SANCDB: a South African natural compound database

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.

Virtual screening of natural products to enhance melanogenosis

Natural products have long been an important source of inspiration for medicinal chemistry and drug discovery. In the cosmetic field, they remain the major elements of the composition and serve as marketing asset. Recent research showed the implication of salt-inducible kinases on the melanin production in skin via MITF regulation. Finding new potent modulators on such target could open the way to several cosmetic applications to attenuate visible signs of photoaging and improve the tan without sun. Since virtual screening can be a powerful tool for detecting hit compounds in the early stages of a drug discovery process, we applied this method on salt-inducible kinase 2 to discover potential interesting compounds. Here, we present the different steps from the construction of a database of natural products, to the validation of a docking protocol and the results of the virtual screening. Hits from the screening were tested in vitro to confirm their efficiency and results are discussed.

Development of a pharmaceutical database as an aid to the nonclinical detection of drug-induced cardiac toxicity

The Health and Environmental Sciences Institute (HESI) Cardiac Safety Committee designed and created a publicly accessible database with an initial set of 128 pharmacologically defined pharmaceutical agents, many with known cardiotoxic properties. The database includes specific information about each compound that could be useful in evaluating hypotheses around mechanisms of drug-induced cardiac toxicity or for development of novel cardiovascular safety assays. Data on each of the compounds was obtained from published literature and online sources (e.g., DrugBank.ca and International Union of Basic and Clinical Pharmacology (IUPHAR) / British Pharmacological Society (BPS) Guide to PHARMACOLOGY) and was curated by 10 subject matter experts. The database includes information such as compound name, pharmacological mode of action, characterized cardiac mode of action, type of cardiac toxicity, known clinical cardiac toxicity profile, animal models used to evaluate the cardiotoxicity profile, routes of administration, and toxicokinetic parameters (i.e., Cmax). Data from both nonclinical and clinical studies are included for each compound. The user-friendly web interface allows for multiple approaches to search the database and is also intended to provide a means for the submission of new data/compounds from relevant users. This will ensure that the database is constantly updated and remains current. Such a data repository will not only aid the HESI working groups in defining drugs for use in any future studies, but safety scientists can also use the database as a vehicle of support for broader cardiovascular safety studies or exploring mechanisms of toxicity associated with certain pharmacological modes of action.

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.

In silico screening of SARS-CoV2 helicase using African natural products: Docking and molecular dynamics approaches

In the current medical era, there is an urgent necessity to identify new effective drugs to enrich the COVID-19's therapeutic arsenal. The SARS-COV-2 NSP13/helicase enzyme has been identified as a potential target for developing novel COVID-19 inhibitors. In this work, we aimed at endorsing effective natural products with potential inhibitory action towards the NSP13 through the virtual screening of 1012 natural products of botanical and marine origin from the South African Natural Compounds Database (SANCDB). The molecules were docked into the NTPase active site, and the best twelve compounds were chosen for further analysis. Thereafter, a combination of molecular dynamics simulations and MM-GBSA free energy calculations were carried out for a subset of best hits complexed with NSP13 helicase. We believe that the findings of this work will pave the way for additional research and experimental validation of some natural products as viable NSP13 helicase inhibitors.

Natural Perylenequinone Compounds as Potent Inhibitors of Schistosoma mansoni Glutathione S-Transferase

The existing treatment strategy for Schistosomiasis centers on praziquantel, a single drug, but its effectiveness is limited due to resistance and lack of preventive benefits. Thus, there is an urgent need for novel antischistosomal agents. Schistosoma glutathione S-transferase (GST) is an essential parasite enzyme, with a high potential for targeted drug discovery. In this study, we conducted a screening of compounds possessing antihelminth properties, focusing on their interaction with the Schistosoma mansoni glutathione S-transferase (SmGST) protein. We demonstrated the unique nature of SmGST in comparison to human GST. Evolutionary analysis indicated its close relationship with other parasitic worms, setting it apart from free-living worms such as C. elegans. Through an assessment of binding pockets and subsequent protein-ligand docking, we identified Scutiaquinone A and Scutiaquinone B, both naturally derived Perylenequinones, as robust binders to SmGST. These compounds have exhibited effectiveness against similar parasites and offer promising potential as antischistosomal agents.

Investigating the role of Sterol C24-Methyl transferase mutation on drug resistance in leishmaniasis and identifying potential inhibitors

Leishmaniasis is a fatal disease caused by the leishmania parasite. For the survival of the leishmania parasite, Sterol C24-Methyl Transferase (SMT) is essential which is an enzyme of the ergosterol pathway. SMT protein mutation is responsible for Amphotericin-B drug resistance in Leishmania, which is the main treatment for visceral leishmaniasis. Amphotericin-B resistance is caused by three mutated residues V131I, V321I and F72C. The underlying mechanisms and structural changes in SMT enzymes responsible for resistance due to mutation are still not well understood. In the current study, the potential mechanism of resistance due to these mutations and the structure variation of wild and mutant SMT proteins were investigated through molecular dynamics simulations and molecular docking analysis. The results showed that AmB established strong bonding interaction with wild SMT as compare to mutants SMT. The binding energy calculation showed that binding energy of AmB with mutants SMT increases as compare to the wild SMT. Further structural based virtual screening was carried out to design potential inhibitors for the mutant SMT. On the basis of structural-based virtual screening four inhibitors (SANC01057, SANC00882, SANC00414, SANC01047) were computationally identified as potential mutant SMT (F72C) inhibitors. This work provides valuable information for improved management of drug resistant Leishmaniasis.Communicated by Ramaswamy H. Sarma.

Multi-dimensional structural footprint identification for the design of potential scaffolds targeting METTL3 in cancer treatment from natural compounds

CONTEXT

[Formula: see text]-adenosine-methyltransferase (METTL3) is the catalytic domain of the 'writer' proteins which is involved in the post modifications of [Formula: see text]-methyladinosine ([Formula: see text]). Though its activities are essential in many biological processes, it has been implicated in several types of cancer. Thus, drug developers and researchers are relentlessly in search of small molecule inhibitors that can ameliorate the oncogenic activities of METTL3. Currently, STM2457 is a potent, highly selective inhibitor of METTL3 but is yet to be approved.

METHODS

In this study, we employed structure-based virtual screening through consensus docking by using AutoDock Vina in PyRx interface and Glide virtual screening workflow of Schrodinger Glide. Thermodynamics via MM-PBSA calculations was further used to rank the compounds based on their total free binding energies. All atom molecular dynamics simulations were performed using AMBER 18 package. FF14SB force fields and Antechamber were used to parameterize the protein and compounds respectively. Post analysis of generated trajectories was analyzed with CPPTRAJ and PTRAJ modules incorporated in the AMBER package while Discovery studio and UCSF Chimera were used for visualization, and origin data tool used to plot all graphs.

