BRUSELAS: HPC Generic and Customizable Software Architecture for 3D Ligand-Based Virtual Screening of Large Molecular Databases

Antiviral Activity of Acetylsalicylic Acid against Bunyamwera Virus in Cell Culture

The Bunyavirales order is a large group of RNA viruses that includes important pathogens for humans, animals and plants. With high-throughput screening of clinically tested compounds we have looked for potential inhibitors of the endonuclease domain of a bunyavirus RNA polymerase. From a list of fifteen top candidates, five compounds were selected and their antiviral properties studied with Bunyamwera virus (BUNV), a prototypic bunyavirus widely used for studies about the biology of this group of viruses and to test antivirals. Four compounds (silibinin A, myricetin, L-phenylalanine and p-aminohippuric acid) showed no antiviral activity in BUNV-infected Vero cells. On the contrary, acetylsalicylic acid (ASA) efficiently inhibited BUNV infection with a half maximal inhibitory concentration (IC₅₀) of 2.02 mM. In cell culture supernatants, ASA reduced viral titer up to three logarithmic units. A significant dose-dependent reduction of the expression levels of Gc and N viral proteins was also measured. Immunofluorescence and confocal microscopy showed that ASA protects the Golgi complex from the characteristic BUNV-induced fragmentation in Vero cells. Electron microscopy showed that ASA inhibits the assembly of Golgi-associated BUNV spherules that are the replication organelles of bunyaviruses. As a consequence, the assembly of new viral particles is also significantly reduced. Considering its availability and low cost, the potential usability of ASA to treat bunyavirus infections deserves further investigation.

Docking and Molecular Dynamics Identify Leads against 5 Alpha Reductase 2 for Benign Prostate Hyperplasia Treatment

Steroid 5 alpha-reductase 2 (5αR-2) is a membrane-embedded protein that together with other isoforms plays a key role in the metabolism of steroids. This enzyme catalyzes the reduction of testosterone to the more potent ligand, dihydrotestosterone (DHT) in the prostate. Androgens, testosterone, and DHT play important roles in prostate growth, development, and function. At the same time, both testosterone and DHT have been implicated in the pathogenesis of benign prostate hyperplasia (BPH). Inhibition of the DHT formation, therefore, provides a therapeutic strategy that offers the possibility of preventing, delaying, or treating BPH. Currently, two steroidal drugs that inhibit 5αR-2, dutasteride and finasteride, have been approved for clinical use. These two come at a high cost and also portray undesirable sexual side effects which necessitate the need to find new chemotherapeutic alternatives for the disease. Based on the aforementioned, finasteride and dutasteride were subjected to scaffold hopping, fragment-based de novo design, molecular docking, and molecular dynamics simulations employing databases like ChEMBL, DrugBank, PubChem, ChemSpider, and Zinc15 in the identification of potential hits targeting 5αR-2. Altogether, ten novel compounds targeting 5αR-2 were identified with binding energies lower or comparable to finasteride and dutasteride, the main inhibitors for this target. Molecular docking and molecular dynamics simulations studies identify amino acid residues Glu57, Phe219, Phe223, and Leu224 to be critical for ligand binding and complex stability. The physicochemical and pharmacological profiling suggests the potential of the hit compounds to be drug-like and orally active. Similarly, the quality parameter assessments revealed the hits possess LELP greater than 3 implying their promise as lead-like molecules. The compounds A5, A9, and A10 were, respectively, predicted to treat prostate disorders with Pa (0.188, 0.361, and 0.270) and Pi (0.176, 0.050, and 0.093), while A8 and A9 were found to be associated with BPH treatment with Pa (0.09 and 0.127) and Pi (0.077 and 0.033), respectively. Structural similarity searches via DrugBank identified the drugs faropenem, acemetacin, estradiol valerate, and yohimbine to be useful for BPH treatment suggesting the de novo designed ligands as potential chemotherapeutic agents for treating this disease.

Improving drug discovery through parallelism

Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go a step further and propose its parallelization. Specifically, we propose a two-layer parallelization. Firstly, an automation of the molecule distribution process between the available nodes in a cluster, and secondly, a parallelization of the internal methods (initialization, reproduction, selection and optimization). This new software, called pOptiPharm, aims to improve the quality of predictions and reduce experimentation time. As the results show, the performance of the proposed methods is good. It can find better solutions than the sequential OptiPharm, all while reducing its computation time almost proportionally to the number of processing units considered.

Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern

INTRODUCTION

Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed.

AREAS COVERED

One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future.

EXPERT OPINION

Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.

