MedPServer: A database for identification of therapeutic targets and novel leads pertaining to natural products

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.

Model nematodes as a practical innovation to promote high throughput screening of natural products for anthelmintics discovery in South Asia: Current challenges, proposed practical and conceptual solutions

With the increasing prevalence of anthelmintic resistance in animals recorded globally, and the threat of resistance in human helminths, the need for novel anthelmintic drugs is greater than ever. Most research aimed at discovering novel anthelmintic leads relies on high throughput screening (HTS) of large libraries of synthetic small molecules in industrial and academic settings in developed countries, even though it is the tropical countries that are most plagued by helminth infections. Tropical countries, however, have the advantage of possessing a rich flora that may yield natural products (NP) with promising anthelmintic activity. Focusing on South Asia, which produces one of the world's highest research outputs in NP and NP-based anthelmintic discovery, we find that limited basic research and funding, a lack of awareness of the utility of model organisms, poor industry-academia partnerships and lack of technological innovations greatly limit anthelmintics research in the region. Here we propose that utilizing model organisms including the free-living nematode Caenorhabditis elegans, that can potentially allow rapid target identification of novel anthelmintics, and Oscheius tipulae, a closely related, free-living nematode which is found abundantly in soil in hotter temperatures, could be a much-needed innovation that can enable cost-effective and efficient HTS of NPs for discovering compounds with anthelmintic/antiparasitic potential in South Asia and other tropical regions that historically have devoted limited funding for such research. Additionally, increased collaborations at the national, regional and international level between parasitologists and pharmacologists/ethnobotanists, setting up government-industry-academia partnerships to fund academic research, creating a centralized, regional collection of plant extracts or purified NPs as a dereplication strategy and HTS library, and holding regional C. elegans/O. tipulae-based anthelmintics workshops and conferences to share knowledge and resources regarding model organisms may collectively promote and foster a NP-based anthelmintics landscape in South Asia and beyond.

Navigating the Chemical Space and Chemical Multiverse of a Unified Latin American Natural Product Database: LANaPDB

The number of databases of natural products (NPs) has increased substantially. Latin America is extraordinarily rich in biodiversity, enabling the identification of novel NPs, which has encouraged both the development of databases and the implementation of those that are being created or are under development. In a collective effort from several Latin American countries, herein we introduce the first version of the Latin American Natural Products Database (LANaPDB), a public compound collection that gathers the chemical information of NPs contained in diverse databases from this geographical region. The current version of LANaPDB unifies the information from six countries and contains 12,959 chemical structures. The structural classification showed that the most abundant compounds are the terpenoids (63.2%), phenylpropanoids (18%) and alkaloids (11.8%). From the analysis of the distribution of properties of pharmaceutical interest, it was observed that many LANaPDB compounds satisfy some drug-like rules of thumb for physicochemical properties. The concept of the chemical multiverse was employed to generate multiple chemical spaces from two different fingerprints and two dimensionality reduction techniques. Comparing LANaPDB with FDA-approved drugs and the major open-access repository of NPs, COCONUT, it was concluded that the chemical space covered by LANaPDB completely overlaps with COCONUT and, in some regions, with FDA-approved drugs. LANaPDB will be updated, adding more compounds from each database, plus the addition of databases from other Latin American countries.

New approaches in developing medicinal herbs databases

Medicinal herbs databases have become a crucial part of organizing new scientific literature generated in medicinal herbs field, as well as new drug discoveries in the information era. The aim of this review was to track the current status of medicinal herbs databases. Search for finding medicinal herbs databases was carried out via Google and PubMed. PubMed was searched for papers introducing medicinal herbs databases by the recruited search strategy. Papers with an active database on the web were included in the review. Google was also searched for medicinal herbs databases. Both retrieved papers and databases were reviewed by the authors. In this review, the current status of 25 medicinal herbs databases was reviewed, and the important characteristics of databases were mentioned. The reviewed databases had a great variety in terms of characteristics and functions. Finally, some recommendations for the efficient development of medicinal herbs databases were suggested. Although contemporary medicinal herbs databases represent much useful information, adding some features to these databases could assist them to have better functionality. This work may not cover all the necessary information, but we hope that our review can provide readers with fundamental concepts, perspectives and suggestions for constructing more useful databases.

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.

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.

The Combination of Tradition and Future: Data-Driven Natural-Product-Based Treatments for Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder in elderly people. The personalized diagnosis and treatment remain challenges all over the world. In recent years, natural products are becoming potential therapies for many complex diseases due to their stability and low drug resistance. With the development of informatics technologies, data-driven natural product discovery and healthcare is becoming reality. For PD, however, the relevant research and tools for natural products are quite limited. Here in this review, we summarize current available databases, tools, and models for general natural product discovery and synthesis. These useful resources could be used and integrated for future PD-specific natural product investigations. At the same time, the challenges and opportunities for future natural-product-based PD care will also be discussed.