RESULTS

Three compounds with total free binding energies higher than STM2457 were selected for extended molecular dynamics simulations. The compounds, SANCDB0370, SANCDB0867, and SANCDB1033, exhibited stability and deeper penetration into the hydrophobic core of the protein. They engaged in relatively stronger intermolecular interactions involving hydrogen bonds with resultant increase in stability, reduced flexibility, and decrease in the surface area of the protein available for solvent interactions suggesting an induced folding of the catalytic domain. Furthermore, in silico pharmacokinetics and physicochemical analysis of the compounds revealed good properties suggesting these compounds could serve as promising MEETL3 entry inhibitors upon modifications and optimizations as presented by natural compounds. Further biochemical testing and experimentations would aid in the discovery of effective inhibitors against the berserk activities of METTL3.

Identification of promising high-affinity inhibitors of SARS-CoV-2 main protease from African Natural Products Databases by Virtual Screening

With the rapid spread of the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen agent of COVID-19 pandemic created a serious threat to global public health, requiring the most urgent research for potential therapeutic agents. The availability of genomic data of SARS-CoV-2 and efforts to determine the protein structure of the virus facilitated the identification of potent inhibitors by using structure-based approach and bioinformatics tools. Many pharmaceuticals have been proposed for the treatment of COVID-19, although their effectiveness has not been assessed yet. However, it is important to find out new-targeted drugs to overcome the resistance concern. Several viral proteins such as proteases, polymerases or structural proteins have been considered as potential therapeutic targets. But the virus target must be essential for host invasion match some drugability criterion. In this Work, we selected the highly validated pharmacological target main protease Mpro and we performed high throughput virtual screening of African Natural Products Databases such as NANPDB, EANPDB, AfroDb, and SANCDB to identify the most potent inhibitors with the best pharmacological properties. In total, 8753 natural compounds were virtually screened by AutoDock vina against the main protease of SARS-CoV-2. Two hundred and five (205) compounds showed high-affinity scores (less than - 10.0 Kcal/mol), while fifty-eight (58) filtered through Lipinski's rules showed better affinity than known Mpro inhibitors (i.e., ABBV-744, Onalespib, Daunorubicin, Alpha-ketoamide, Perampanel, Carprefen, Celecoxib, Alprazolam, Trovafloxacin, Sarafloxacin, Ethyl biscoumacetate…). Those promising compounds could be considered for further investigations toward the developpement of SARS-CoV-2 drug development.

Anti-trypanosomatid structure-based drug design - lessons learned from targeting the folate pathway

INTRODUCTION

Trypanosomatidic parasitic infections of humans and animals caused by Trypanosoma brucei, Trypanosoma cruzi, and Leishmania species pose a significant health and economic burden in developing countries. There are few effective and accessible treatments for these diseases, and the existing therapies suffer from problems such as parasite resistance and side effects. Structure-based drug design (SBDD) is one of the strategies that has been applied to discover new compounds targeting trypanosomatid-borne diseases.

AREAS COVERED

We review the current literature (mostly over the last 5 years, searched in PubMed database on Nov 11^th 2021) on the application of structure-based drug design approaches to identify new anti-trypanosomatidic compounds that interfere with a validated target biochemical pathway, the trypanosomatid folate pathway.

EXPERT OPINION

The application of structure-based drug design approaches to perturb the trypanosomatid folate pathway has successfully provided many new inhibitors with good selectivity profiles, most of which are natural products or their derivatives or have scaffolds of known drugs. However, the inhibitory effect against the target protein(s) often does not translate to anti-parasitic activity. Further progress is hampered by our incomplete understanding of parasite biology and biochemistry, which is necessary to complement SBDD in a multiparameter optimization approach to discovering selective anti-parasitic drugs.

In-Silico targeting of SARS-CoV-2 NSP6 for drug and natural products repurposing

Non-Structural Protein 6 (NSP6) has a protecting role for SARS-CoV-2 replication by inhibiting the expansion of autophagosomes inside the cell. NSP6 is involved in the endoplasmic reticulum stress response by binding to Sigma receptor 1 (SR1). Nevertheless, NSP6 crystal structure is not solved yet. Therefore, NSP6 is considered a challenging target in Structure-Based Drug Discovery. Herein, we utilized the high quality NSP6 model built by AlphaFold in our study. Targeting a putative NSP6 binding site is believed to inhibit the SR1-NSP6 protein-protein interactions. Three databases were virtually screened, namely FDA-approved drugs (DrugBank), Northern African Natural Products Database (NANPDB) and South African Natural Compounds Database (SANCDB) with a total of 8158 compounds. Further validation for 9 candidates via molecular dynamics simulations for 100 ns recommended potential binders to the NSP6 binding site. The proposed candidates are recommended for biological testing to cease the rapidly growing pandemic.

The LOTUS initiative for open knowledge management in natural products research

Contemporary bioinformatic and chemoinformatic capabilities hold promise to reshape knowledge management, analysis and interpretation of data in natural products research. Currently, reliance on a disparate set of non-standardized, insular, and specialized databases presents a series of challenges for data access, both within the discipline and for integration and interoperability between related fields. The fundamental elements of exchange are referenced structure-organism pairs that establish relationships between distinct molecular structures and the living organisms from which they were identified. Consolidating and sharing such information via an open platform has strong transformative potential for natural products research and beyond. This is the ultimate goal of the newly established LOTUS initiative, which has now completed the first steps toward the harmonization, curation, validation and open dissemination of 750,000+ referenced structure-organism pairs. LOTUS data is hosted on Wikidata and regularly mirrored on https://lotus.naturalproducts.net. Data sharing within the Wikidata framework broadens data access and interoperability, opening new possibilities for community curation and evolving publication models. Furthermore, embedding LOTUS data into the vast Wikidata knowledge graph will facilitate new biological and chemical insights. The LOTUS initiative represents an important advancement in the design and deployment of a comprehensive and collaborative natural products knowledge base.

Translational Informatics for Natural Products as Antidepressant Agents

Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.