Homology Modeling, de Novo Design of Ligands, and Molecular Docking Identify Potential Inhibitors of Leishmania donovani 24-Sterol Methyltransferase

The therapeutic challenges pertaining to leishmaniasis due to reported chemoresistance and toxicity necessitate the need to explore novel pathways to identify plausible inhibitory molecules. Leishmania donovani 24-sterol methyltransferase (LdSMT) is vital for the synthesis of ergosterols, the main constituents of Leishmania cellular membranes. So far, mammals have not been shown to possess SMT or ergosterols, making the pathway a prime candidate for drug discovery. The structural model of LdSMT was elucidated using homology modeling to identify potential novel 24-SMT inhibitors via virtual screening, scaffold hopping, and de-novo fragment-based design. Altogether, six potential novel inhibitors were identified with binding energies ranging from −7.0 to −8.4 kcal/mol with e-LEA3D using 22,26-azasterol and S1–S4 obtained from scaffold hopping via the ChEMBL, DrugBank, PubChem, ChemSpider, and ZINC15 databases. These ligands showed comparable binding energy to 22,26-azasterol (−7.6 kcal/mol), the main inhibitor of LdSMT. Moreover, all the compounds had plausible ligand efficiency-dependent lipophilicity (LELP) scores above 3. The binding mechanism identified Tyr92 to be critical for binding, and this was corroborated via molecular dynamics simulations and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) calculations. The ligand A1 was predicted to possess antileishmanial properties with a probability of activity (Pa) of 0.362 and a probability of inactivity (Pi) of 0.066, while A5 and A6 possessed dermatological properties with Pa values of 0.205 and 0.249 and Pi values of 0.162 and 0.120, respectively. Structural similarity search via DrugBank identified vabicaserin, daledalin, zanapezil, imipramine, and cefradine with antileishmanial properties suggesting that the de-novo compounds could be explored as potential antileishmanial agents.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Discovery of potent inhibitors for SARS-CoV-2's main protease by ligand-based/structure-based virtual screening, MD simulations, and binding energy calculations

COVID-19 has caused lockdowns all over the world in early 2020, as a global pandemic. Both theoretical and experimental efforts are seeking to find an effective treatment to suppress the virus. In silico drug design can play a vital role in identifying promising drug candidates against COVID-19. Herein, we focused on the main protease of SARS-CoV-2 that has crucial biological functions in the virus. We performed a ligand-based virtual screening followed by a docking screening for testing approved drugs and bioactive compounds listed in the DrugBank and ChEMBL databases. The top 8 docking results were advanced to all-atom MD simulations to study the relative stability of the protein-ligand interactions. MD simulations support that the catalytic residue, His41, has a neutral side chain with a protonated delta position. An absolute binding energy (ΔG) of -42 kJ mol-1 for the protein-ligand (Mpro-N3) complex has been calculated using the potential-of-mean-force (geometrical) approach. Furthermore, the relative binding energies were computed for the top docking results. Our results suggest several promising approved and bioactive inhibitors of SARS-CoV-2 Mpro as follows: a bioactive compound, ChEMBL275592, which has the best MM/GBSA binding energy; the second-best compound, montelukast, is an approved drug used in the treatment of asthma and allergic rhinitis; the third-best compound, ChEMBL288347, is a bioactive compound. Bromocriptine and saquinavir are other approved drugs that also demonstrate stability in the active site of Mpro, albeit their relative binding energies are low compared to the N3 inhibitor. This study provides useful insights into de novo protein design and novel inhibitor development, which could reduce the cost and time required for the discovery of a potent drug to combat SARS-CoV-2.

NMR-Based Chemical Profiling, Isolation and Evaluation of the Cytotoxic Potential of the Diterpenoid Siderol from Cultivated Sideritis euboea Heldr

Diterpenes are characteristic compounds from the genus Sideritis L., possessing an array of biological activities. Siderol is the main constituent of the ent-kaurene diterpenes in Sideritis species. In order to isolate the specific compound and evaluate for the first time its cytotoxic activity, we explored the dichloromethane extract of cultivated Sideritis euboea Heldr. To track the specific natural bioactive agent, we applied NMR spectroscopy to the crude plant extract, since NMR can serve as a powerful and rapid tool both to navigate the targeted isolation process of bioactive constituents, and to also reveal the identity of bioactive components. Along these lines, from the rapid 1D 1H NMR spectrum of the total crude plant extract, we were able to determine the characteristic proton NMR signals of siderol. Furthermore, with the same NMR spectrum, we were able to categorize several secondary metabolites into chemical groups as a control of the isolation process. Therefore, this non-polar extract was explored, for the first time, revealing eleven compounds-one fatty acid ester; 2-(p-hydroxyphenyl)ethylstearate (1), three phytosterols; β-sitosterol (2), stigmasterol (3), and campesterol (4); one triterpenoid; ursolic acid (5), four diterpenoids; siderol (6), eubol (7), eubotriol (8), 7-epicandicandiol (9) and two flavonoids; xanthomicrol (10) and penduletin (11). The main isolated constituent was siderol. The antiproliferative potential of siderol was evaluated, using the MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) assay, on three human cancer cell lines DLD1, HeLa, and A549, where the IC50 values were estimated at 26.4 ± 3.7, 44.7 ± 7.2, and 46.0 ± 4.9 μΜ, respectively. The most potent activity was recorded in the human colon cancer cell line DLD1, where siderol exhibited the lowest IC50. Our study unveiled the beneficial potential of siderol as a remarkable cytotoxic agent and the significant contribution of NMR spectroscopy towards the isolation and identification of this potent anticancer agent.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.