Fragment Library of Natural Products and Compound Databases for Drug Discovery

In silico construction of a focused fragment library facilitating exploration of chemical space

Fragment-based drug design (FBDD) has emerged as a captivating subject in the realm of computer-aided drug design, enabling the generation of novel molecules through the rearrangement of ring systems within known compounds. The construction of focused fragment library plays a pivotal role in FBDD, necessitating the compilation of all potential bioactive ring systems capable of interacting with a specific target. In our study, we propose a workflow for the development of a focused fragment library and combinatorial compound library. The fragment library comprises seed fragments and collected fragments. The extraction of seed fragments is guided by receptor information, serving as a prerequisite for establishing a focused libraries. Conversely, collected fragments are obtained using the feature graph method, which offers a simplified representation of fragments and strikes a balance between diversity and similarity when categorizing different fragments. The utilization of feature graph facilitates the rational partitioning of chemical space at fragment level, enabling the exploration of desired chemical space and enhancing the efficiency of screening compound library. Analysis demonstrates that our workflow enables the enumeration of a greater number of entirely new potential compounds, thereby aiding in the rational design of drugs.

Chemical Multiverse and Diversity of Food Chemicals

Food chemicals have a fundamental role in our lives, with an extended impact on nutrition, disease prevention, and marked economic implications in the food industry. The number of food chemical compounds in public databases has substantially increased in the past few years, which can be characterized using chemoinformatics approaches. We and other groups explored public food chemical libraries containing up to 26,500 compounds. This study aimed to analyze the chemical contents, diversity, and coverage in the chemical space of food chemicals and additives and, from here on, food components. The approach to food components addressed in this study is a public database with more than 70,000 compounds, including those predicted via omics techniques. It was concluded that food components have distinctive physicochemical properties and constitutional descriptors despite sharing many chemical structures with natural products. Food components, on average, have large molecular weights and several apolar structures with saturated hydrocarbons. Compared to reference databases, food component structures have low scaffold and fingerprint-based diversity and high structural complexity, as measured by the fraction of sp3 carbons. These structural features are associated with a large fraction of macronutrients as lipids. Lipids in food components were decompiled by an analysis of the maximum common substructures. The chemical multiverse representation of food chemicals showed a larger coverage of chemical space than natural products and FDA-approved drugs by using different sets of representations.

Mechanisms of Nelumbinis folium targeting PPARγ for weight management: A molecular docking and molecular dynamics simulations study

The lotus leaf, Nelumbinis folium (NF), has frequently appeared in obesity clinical trials as an intervention to promote weight loss and improve metabolic profiles. However, the molecular mechanisms by which it interacts with important obesity targets and pathways, such as the peroxisome proliferator-activated receptor gamma (PPARγ) within the PPAR signalling pathway, were not well understood. This study aims to screen for candidate compounds from NF with desirable pharmacokinetic properties and examine their binding feasibility at the PPARγ ligand-binding domain (LBD). Ligand- and structure-based screening of NF compounds were performed, and a consensus approach has been applied to identify druggable candidates. By examining the pharmacokinetic profiles, a large proportion of NF compounds exhibited favourable drug-likeness and oral bioavailability properties. Furthermore, the binding affinity scores and poses provided new insights on the distinctive binding behaviours of NF compounds at the LBD of PPARγ in its inactive form. Several NF compounds could bind strongly to PPARγ at sub-pockets where partial agonists and antagonists were found to bind and may induce conformational changes that influence co-repressor binding, trans-repression, and gene expression inhibition. Subsequent molecular dynamics simulations of a candidate compound (NF129 narcissin) bound to PPARγ revealed conformational stability, residue fluctuation, and binding behaviours comparable to that of the known inhibitor, SR1664. Therefore, it can be proposed that narcissin exhibits characteristics of a PPARγ antagonist. Further experimental validation to support the development of NF129 as a future anti-obesity agent is warranted.

Natural photosensitizers potentiate the targeted antimicrobial photodynamic therapy as the Monkeypox virus entry inhibitors: An in silico approach

BACKGROUND

Monkeypox is a viral zoonotic disease that has emerged as a threat to public health. Currently, there is no treatment approved specifically targeting Monkeypox disease. Hence, it is essential to identify and develop therapeutic approaches to the Monkeypox virus. In the current in silico paper, we comprehensively involve using computer simulations and modeling to insights and predict hypotheses on the potential of natural photosensitizers-mediated targeted antimicrobial photodynamic therapy (aPDT) against D8L as a Monkeypox virus protein involved in viral cell entry.

MATERIALS AND METHODS

In the current study, computational techniques such as molecular docking were combined with in silico ADMET predictions to examine how Curcumin (Cur), Quercetin (Qct), and Riboflavin (Rib) as the natural photosensitizers bind to the D8L protein in Monkeypox virus, as well as to determine pharmacokinetic properties of these photosensitizers.