Novel dynamic residue network analysis approaches to study allosteric modulation: SARS-CoV-2 Mpro and its evolutionary mutations as a case study

The rational search for allosteric modulators and the allosteric mechanisms of these modulators in the presence of mutations is a relatively unexplored field. Here, we established novel in silico approaches and applied them to SARS-CoV-2 main protease (Mpro) as a case study. First, we identified six potential allosteric modulators. Then, we focused on understanding the allosteric effects of these modulators on each of its protomers. We introduced a new combinatorial approach and dynamic residue network (DRN) analysis algorithms to examine patterns of change and conservation of critical nodes, according to five independent criteria of network centrality. We observed highly conserved network hubs for each averaged DRN metric on the basis of their existence in both protomers in the absence and presence of all ligands (persistent hubs). We also detected ligand specific signal changes. Using eigencentrality (EC) persistent hubs and ligand introduced hubs we identified a residue communication path connecting the allosteric binding site to the catalytic site. Finally, we examined the effects of the mutations on the behavior of the protein in the presence of selected potential allosteric modulators and investigated the ligand stability. One crucial outcome was to show that EC centrality hubs form an allosteric communication path between the allosteric ligand binding site to the active site going through the interface residues of domains I and II; and this path was either weakened or lost in the presence of some of the mutations. Overall, the results revealed crucial aspects that need to be considered in rational computational drug discovery.

NPCDR: natural product-based drug combination and its disease-specific molecular regulation

Natural product (NP) has a long history in promoting modern drug discovery, which has derived or inspired a large number of currently prescribed drugs. Recently, the NPs have emerged as the ideal candidates to combine with other therapeutic strategies to deal with the persistent challenge of conventional therapy, and the molecular regulation mechanism underlying these combinations is crucial for the related communities. Thus, it is urgently demanded to comprehensively provide the disease-specific molecular regulation data for various NP-based drug combinations. However, no database has been developed yet to describe such valuable information. In this study, a newly developed database entitled ‘Natural Product-based Drug Combination and Its Disease-specific Molecular Regulation (NPCDR)’ was thus introduced. This database was unique in (a) providing the comprehensive information of NP-based drug combinations & describing their clinically or experimentally validated therapeutic effect, (b) giving the disease-specific molecular regulation data for a number of NP-based drug combinations, (c) fully referencing all NPs, drugs, regulated molecules/pathways by cross-linking them to the available databases describing their biological or pharmaceutical characteristics. Therefore, NPCDR is expected to have great implications for the future practice of network pharmacology, medical biochemistry, drug design, and medicinal chemistry. This database is now freely accessible without any login requirement at both official (https://idrblab.org/npcdr/) and mirror (http://npcdr.idrblab.net/) sites.

Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop

We report the major conclusions of the online open-access workshop "Computational Applications in Secondary Metabolite Discovery (CAiSMD)" that took place from 08 to 10 March 2021. Invited speakers from academia and industry and about 200 registered participants from five continents (Africa, Asia, Europe, South America, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites (SMs) or natural products (NPs) as potential drugs and drug leads. During 3 days, the participants of this online workshop received an overview of modern computer-based approaches for exploring NP discovery in the "omics" age. The invited experts gave keynote lectures, trained participants in hands-on sessions, and held round table discussions. This was followed by oral presentations with much interaction between the speakers and the audience. Selected applicants (early-career scientists) were offered the opportunity to give oral presentations (15 min) and present posters in the form of flash presentations (5 min) upon submission of an abstract. The final program available on the workshop website ( https://caismd.indiayouth.info/ ) comprised of 4 keynote lectures (KLs), 12 oral presentations (OPs), 2 round table discussions (RTDs), and 5 hands-on sessions (HSs). This meeting report also references internet resources for computational biology in the area of secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community. The workshop concluded with an online survey form to be completed by speakers and participants for the goal of improving any subsequent editions.

Web repositories of natural agents promote pests and pathogenic microbes management

The grand challenge to meet the increasing demands for food by a rapidly growing global population requires protecting crops from pests. Natural active substances play a significant role in the sustainable pests and pathogenic microbes management. In recent years, natural products- (NPs), antimicrobial peptides- (AMPs), medicinal plant- and plant essential oils (EOs)-related online resources have greatly facilitated the development of pests and pathogenic microbes control agents in an efficient and economical manner. However, a comprehensive comparison, analysis and summary of these existing web resources are still lacking. Here, we surveyed these databases of NPs, AMPs, medicinal plants and plant EOs with insecticidal, antibacterial, antiviral and antifungal activity, and we compared their functionality, data volume, data sources and applicability. We comprehensively discussed the limitation of these web resources. This study provides a toolbox for bench scientists working in the pesticide, botany, biomedical and pharmaceutical engineering fields. The aim of the review is to hope that these web resources will facilitate the discovery and development of potential active ingredients of pests and pathogenic microbes control agents.

SANCDB: an update on South African natural compounds and their readily available analogs

BACKGROUND

South African Natural Compounds Database (SANCDB; https://sancdb.rubi.ru.ac.za/ ) is the sole and a fully referenced database of natural chemical compounds of South African biodiversity. It is freely available, and since its inception in 2015, the database has become an important resource to several studies. Its content has been: used as training data for machine learning models; incorporated to larger databases; and utilized in drug discovery studies for hit identifications.

DESCRIPTION

Here, we report the updated version of SANCDB. The new version includes 412 additional compounds that have been reported since 2015, giving a total of 1012 compounds in the database. Further, although natural products (NPs) are an important source of unique scaffolds, they have a major drawback due to their complex structure resulting in low synthetic feasibility in the laboratory. With this in mind, SANCDB is, now, updated to provide direct links to commercially available analogs from two major chemical databases namely Mcule and MolPort. To our knowledge, this feature is not available in other NP databases. Additionally, for easier access to information by users, the database and website interface were updated. The compounds are now downloadable in many different chemical formats.

CONCLUSIONS

The drug discovery process relies heavily on NPs due to their unique chemical organization. This has inspired the establishment of numerous NP chemical databases. With the emergence of newer chemoinformatic technologies, existing chemical databases require constant updates to facilitate information accessibility and integration by users. Besides increasing the NPs compound content, the updated SANCDB allows users to access the individual compounds (if available) or their analogs from commercial databases seamlessly.

Predicting Potential SARS-COV-2 Drugs-In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking

Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing drugs, as well as the discovery of multiple vaccines. During an urgent crisis, rapidly identifying potential new treatments requires global and cross-discipline cooperation, together with an enhanced open-access research model to distribute new ideas and leads. Herein, we introduce an application of a deep neural network based drug screening method, validating it using a docking algorithm on approved drugs for drug repurposing efforts, and extending the screen to a large library of 750,000 compounds for de novo drug discovery effort. The results of large library screens are incorporated into an open-access web interface to allow researchers from diverse fields to target molecules of interest. Our combined approach allows for both the identification of existing drugs that may be able to be repurposed and de novo design of ACE2-regulatory compounds. Through these efforts we demonstrate the utility of a new machine learning algorithm for drug discovery, SSnet, that can function as a tool to triage large molecular libraries to identify classes of molecules with possible efficacy.