An in silico approach to identify potential medicinal plants for treating Alzheimer disease: a case study with acetylcholinesterase

Alzheimer's disease (AD) is a progressive neurological disorder affecting an estimated 10 million people worldwide. There is no cure for AD, and only a handful of drugs are known to provide some relief of the symptoms. The prescription drug donepezil has been widely used to treat to slow the progression and onset of the disease; however, the unpleasant side effects have paved the way to find alternative medicines. Many herbs are known to improve brain function, but evidence of medicinal plants that can treat AD is limited due to the lack of concrete rational evidences. Moreover, the traditional method of randomly screening plant extract against AD targets takes time and resources. In this study, a receptor-based in silico method has been implemented which serves to accelerate the process of identification of medicinal plants useful for treatment of AD. A database of natural compounds was compiled to identify hits against acetylcholinesterase (AChE). Receptor-based pharmacophore screening was performed, and selected hits were subjected to docking and molecular dynamics simulations. Molecular Mechanics/Generalized Born surface area (MM/GBSA) calculations were carried out to identify the best scoring hits further. In vitro assays were done for the plant extracts containing the top-scoring hits against AChE. Three plant extracts showed favorable inhibitory activity.Communicated by Ramaswamy H. Sarma.

Scope of 3D Shape-Based Approaches in Predicting the Macromolecular Targets of Structurally Complex Small Molecules Including Natural Products and Macrocyclic Ligands

A plethora of similarity-based, network-based, machine learning, docking and hybrid approaches for predicting the macromolecular targets of small molecules are available today and recognized as valuable tools for providing guidance in early drug discovery. With the increasing maturity of target prediction methods, researchers have started to explore ways to expand their scope to more challenging molecules such as structurally complex natural products and macrocyclic small molecules. In this work, we systematically explore the capacity of an alignment-based approach to identify the targets of structurally complex small molecules (including large and flexible natural products and macrocyclic compounds) based on the similarity of their 3D molecular shape to noncomplex molecules (i.e., more conventional, “drug-like”, synthetic compounds). For this analysis, query sets of 10 representative, structurally complex molecules were compiled for each of the 28 pharmaceutically relevant proteins. Subsequently, ROCS, a leading shape-based screening engine, was utilized to generate rank-ordered lists of the potential targets of the 28 × 10 queries according to the similarity of their 3D molecular shapes with those of compounds from a knowledge base of 272 640 noncomplex small molecules active on a total of 3642 different proteins. Four of the scores implemented in ROCS were explored for target ranking, with the TanimotoCombo score consistently outperforming all others. The score successfully recovered the targets of 30% and 41% of the 280 queries among the top-5 and top-20 positions, respectively. For 24 out of the 28 investigated targets (86%), the method correctly assigned the first rank (out of 3642) to the target of interest for at least one of the 10 queries. The shape-based target prediction approach showed remarkable robustness, with good success rates obtained even for compounds that are clearly distinct from any of the ligands present in the knowledge base. However, complex natural products and macrocyclic compounds proved to be challenging even with this approach, although cases of complete failure were recorded only for a small number of targets.

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.

Discovery of sulfone-resistant dihydropteroate synthase (DHPS) as a target enzyme for kaempferol, a natural flavanoid

Kaempferol is a ubiquitous flavonoid, found in various plants having a wide range of known pharmacological activities, including antioxidant, antiinflammatory, anticancer, antiallergic, antidiabetic, neuroprotective, cardioprotective and antimicrobial activities. Nonetheless various evidence suggest that kaempferol is also able to interact with many unknown therapeutic targets modulating signalling pathways, thus providing an opportunity to explore the potential target space of kaempferol. In this study, we have employed various ligand-based approaches to identify the potential targets of kaempferol, followed by validations using modelling and docking studies. Molecular dynamics, free energy calculations, volume and residue contact map analyses were made to delineate the cause of drug-resistance among mutants. We have discovered dihydropteroate synthase (DHPS) as a novel potential therapeutic target for kaempferol. Further studies employing molecular dynamics simulations and binding free energies indicate that kaempferol has potential to inhibit even the sulfone-resistant DHPS mutants, which makes it a very attractive antibiotic agent. The identification of natural-product based kaempferol opens up the door for the design of antibiotics in a quick and high throughput fashion for identifying antibiotic leads.