RESULTS

The three-dimensional structure of the D8L protein in the Monkeypox virus was constructed using homology modeling (PDB ID: 4E9O). According to the physicochemical properties and functional characterization, 4E9O was a stable protein with the nature of a hydrophilic structure. The docking studies employing a three-dimensional model of 4E9O with natural photosensitizers exhibited good binding affinity. D8L protein illustrated the best docking score (-7.6 kcal/mol) in relation to the Rib and displayed good docking scores in relation to the Cur (-7.0 kcal/mol) and Qct (-7.5 kcal/mol).

CONCLUSIONS

The findings revealed that all three photosensitizers were found to obey the criteria of Lipinski's rule of five and displayed drug-likeness. Moreover, all the tested photosensitizers were found to be non-hepatotoxic and non-cytotoxic. In summary, our investigation identified Cur, Qct, and Rib could efficiently interact with D8L protein with a strong binding affinity. It can be concluded that aPDT using these natural photosensitizers may be considered an adjuvant treatment against Monkeypox disease.

A Scaffold-based Deep Generative Model Considering Molecular Stereochemical Information

Designing molecules with specific scaffolds can facilitate the discovery and optimization of lead compounds. Some scaffold-based molecular generation models have been developed using deep-learning methods based on specific scaffolds, although incorporating scaffold generalization is expected to achieve scaffold hopping. Moreover, most of the existing models focus on the 2D shape of the scaffold and overlook the stereochemical properties of the compound, especially for natural products. In this study, we optimized the scaffold-based molecular generation model designed by Lim et al. (Chemical Science 2020, 11, 1153-1164). Real-time ultrafast shape recognition with pharmacophore constraints (USRCAT) was introduced into the model to search for molecules similar to the 3D conformation and pharmacophore of the input scaffold sourced from the training set; the searched molecules were then used as new scaffolds to execute scaffold hopping. The optimized model could generate new molecules with the same chirality as the input scaffold. Furthermore, the probability distribution of the molecular structure and various physicochemical properties were analyzed to evaluate the model's generation capability. We thus believe that the optimized model can provide a basis for medicinal chemists to explore a wider chemical space toward optimization of the lead compounds and to screen the virtual compound library.

Progress and Impact of Latin American Natural Product Databases

Natural products (NPs) are a rich source of structurally novel molecules, and the chemical space they encompass is far from being fully explored. Over history, NPs have represented a significant source of bioactive molecules and have served as a source of inspiration for developing many drugs on the market. On the other hand, computer-aided drug design (CADD) has contributed to drug discovery research, mitigating costs and time. In this sense, compound databases represent a fundamental element of CADD. This work reviews the progress toward developing compound databases of natural origin, and it surveys computational methods, emphasizing chemoinformatic approaches to profile natural product databases. Furthermore, it reviews the present state of the art in developing Latin American NP databases and their practical applications to the drug discovery area.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

An in silico pipeline for the discovery of multitarget ligands: A case study for epi-polypharmacology based on DNMT1/HDAC2 inhibition

The search for novel therapeutic compounds remains an overwhelming task owing to the time-consuming and expensive nature of the drug development process and low success rates. Traditional methodologies that rely on the one drug-one target paradigm have proven insufficient for the treatment of multifactorial diseases, leading to a shift to multitarget approaches. In this emerging paradigm, molecules with off-target and promiscuous interactions may result in preferred therapies. In this study, we developed a general pipeline combining machine learning algorithms and a deep generator network to train a dual inhibitor classifier capable of identifying putative pharmacophoric traits. As a case study, we focused on dual inhibitors targeting DNA methyltransferase 1 (DNMT) and histone deacetylase 2 (HDAC2), two enzymes that play a central role in epigenetic regulation. We used this approach to identify dual inhibitors from a novel large natural product database in the public domain. We used docking and atomistic simulations as complementary approaches to establish the ligand-interaction profiles between the best hits and DNMT1/HDAC2. By using the combined ligand- and structure-based approaches, we discovered two promising novel scaffolds that can be used to simultaneously target both DNMT1 and HDAC2. We conclude that the flexibility and adaptability of the proposed pipeline has predictive capabilities of similar or derivative methods and is readily applicable to the discovery of small molecules targeting many other therapeutically relevant proteins.

Towards the De Novo Design of HIV-1 Protease Inhibitors Based on Natural Products

Acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are a way to control HIV infection but not to cure AIDS. As such, effective treatment must be developed to control AIDS. Developing a drug is not an easy task, and there is an enormous amount of work and economic resources invested. For this reason, it is highly convenient to employ computer-aided drug design methods, which can help generate and identify novel molecules. Using the de novo design, novel molecules can be developed using fragments as building blocks. In this work, we develop a virtual focused compound library of HIV-1 viral protease inhibitors from natural product fragments. Natural products are characterized by a large diversity of functional groups, many sp³ atoms, and chiral centers. Pseudo-natural products are a combination of natural products fragments that keep the desired structural characteristics from different natural products. An interactive version of chemical space visualization of virtual compounds focused on HIV-1 viral protease inhibitors from natural product fragments is freely available in the supplementary material.