VSPrep: A KNIME Workflow for the Preparation of Molecular Databases for Virtual Screening

Drug discovery is a challenging and expensive field. Hence, novel in silico tools have been developed in early discovery stage to identify and prioritize novel molecules with suitable physicochemical properties. In many in silico drug design projects, molecular databases are screened by virtual screening tools to search for potential bioactive molecules. The preparation of the molecules is therefore a key step in the success of well-established techniques such as docking, similarity or pharmacophore searching. We review here the lists of several toolkits used in different steps during the cleaning of molecular databases, integrated within a KNIME workflow. During the first step of the automatic workflow, salts are removed, and mixtures are split to get one compound per entry. Then compounds with unwanted features are filtered. Duplicated entries are then deleted while considering stereochemistry. As a compromise between exhaustiveness and computational time, most distributed tautomers at physiological pH are computed. Additionally, various flags are applied to molecules by using either classical molecular descriptors, similarity search to known libraries or substructure search rules. Moreover, stereoisomers are enumerated depending on the unassigned chiral centers. Then, three-dimensional coordinates, and optionally conformers, are generated. This workflow has been already applied to several drug design projects and can be used for molecular database preparation upon request.

COCONUT online: Collection of Open Natural Products database

Natural products (NPs) are small molecules produced by living organisms with potential applications in pharmacology and other industries as many of them are bioactive. This potential raised great interest in NP research around the world and in different application fields, therefore, over the years a multiplication of generalistic and thematic NP databases has been observed. However, there is, at this moment, no online resource regrouping all known NPs in just one place, which would greatly simplify NPs research and allow computational screening and other in silico applications. In this manuscript we present the online version of the COlleCtion of Open Natural prodUcTs (COCONUT): an aggregated dataset of elucidated and predicted NPs collected from open sources and a web interface to browse, search and easily and quickly download NPs. COCONUT web is freely available at https://coconut.naturalproducts.net .

Inhibition analysis of aflatoxin by in silico targeting the thioesterase domain of polyketide synthase enzyme in Aspergillus ssp

The spread of fungal growth causes enormous economic, agricultural, and health problems for humans, such as Aspergillus sp., which produce aflatoxins. Thus, the inhibition of aflatoxin production became a precious target. In this research, the thioesterase (TE) domain from Polyketide synthase enzyme was selected to employ the in silico docking, using AutoDock Vina, against 623 natural compounds from the South African natural compound database (SANCDB), to identify potential inhibitors that can selectively inhibit thioesterase domain. The top ten inhibitors components were pinocembrin, typhaphthalide, p-coumaroylputrescine, dilemmaone A, 9-angelylplatynecine, 2,4,6-octatrienal, 4,8-dichloro-3,7-dimethyl-, (2e,4z,6e)-, lilacinobiose, 1,3,7-octatriene, 5,6-dichloro-2-(dichloromethyl)-6-methyl-, [r*,s*-(e)]-(-)- (9ci), lilacinobiose, 1,3,7-octatriene, 5,6-dichloro-2-(dichloromethyl)-6-methyl-, [r*,s*-(e)]-(-)- (9ci), 1,3,7-octatriene, 1,5,6-trichloro-2-(dichloromethyl)-6-methyl-, [r*,s*-(z,e)] and 9-angelylhastanecine and that depending on the lowest binding energy, the best chemical interactions and the best drug-likeness. The results of those components gave successful inhibition with the thioesterase domain. So, they can be used for inhibition and controlling aflatoxin contamination of agriculture crop yields, specially, pinocembrin which gave promising results.Communicated by Ramaswamy H. Sarma.

Characterisation of plasmodial transketolases and identification of potential inhibitors: an in silico study

BACKGROUND

Plasmodial transketolase (PTKT) enzyme is one of the novel pharmacological targets being explored as potential anti-malarial drug target due to its functional role and low sequence identity to the human enzyme. Despite this, features contributing to such have not been exploited for anti-malarial drug design. Additionally, there are no anti-malarial drugs targeting PTKTs whereas the broad activity of these inhibitors against PTKTs from other Plasmodium spp. is yet to be reported. This study characterises different PTKTs [Plasmodium falciparum (PfTKT), Plasmodium vivax (PvTKT), Plasmodium ovale (PoTKT), Plasmodium malariae (PmTKT) and Plasmodium knowlesi (PkTKT) and the human homolog (HsTKT)] to identify key sequence and structural based differences as well as the identification of selective potential inhibitors against PTKTs.

METHODS

A sequence-based study was carried out using multiple sequence alignment, phylogenetic tree calculations and motif discovery analysis. Additionally, TKT models of PfTKT, PmTKT, PoTKT, PmTKT and PkTKT were modelled using the Saccharomyces cerevisiae TKT structure as template. Based on the modelled structures, molecular docking using 623 South African natural compounds was done. The stability, conformational changes and detailed interactions of selected compounds were accessed viz all-atom molecular dynamics (MD) simulations and binding free energy (BFE) calculations.

RESULTS

Sequence alignment, evolutionary and motif analyses revealed key differences between plasmodial and the human TKTs. High quality homodimeric three-dimensional PTKTs structures were constructed. Molecular docking results identified three compounds (SANC00107, SANC00411 and SANC00620) which selectively bind in the active site of all PTKTs with the lowest (better) binding affinity ≤ - 8.5 kcal/mol. MD simulations of ligand-bound systems showed stable fluctuations upon ligand binding. In all systems, ligands bind stably throughout the simulation and form crucial interactions with key active site residues. Simulations of selected compounds in complex with human TKT showed that ligands exited their binding sites at different time steps. BFE of protein-ligand complexes showed key residues involved in binding.

CONCLUSIONS

This study highlights significant differences between plasmodial and human TKTs and may provide valuable information for the development of novel anti-malarial inhibitors. Identified compounds may provide a starting point in the rational design of PTKT inhibitors and analogues based on these scaffolds.

Potential Impact of the Multi-Target Drug Approach in the Treatment of Some Complex Diseases

It is essential to acknowledge the efforts made thus far to manage or eliminate various disease burden faced by humankind. However, the rising global trends of the so-called incurable diseases continue to put pressure on Pharma industries and other drug discovery platforms. In the past, drugs with more than one target were deemed as undesirable options with interest being on the one-drug-single target. Despite the successes of the single-target drugs, it is currently beyond doubt that these drugs have limited efficacy against complex diseases in which the pathogenesis is dependent on a set of biochemical events and several bioreceptors operating concomitantly. Different approaches have thus been proposed to come up with effective drugs to combat even the complex diseases. In the past, the focus was on producing drugs from screening plant compounds; today, we talk about combination therapy and multi-targeting drugs. The multi-target drugs have recently attracted much attention as promising tools to fight against most challenging diseases, and thus a new research focus area. This review will discuss the potential impact of multi-target drug approach on various complex diseases with focus on malaria, tuberculosis (TB), diabetes and neurodegenerative diseases as the main representatives of multifactorial diseases. We will also discuss alternative ideas to solve the current problems bearing in mind the fourth industrial revolution on drug discovery.

