directions for the Discovery of Natural Product-Derived Immunomodulating Drugs

Drug discovery from natural sources is going through a renaissance, having spent many decades in the shadow of synthetic molecule drug discovery, despite the fact that natural product-derived compounds occupy a much greater chemical space than those created through synthetic chemistry methods. With this new era comes new possibilities, not least the novel targets that have emerged in recent times and the development of state-of-the-art technologies that can be applied to drug discovery from natural sources. Although progress has been made with some immunomodulating drugs, there remains a pressing need for new agents that can be used to treat the wide variety of conditions that arise from disruption, or over-activation, of the immune system; natural products may therefore be key in filling this gap. Recognising that, at present, there is no authoritative article that details the current state-of-the-art of the immunomodulatory activity of natural products, this in-depth review has arisen from a joint effort between the International Union of Basic and Clinical Pharmacology (IUPHAR) Natural Products and Immunopharmacology, with contributions from a Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation number of world-leading researchers in the field of natural product drug discovery, to provide a "position statement" on what natural products has to offer in the search for new immunomodulatory argents. To this end, we provide a historical look at previous discoveries of naturally occurring immunomodulators, present a picture of the current status of the field and provide insight into the future opportunities and challenges for the discovery of new drugs to treat immune-related diseases.

Computer-Aided Drug Design (CADD) to De-Orphanize Marine Molecules: Finding Potential Therapeutic Agents for Neurodegenerative and Cardiovascular Diseases

Computer-aided drug design (CADD) techniques allow the identification of compounds capable of modulating protein functions in pathogenesis-related pathways, which is a promising line on drug discovery. Marine natural products (MNPs) are considered a rich source of bioactive compounds, as the oceans are home to much of the planet's biodiversity. Biodiversity is directly related to chemodiversity, which can inspire new drug discoveries. Therefore, natural products (NPs) in general, and MNPs in particular, have been used for decades as a source of inspiration for the design of new drugs. However, NPs present both opportunities and challenges. These difficulties can be technical, such as the need to dive or trawl to collect the organisms possessing the compounds, or biological, due to their particular marine habitats and the fact that they can be uncultivable in the laboratory. For all these difficulties, the contributions of CADD can play a very relevant role in simplifying their study, since, for example, no biological sample is needed to carry out an in-silico analysis. Therefore, the amount of natural product that needs to be used in the entire preclinical and clinical study is significantly reduced. Here, we exemplify how this combination between CADD and MNPs can help unlock their therapeutic potential. In this study, using a set of marine invertebrate molecules, we elucidate their possible molecular targets and associated therapeutic potential, establishing a pipeline that can be replicated in future studies.

Inhibition Ability of Natural Compounds on Receptor-Binding Domain of SARS-CoV2: An In Silico Approach

The lack of medication to treat COVID-19 is still an obstacle that needs to be addressed by all possible scientific approaches. It is essential to design newer drugs with varied approaches. A receptor-binding domain (RBD) is a key part of SARS-CoV-2 virus, located on its surface, that allows it to dock to ACE2 receptors present on human cells, which is followed by admission of virus into cells, and thus infection is triggered. Specific receptor-binding domains on the spike protein play a pivotal role in binding to the receptor. In this regard, the in silico method plays an important role, as it is more rapid and cost effective than the trial and error methods using experimental studies. A combination of virtual screening, molecular docking, molecular simulations and machine learning techniques are applied on a library of natural compounds to identify ligands that show significant binding affinity at the hydrophobic pocket of the RBD. A list of ligands with high binding affinity was obtained using molecular docking and molecular dynamics (MD) simulations for protein–ligand complexes. Machine learning (ML) classification schemes have been applied to obtain features of ligands and important descriptors, which help in identification of better binding ligands. A plethora of descriptors were used for training the self-organizing map algorithm. The model brings out descriptors important for protein–ligand interactions.