Advances in the Exploration of the Epigenetic Relevant Chemical Space

Epigenetic drug discovery is a promising avenue to find therapeutic agents for treating several diseases and developing novel chemical probes for research. In order to identify hit and lead compounds, the chemical space has been explored and screened, generating valuable bioactivity information that can be used for multiple purposes such as prediction of the activity of existing chemicals, e.g., small molecules, guiding the design or optimization of compounds, and expanding the epigenetic relevant chemical space. Herein, we review the chemical spaces explored for epigenetic drug discovery and discuss the advances in using structure-activity relationships stored in public chemogenomic databases. We also review current efforts to chart and identify novel regions of the epigenetic relevant chemical space. In particular, we discuss the development and accessibility of two significant types of compound libraries focused on epigenetic targets: commercially available libraries for screening and targeted chemical libraries using de novo design. In this mini-review, we emphasize inhibitors of DNA methyltransferases.

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.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Latin American databases of natural products: biodiversity and drug discovery against SARS-CoV-2

In this study, we evaluated 3444 Latin American natural products using cheminformatic tools. We also characterized 196 compounds for the first time from the flora of El Salvador that were compared with the databases of secondary metabolites from Brazil, Mexico, and Panama, and 42 969 compounds (natural, semi-synthetic, synthetic) from different regions of the world. The overall analysis was performed using drug-likeness properties, molecular fingerprints of different designs, two parameters similarity, molecular scaffolds, and molecular complexity metrics. It was found that, in general, Salvadoran natural products have a large diversity based on fingerprints. Simultaneously, those belonging to Mexico and Panama present the greatest diversity of scaffolds compared to the other databases. This study provided evidence of the high structural complexity that Latin America's natural products have as a benchmark. The COVID-19 pandemic has had a negative effect on a global level. Thus, in the search for substances that may influence the coronavirus life cycle, the secondary metabolites from El Salvador and Panama were evaluated by docking against the endoribonuclease NSP-15, an enzyme involved in the SARS CoV-2 viral replication. We propose in this study three natural products as potential inhibitors of NSP-15.

Harnessing the natural pool of polyketide and non-ribosomal peptide family: A route map towards novel drug development

Emergence of communicable and non-communicable diseases possess health challenge to millions of people worldwide and is a major threat to the economic and social development in the coming century. The occurrence of recent pandemic, SARS-CoV-2 caused by lethal severe acute respiratory syndrome coronavirus 2 is one such example. Rapid research and development of drugs for the treatment and management of these diseases has been an incredibly challenging task for the pharmaceutical industry. Although, substantial focus has been made in the discovery of therapeutic compounds from natural sources having significant medicinal potential, their synthesis has shown a slow progress. Hence, the discovery of new targets by the application of the latest biotechnological and synthetic biology approaches is very much the need of the hour. Polyketides (PKs) and non-ribosomal peptides (NRPs) found in bacteria, fungi and plants are a large diverse family of natural products synthesized by two classes of enzymes: polyketide synthases (PKS) and non-ribosomal peptide synthetases (NRPS). These enzymes possess immense biomedical potential due to their simple architecture, catalytic capacity, as well as diversity. With the advent of latest in-silico and in-vitro strategies, these enzymes and their related metabolic pathways, if targeted, can contribute highly towards the biosynthesis of an array of potentially natural drug leads that have antagonist effects on biopolymers associated with various human diseases. In the face of the rising threat from the multidrug-resistant pathogens, this will further open new avenues for the discovery of novel and improved drugs by combining the natural and the synthetic approaches. This review discusses the relevance of polyketides and non-ribosomal peptides and the improvement strategies for the development of their derivatives and scaffolds, and how they will be beneficial to the future bioprospecting and drug discovery.

Natural and Nature-Derived Products Targeting Human Coronaviruses

The ongoing pandemic of severe acute respiratory syndrome (SARS), caused by the SARS-CoV-2 human coronavirus (HCoV), has brought the international scientific community before a state of emergency that needs to be addressed with intensive research for the discovery of pharmacological agents with antiviral activity. Potential antiviral natural products (NPs) have been discovered from plants of the global biodiversity, including extracts, compounds and categories of compounds with activity against several viruses of the respiratory tract such as HCoVs. However, the scarcity of natural products (NPs) and small-molecules (SMs) used as antiviral agents, especially for HCoVs, is notable. This is a review of 203 publications, which were selected using PubMed/MEDLINE, Web of Science, Scopus, and Google Scholar, evaluates the available literature since the discovery of the first human coronavirus in the 1960s; it summarizes important aspects of structure, function, and therapeutic targeting of HCoVs as well as NPs (19 total plant extracts and 204 isolated or semi-synthesized pure compounds) with anti-HCoV activity targeting viral and non-viral proteins, while focusing on the advances on the discovery of NPs with anti-SARS-CoV-2 activity, and providing a critical perspective.

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 .