Identification of Selective Novel Hits against Plasmodium falciparum Prolyl tRNA Synthetase Active Site and a Predicted Allosteric Site Using in silico Approaches

Recently, there has been increased interest in aminoacyl tRNA synthetases (aaRSs) as potential malarial drug targets. These enzymes play a key role in protein translation by the addition of amino acids to their cognate tRNA. The aaRSs are present in all Plasmodium life cycle stages, and thus present an attractive malarial drug target. Prolyl tRNA synthetase is a class II aaRS that functions in charging tRNA with proline. Various inhibitors against Plasmodium falciparum ProRS (PfProRS) active site have been designed. However, none have gone through clinical trials as they have been found to be highly toxic to human cells. Recently, a possible allosteric site was reported in PfProRS with two possible allosteric modulators: glyburide and TCMDC-124506. In this study, we sought to identify novel selective inhibitors targeting PfProRS active site and possible novel allosteric modulators of this enzyme. To achieve this, virtual screening of South African natural compounds against PfProRS and the human homologue was carried out using AutoDock Vina. The modulation of protein motions by ligand binding was studied by molecular dynamics (MD) using the GROningen MAchine for Chemical Simulations (GROMACS) tool. To further analyse the protein global motions and energetic changes upon ligand binding, principal component analysis (PCA), and free energy landscape (FEL) calculations were performed. Further, to understand the effect of ligand binding on the protein communication, dynamic residue network (DRN) analysis of the MD trajectories was carried out using the MD-TASK tool. A total of ten potential natural hit compounds were identified with strong binding energy scores. Binding of ligands to the protein caused observable global and residue level changes. Dynamic residue network calculations showed increase in betweenness centrality (BC) metric of residues at the allosteric site implying these residues are important in protein communication. A loop region at the catalytic domain between residues 300 and 350 and the anticodon binding domain showed significant contributions to both PC1 and PC2. Large motions were observed at a loop in the Z-domain between residues 697 and 710 which was also in agreement with RMSF calculations that showed increase in flexibility of residues in this region. Residues in this loop region are implicated in ATP binding and thus a change in dynamics may affect ATP binding affinity. Free energy landscape (FEL) calculations showed that the holo protein (protein-ADN complex) and PfProRS-SANC184 complexes were stable, as shown by the low energy with very few intermediates and hardly distinguishable low energy barriers. In addition, FEL results agreed with backbone RMSD distribution plots where stable complexes showed a normal RMSD distribution while unstable complexes had multimodal RMSD distribution. The betweenness centrality metric showed a loss of functional importance of key ATP binding site residues upon allosteric ligand binding. The deep basins in average L observed at the allosteric region imply that there is high accessibility of residues at this region. To further analyse BC and average L metrics data, we calculated the ΔBC and ΔL values by taking each value in the holo protein BC or L matrix less the corresponding value in the ligand-bound complex BC or L matrix. Interestingly, in allosteric complexes, residues located in a loop region implicated in ATP binding had negative ΔL values while in orthosteric complexes these residues had positive ΔL values. An increase in contact frequency between residues Ser263, Thr267, Tyr285, and Leu707 at the allosteric site and residues Thr397, Pro398, Thr402, and Gln395 at the ATP binding TXE loop was observed. In summary, this study identified five potential orthosteric inhibitors and five allosteric modulators against PfProRS. Allosteric modulators changed ATP binding site dynamics, as shown by RMSF, PCA, and DRN calculations. Changes in dynamics of the ATP binding site and increased contact frequency between residues at the proposed allosteric site and the ATP binding site may explain how allosteric modulators distort the ATP binding site and thus might inhibit PfProRS. The scaffolds of the identified hits in the study can be used as a starting point for antimalarial inhibitor development with low human cytotoxicity.

An enumeration of natural products from microbial, marine and terrestrial sources

The discovery of a new drug is a multidisciplinary and very costly task. One of the major steps is the identification of a lead compound, i.e. a compound with a certain degree of potency and that can be chemically modified to improve its activity, metabolic properties, and pharmacokinetics profiles. Terrestrial sources (plants and fungi), microbes and marine organisms are abundant resources for the discovery of new structurally diverse and biologically active compounds. In this chapter, an attempt has been made to quantify the numbers of known published chemical structures (available in chemical databases) from natural sources. Emphasis has been laid on the number of unique compounds, the most abundant compound classes and the distribution of compounds in terrestrial and marine habitats. It was observed, from the recent investigations, that ~500,000 known natural products (NPs) exist in the literature. About 70 % of all NPs come from plants, terpenoids being the most represented compound class (except in bacteria, where amino acids, peptides, and polyketides are the most abundant compound classes). About 2,000 NPs have been co-crystallized in PDB structures.

Review on natural products databases: where to find data in 2020

Natural products (NPs) have been the centre of attention of the scientific community in the last decencies and the interest around them continues to grow incessantly. As a consequence, in the last 20 years, there was a rapid multiplication of various databases and collections as generalistic or thematic resources for NP information. In this review, we establish a complete overview of these resources, and the numbers are overwhelming: over 120 different NP databases and collections were published and re-used since 2000. 98 of them are still somehow accessible and only 50 are open access. The latter include not only databases but also big collections of NPs published as supplementary material in scientific publications and collections that were backed up in the ZINC database for commercially-available compounds. Some databases, even published relatively recently are already not accessible anymore, which leads to a dramatic loss of data on NPs. The data sources are presented in this manuscript, together with the comparison of the content of open ones. With this review, we also compiled the open-access natural compounds in one single dataset a COlleCtion of Open NatUral producTs (COCONUT), which is available on Zenodo and contains structures and sparse annotations for over 400,000 non-redundant NPs, which makes it the biggest open collection of NPs available to this date.

Integrated Computational Approaches and Tools forAllosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.

Plant Metabolite Databases: From Herbal Medicines to Modern Drug Discovery

Traditional herbal medicine has been an inseparable part of the traditional medical science in many countries throughout history. Nowadays, the popularity of using herbal medicines in daily life, as well as clinical practices, has gradually expanded to numerous Western countries with positive impacts and acceptance. The continuous growth of the herbal consumption market has promoted standardization and modernization of herbal-derived products with present pharmacological criteria. To store and extensively share this knowledge with the community and serve scientific research, various herbal metabolite databases have been developed with diverse focuses under the support of modern advances. The advent of these databases has contributed to accelerating research on pharmaceuticals of natural origins. In the scope of this study, we critically review 30 herbal metabolite databases, discuss different related perspectives, and provide a comparative analysis of 18 accessible noncommercial ones. We hope to provide you with fundamental information and multidimensional perspectives from herbal medicines to modern drug discovery.

ADMET profiling of geographically diverse phytochemical using chemoinformatic tools

Aim: Phytocompounds are important due to their uniqueness, however, only few reach the development phase due to their poor pharmacokinetics. Therefore, preassessing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties is essential in drug discovery. Methodology: Biologically diverse databases (Phytochemica, SerpentinaDB, SANCDB and NuBBE_DB) covering the region of India, Brazil and South Africa were considered to predict the ADMET using chemoinformatic tools (Qikprop, pkCSM and DataWarrior). Results: Screening through each of pharmacokinetic criteria resulted in identification of 24 compounds that adhere to all the ADMET properties. Furthermore, assessment revealed that five have potent anticancer biological activity against cancer cell lines. Conclusion: We have established an open-access database (ADMET-BIS) to enable identification of promising molecules that follow ADMET properties and can be considered for drug development.

Establishing Computational Approaches Towards Identifying Malarial Allosteric Modulators: A Case Study of Plasmodium falciparum Hsp70s

Combating malaria is almost a never-ending battle, as Plasmodium parasites develop resistance to the drugs used against them, as observed recently in artemisinin-based combination therapies. The main concern now is if the resistant parasite strains spread from Southeast Asia to Africa, the continent hosting most malaria cases. To prevent catastrophic results, we need to find non-conventional approaches. Allosteric drug targeting sites and modulators might be a new hope for malarial treatments. Heat shock proteins (HSPs) are potential malarial drug targets and have complex allosteric control mechanisms. Yet, studies on designing allosteric modulators against them are limited. Here, we identified allosteric modulators (SANC190 and SANC651) against P. falciparum Hsp70-1 and Hsp70-x, affecting the conformational dynamics of the proteins, delicately balanced by the endogenous ligands. Previously, we established a pipeline to identify allosteric sites and modulators. This study also further investigated alternative approaches to speed up the process by comparing all atom molecular dynamics simulations and dynamic residue network analysis with the coarse-grained (CG) versions of the calculations. Betweenness centrality (BC) profiles for PfHsp70-1 and PfHsp70-x derived from CG simulations not only revealed similar trends but also pointed to the same functional regions and specific residues corresponding to BC profile peaks.

South African Abietane Diterpenoids and Their Analogs as Potential Antimalarials: Novel Insights from Hybrid Computational Approaches

The hemoglobin degradation process in Plasmodium parasites is vital for nutrient acquisition required for their growth and proliferation. In P. falciparum, falcipains (FP-2 and FP-3) are the major hemoglobinases, and remain attractive antimalarial drug targets. Other Plasmodium species also possess highly homologous proteins to FP-2 and FP-3. Although several inhibitors have been designed against these proteins, none has been commercialized due to associated toxicity on human cathepsins (Cat-K, Cat-L and Cat-S). Despite the two enzyme groups sharing a common structural fold and catalytic mechanism, distinct active site variations have been identified, and can be exploited for drug development. Here, we utilize in silico approaches to screen 628 compounds from the South African natural sources to identify potential hits that can selectively inhibit the plasmodial proteases. Using docking studies, seven abietane diterpenoids, binding strongly to the plasmodial proteases, and three additional analogs from PubChem were identified. Important residues involved in ligand stabilization were identified for all potential hits through binding pose analysis and their energetic contribution determined by binding free energy calculations. The identified compounds present important scaffolds that could be further developed as plasmodial protease inhibitors. Previous laboratory assays showed the effect of the seven diterpenoids as antimalarials. Here, for the first time, we demonstrate that their possible mechanism of action could be by interacting with falcipains and their plasmodial homologs. Dynamic residue network (DRN) analysis on the plasmodial proteases identified functionally important residues, including a region with high betweenness centrality, which had previously been proposed as a potential allosteric site in FP-2.

STarFish: A Stacked Ensemble Target Fishing Approach and its Application to Natural Products

Target fishing is the process of identifying the protein target of a bioactive small molecule. To do so experimentally requires a significant investment of time and resources, which can be expedited with a reliable computational target fishing model. The development of computational target fishing models using machine learning has become very popular over the last several years because of the increased availability of large amounts of public bioactivity data. Unfortunately, the applicability and performance of such models for natural products has not yet been comprehensively assessed. This is, in part, due to the relative lack of bioactivity data available for natural products compared to synthetic compounds. Moreover, the databases commonly used to train such models do not annotate which compounds are natural products, which makes the collection of a benchmarking set difficult. To address this knowledge gap, a data set composed of natural product structures and their associated protein targets was generated by cross-referencing 20 publicly available natural product databases with the bioactivity database ChEMBL. This data set contains 5589 compound-target pairs for 1943 unique compounds and 1023 unique targets. A synthetic data set comprising 107 190 compound-target pairs for 88 728 unique compounds and 1907 unique targets was used to train k-nearest neighbors, random forest, and multilayer perceptron models. The predictive performance of each model was assessed by stratified 10-fold cross-validation and benchmarking on the newly collected natural product data set. Strong performance was observed for each model during cross-validation with area under the receiver operating characteristic (AUROC) scores ranging from 0.94 to 0.99 and Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC) scores from 0.89 to 0.94. When tested on the natural product data set, performance dramatically decreased with AUROC scores ranging from 0.70 to 0.85 and BEDROC scores from 0.43 to 0.59. However, the implementation of a model stacking approach, which uses logistic regression as a meta-classifier to combine model predictions, dramatically improved the ability to correctly predict the protein targets of natural products and increased the AUROC score to 0.94 and BEDROC score to 0.73. This stacked model was deployed as a web application, called STarFish, and has been made available for use to aid in target identification for natural products.

Computer-Assisted Drug Virtual Screening Based on the Natural Product Databases

BACKGROUND

Computer-assisted drug virtual screening models the process of drug screening through computer simulation technology, by docking small molecules in some of the databases to a certain protein target. There are many kinds of small molecules databases available for drug screening, including natural product databases.

METHODS

Plants have been used as a source of medication for millennia. About 80% of drugs were either natural products or related analogues by 1990, and many natural products are biologically active and have favorable absorption, distribution, metabolization, excretion, and toxicology.

RESULTS

In this paper, we review the natural product databases' contributions to drug discovery based on virtual screening, focusing particularly on the introductions of plant natural products, microorganism natural product, Traditional Chinese medicine databases, as well as natural product toxicity prediction databases.

CONCLUSION

We highlight the applications of these databases in many fields of virtual screening, and attempt to forecast the importance of the natural product database in next-generation drug discovery.

ETM-DB: integrated Ethiopian traditional herbal medicine and phytochemicals database

Background

Recently, there has been an increasing tendency to go back to nature in search of new medicines. To facilitate this, a great deal of effort has been made to compile information on natural products worldwide, and as a result, many ethnic-based traditional medicine databases have been developed. In Ethiopia, there are more than 80 ethnic groups, each having their indigenous knowledge on the use of traditional medicine. About 80% of the population uses traditional medicine for primary health care. Despite this, there is no structured online database for Ethiopian traditional medicine, which limits natural products based drug discovery researches using natural products from this country.

Description

To develop ETM-DB, online research articles, theses, books, and public databases containing Ethiopian herbal medicine and phytochemicals information were searched. These resources were thoroughly inspected and the necessary data were extracted. Then, we developed a comprehensive online relational database which contains information on 1054 Ethiopian medicinal herbs with 1465 traditional therapeutic uses, 573 multi-herb prescriptions, 4285 compounds, 11,621 human target gene/proteins, covering 5779 herb-phenotype, 1879 prescription-herb, 16,426 herb-compound, 105,202 compound-phenotype, 162,632 compound-gene/protein, and 16,584 phenotype-gene/protein relationships. Using various cheminformatics tools, we obtained predicted physicochemical and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of ETM-DB compounds. We also evaluated drug-likeness properties of these compounds using FAF-Drugs4 webserver. From the 4285 compounds, 4080 of them passed the FAF-Drugs4 input data curation stage, of which 876 were found to have acceptable drug-likeness properties.

Conclusion

ETM-DB is the largest, freely accessible, web-based integrated resource on Ethiopian traditional medicine. It provides traditional herbal medicine entities and their relationships in well-structured forms including reference to the sources. The ETM-DB website interface allows users to search the entities using various options provided by the search menu. We hope that our database will expedite drug discovery and development researches from Ethiopian natural products as it contains information on the chemical composition and related human target gene/proteins. The current version of ETM-DB is openly accessible at http://biosoft.kaist.ac.kr/etm.

NPASS: natural product activity and species source database for natural product research, discovery and tool development

There has been renewed interests in the exploration of natural products (NPs) for drug discovery, and continuous investigations of the therapeutic claims and mechanisms of traditional and herbal medicines. In-silico methods have been employed for facilitating these studies. These studies and the optimization of in-silico algorithms for NP applications can be facilitated by the quantitative activity and species source data of the NPs. A number of databases collectively provide the structural and other information of ∼470 000 NPs, including qualitative activity information for many NPs, but only ∼4000 NPs are with the experimental activity values. There is a need for the activity and species source data of more NPs. We therefore developed a new database, NPASS (Natural Product Activity and Species Source) to complement other databases by providing the experimental activity values and species sources of 35 032 NPs from 25 041 species targeting 5863 targets (2946 proteins, 1352 microbial species and 1227 cell-lines). NPASS contains 446 552 quantitative activity records (e.g. IC50, Ki, EC50, GI50 or MIC mainly in units of nM) of 222 092 NP-target pairs and 288 002 NP-species pairs. NPASS, http://bidd2.nus.edu.sg/NPASS/, is freely accessible with its contents searchable by keywords, physicochemical property range, structural similarity, species and target search facilities.

NaPLeS: a natural products likeness scorer-web application and database

Natural products (NPs), often also referred to as secondary metabolites, are small molecules synthesised by living organisms. Natural products are of interest due to their bioactivity and in this context as starting points for the development of drugs and other bioactive synthetic products. In order to select compounds from virtual libraries, Ertl et al. developed a natural product likeness score which was later published as an open data, open source implementation. Here we present NaPLeS, an easily portable, containerised, open source web application based on open data to compute natural product likeness scores for chemical libraries.

A primer on natural product-based virtual screening

Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.

Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates

Background:

Determination or prediction of the Absorption, Distribution, Metabolism, and Excretion (ADME) properties of drug candidates and drug-induced toxicity plays crucial roles in drug discovery and development. Metabolism is one of the most complicated pharmacokinetic properties to be understood and predicted. However, experimental determination of the substrate binding, selectivity, sites and rates of metabolism is time- and recourse- consuming. In the phase I metabolism of foreign compounds (i.e., most of drugs), cytochrome P450 enzymes play a key role. To help develop drugs with proper ADME properties, computational models are highly desired to predict the ADME properties of drug candidates, particularly for drugs binding to cytochrome P450.

Objective:

This narrative review aims to briefly summarize machine learning techniques used in the prediction of the cytochrome P450 isoform specificity of drug candidates.

Results:

Both single-label and multi-label classification methods have demonstrated good performance on modelling and prediction of the isoform specificity of substrates based on their quantitative descriptors.

Conclusion:

This review provides a guide for researchers to develop machine learning-based methods to predict the cytochrome P450 isoform specificity of drug candidates.

An Apparent Binary Choice in Biochemistry: Mutual Reactivity Implies Life Chooses Thiols or Nitrogen-Sulfur Bonds, but Not Both

A fundamental goal of biology is to understand the rules behind life's use of chemical space. Established work focuses on why life uses the chemistry that it does. Given the enormous scope of possible chemical space, we postulate that it is equally important to ask why life largely avoids certain areas of chemical space. The nitrogen-sulfur bond is a prime example, as it rarely appears in natural molecules, despite the very rich N-S bond chemistry applied in various branches of industry (e.g., industrial materials, agrochemicals, pharmaceuticals). We find that, out of more than 200,000 known, unique compounds made by life, only about 100 contain N-S bonds. Furthermore, the limited number of N-S bond-containing molecules that life produces appears to fall into a few very distinctive structural groups. One may think that industrial processes are unrelated to biochemistry because of a greater possibility of solvents, catalysts, and temperatures available to industry than to the cellular environment. However, the fact that life does rarely make N-S bonds, from the plentiful precursors available, and has evolved the ability to do so independently several times, suggests that the restriction on life's use of N-S chemistry is not in its synthesis. We present a hypothesis to explain life's extremely limited usage of the N-S bond: that the N-S bond chemistry is incompatible with essential segments of biochemistry, specifically with thiols. We support our hypothesis by (1) a quantitative analysis of the occurrence of N-S bond-containing natural products and (2) reactivity experiments between selected N-S compounds and key biological molecules. This work provides an example of a reason why life nearly excludes a distinct region of chemical space. Combined with future examples, this potentially new field of research may provide fresh insight into life's evolution through chemical space and its origin and early evolution.

Modulation of Human Hsp90α Conformational Dynamics by Allosteric Ligand Interaction at the C-Terminal Domain

Recent years have seen heat shock protein 90 kDa (Hsp90) attract significant interest as a viable drug target, particularly for cancer. To date, designed inhibitors that target the ATPase domain demonstrate potent anti-proliferative effects, but have failed clinical trials due to high levels of associated toxicity. To circumvent this, the focus has shifted away from the ATPase domain. One option involves modulation of the protein through allosteric activation/inhibition. Here, we propose a novel approach: we use previously obtained information via residue perturbation scanning coupled with dynamic residue network analysis to identify allosteric drug targeting sites for inhibitor docking. We probe the open conformation of human Hsp90α for druggable sites that overlap with these allosteric control elements, and identify three putative natural compound allosteric modulators: Cephalostatin 17, 20(29)-Lupene-3β-isoferulate and 3'-Bromorubrolide F. We assess the allosteric potential of these ligands by examining their effect on the conformational dynamics of the protein. We find evidence for the selective allosteric activation and inhibition of Hsp90's conformational transition toward the closed state in response to ligand binding and shed valuable insight to further the understanding of allosteric drug design and Hsp90's complex allosteric mechanism of action.

NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules

Natural products (NPs) remain the most prolific resource for the development of small-molecule drugs. Here we report a new machine learning approach that allows the identification of natural products with high accuracy. The method also generates similarity maps, which highlight atoms that contribute significantly to the classification of small molecules as a natural product or synthetic molecule. The method can hence be utilized to (i) identify natural products in large molecular libraries, (ii) quantify the natural product-likeness of small molecules, and (iii) visualize atoms in small molecules that are characteristic of natural products or synthetic molecules. The models are based on random forest classifiers trained on data sets consisting of more than 265,000 to 322,000 natural products and synthetic molecules. Two-dimensional molecular descriptors, MACCS keys and Morgan2 fingerprints were explored. On an independent test set the models reached areas under the receiver operating characteristic curve (AUC) of 0.997 and Matthews correlation coefficients (MCCs) of 0.954 and higher. The method was further tested on data from the Dictionary of Natural Products, ChEMBL and other resources. The best-performing models are accessible as a free web service at http://npscout.zbh.uni-hamburg.de/npscout.

Discorhabdin N, a South African Natural Compound, for Hsp72 and Hsc70 Allosteric Modulation: Combined Study of Molecular Modeling and Dynamic Residue Network Analysis

The human heat shock proteins (Hsps), predominantly Hsp72 and Hsp90, have been strongly implicated in various critical stages of oncogenesis and progression of human cancers. While drug development has extensively focused on Hsp90 as a potential anticancer target, much less effort has been put against Hsp72. This work investigated the therapeutic potential of Hsp72 and its constitutive isoform, Hsc70, via in silico-based screening against the South African Natural Compounds Database (SANCDB). A comparative modeling approach was used to obtain nearly full-length 3D structures of the closed conformation of Hsp72 and Hsc70 proteins. Molecular docking of SANCDB compounds identified one potential allosteric modulator, Discorhabdin N, binding to the allosteric β substrate binding domain (SBDβ) back pocket, with good binding affinities in both cases. This allosteric region was identified in one of our previous studies. Subsequent all-atom molecular dynamics simulations and free energy calculations exhibited promising protein⁻ligand association characteristics, indicative of strong binding qualities. Further, we utilised dynamic residue network analysis (DRN) to highlight protein regions actively involved in cross-domain communication. Most residues identified agreed with known allosteric signal regulators from literature, and were further investigated for the purpose of deducing meaningful insights into the allosteric modulation properties of Discorhabdin N.

Resources for Chemical, Biological, and Structural Data on Natural Products

Natural products from plants, marine life, animals, fungi, bacteria, and other organisms remain the most productive source of inspiration for small-molecule drug discovery. Today, a wealth of information on natural products that is particularly valuable to applications in cheminformatics is at our disposal. In this contribution, we provide a timely overview of relevant resources for measured chemical, biological, and structural data on natural products. In particular, we comment on the accessibility, scope, chemical space, and limitations of the individual data sources. The bottleneck of natural products remains the limited availability of material for testing. In this context, we analyze the number of natural products readily obtainable from commercial and other sources.

Identification of Novel Potential Inhibitors of Pteridine Reductase 1 in Trypanosoma brucei via Computational Structure-Based Approaches and in Vitro Inhibition Assays

Pteridine reductase 1 (PTR1) is a trypanosomatid multifunctional enzyme that provides a mechanism for escape of dihydrofolate reductase (DHFR) inhibition. This is because PTR1 can reduce pterins and folates. Trypanosomes require folates and pterins for survival and are unable to synthesize them de novo. Currently there are no anti-folate based Human African Trypanosomiasis (HAT) chemotherapeutics in use. Thus, successful dual inhibition of Trypanosoma brucei dihydrofolate reductase (TbDHFR) and Trypanosoma brucei pteridine reductase 1 (TbPTR1) has implications in the exploitation of anti-folates. We carried out molecular docking of a ligand library of 5742 compounds against TbPTR1 and identified 18 compounds showing promising binding modes. The protein-ligand complexes were subjected to molecular dynamics to characterize their molecular interactions and energetics, followed by in vitro testing. In this study, we identified five compounds which showed low micromolar Trypanosome growth inhibition in in vitro experiments that might be acting by inhibition of TbPTR1. Compounds RUBi004, RUBi007, RUBi014, and RUBi018 displayed moderate to strong antagonism (mutual reduction in potency) when used in combination with the known TbDHFR inhibitor, WR99210. This gave an indication that the compounds might inhibit both TbPTR1 and TbDHFR. RUBi016 showed an additive effect in the isobologram assay. Overall, our results provide a basis for scaffold optimization for further studies in the development of HAT anti-folates.

SistematX, an Online Web-Based Cheminformatics Tool for Data Management of Secondary Metabolites

The traditional work of a natural products researcher consists in large part of time-consuming experimental work, collecting biota to prepare and analyze extracts and to identify innovative metabolites. However, along this long scientific path, much information is lost or restricted to a specific niche. The large amounts of data already produced and the science of metabolomics reveal new questions: Are these compounds known or new? How fast can this information be obtained? To answer these and other relevant questions, an appropriate procedure to correctly store information on the data retrieved from the discovered metabolites is necessary. The SistematX (http://sistematx.ufpb.br) interface is implemented considering the following aspects: (a) the ability to search by structure, SMILES (Simplified Molecular-Input Line-Entry System) code, compound name and species; (b) the ability to save chemical structures found by searching; (c) compound data results include important characteristics for natural products chemistry; and (d) the user can find specific information for taxonomic rank (from family to species) of the plant from which the compound was isolated, the searched-for molecule, and the bibliographic reference and Global Positioning System (GPS) coordinates. The SistematX homepage allows the user to log into the data management area using a login name and password and gain access to administration pages. In this article, we introduced a modern and innovative web interface for the management of a secondary metabolite database. With its multiplatform design, it is able to be properly consulted via the internet and managed from any accredited computer. The interface provided by SistematX contains a wealth of useful information for the scientific community about natural products, highlighting the locations of species from which compounds are isolated.