Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors

Glycogen synthase kinase-3β (GSK3β) is associated with various key biological processes, and it has been considered as a critical therapeutic target for the treatment of many diseases. However, it is a big challenge to develop ATP-competition GSK3β inhibitors because of the high sequence homology with other kinases. In this work, a novel parallel virtual screening strategy based on multiple GSK3β protein structures, integrating molecular docking, complex-based pharmacophore, and naive Bayesian classification, was developed to screen a large chemical database, the 50 compounds with top-scores then underwent a luminescent kinase assay, which led to the discovery of two GSK3β inhibitor hits. The high screening enrichment rate indicates the reliability and practicability of the integrated protocol. Finally, molecular docking and molecular dynamics simulation were employed to investigate the binding modes of the GSK3β inhibitors, and some “hot residues” critical to GSK3β affinity were highlighted. The present study may provide some valuable guidance for the development of novel GSK3β inhibitors.

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.

Pharmacophore based virtual screening, molecular docking, molecular dynamics and MM-GBSA approach for identification of prospective SARS-CoV-2 inhibitor from natural product databases

COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily appeared in Wuhan, China, in December 2019. At present, no proper therapy and vaccinations are available for the disease, and it is increasing day by day with a high mortality rate. Pharmacophore based virtual screening of the selected natural product databases followed by Glide molecular docking and dynamics studies against SARS-CoV-2 main protease was investigated to identify potential ligands that may act as inhibitors. The molecules SN00293542 and SN00382835 revealed the highest docking score of −14.57 and −12.42 kcal/mol, respectively, when compared with the co-crystal ligands of PDB-6Y2F (O6K) and 6W63 (X77) of the SARS-CoV-2 M^pro. To further validate the interactions of top scored molecules SN00293542 and SN00382835, molecular dynamics study of 100 ns was carried out. This indicated that the protein-ligand complex was stable throughout the simulation period, and minimal backbone fluctuations have ensued in the system. Post-MM-GBSA analysis of molecular dynamics data showed free binding energy-71.7004 +/− 7.98, −56.81+/− 7.54 kcal/mol, respectively. The computational study identified several ligands that may act as potential inhibitors of SARS-CoV-2 M^pro. The top-ranked molecules SN00293542, and SN00382835 occupied the active site of the target, the main protease like that of the co-crystal ligand. These molecules may emerge as a promising ligands against SARS-CoV-2 and thus needs further detailed investigations.

Communicated by Ramaswamy H. Sarma

In-silico Studies and Biological Activity of Potential BACE-1 Inhibitors

BACKGROUND

Alzheimer's disease is a neurological condition causing cognitive inability and dementia. The pathological lesions and neuronal damage in the brain are caused by self-aggregated fragments of mutated Amyloidal precursor protein (APP).

OBJECTIVE

The controlled APP processing by inhibition of secretase is the strategy to reduce Aβ load to treat Alzheimer's disease.

METHODS

A QSAR study was performed on 55 Pyrrolidine based ligands as BACE-1 inhibitors with an activity magnitude greater than 4 of compounds.

RESULTS

In the advent of designing new BACE-1 inhibitors, the pharmacophore model with correlation (r = 0.90) and root mean square deviation (RMSD) of 0.87 was developed and validated. Further, the hits retrieved by the in-silico approach were evaluated by docking interactions.

CONCLUSION

Two structurally diverse compounds exhibited Asp32 and Thr232 binding with the BACE-1 receptor. The aryl-substituted carbamate compound exhibited the highest fit value and docking score. The biological activity evaluation by in-vitro assay was found to be >0.1μM.

Targeting SARS-CoV-2 RBD Interface: a Supervised Computational Data-Driven Approach to Identify Potential Modulators

Coronavirus disease 2019 (COVID-19) has spread out as a pandemic threat affecting over 2 million people. The infectious process initiates via binding of SARS-CoV-2 Spike (S) glycoprotein to host angiotensin-converting enzyme 2 (ACE2). The interaction is mediated by the receptor-binding domain (RBD) of S glycoprotein, promoting host receptor recognition and binding to ACE2 peptidase domain (PD), thus representing a promising target for therapeutic intervention. Herein, we present a computational study aimed at identifying small molecules potentially able to target RBD. Although targeting PPI remains a challenge in drug discovery, our investigation highlights that interaction between SARS-CoV-2 RBD and ACE2 PD might be prone to small molecule modulation, due to the hydrophilic nature of the bi-molecular recognition process and the presence of druggable hot spots. The fundamental objective is to identify, and provide to the international scientific community, hit molecules potentially suitable to enter the drug discovery process, preclinical validation and development.

Antidepressant Drug Design on TCAs and Phenoxyphenylpropylamines utilizing QSAR and Pharmacophore Modeling

BACKGROUND

Depression is a mental illness caused by the imbalance of important neurotransmitters such as serotonin (5-HT) and norepinephrine (NE). It is a serious neurological disorder that could be treated by antidepressant drugs.

OBJECTIVE

There are two major classes such as TCAs and phenoxyphenylpropylamines which have been proven to be broad-spectrum antidepressant compounds. Several attempts were made to design, synthesize and discover potent antidepressant compounds having the least toxicity and most selectivity towards serotonin and norepinephrine transporters. But there is hardly any drug design based on quantitative structure-activity relationship (QSAR) and pharmacophore modeling attempted yet.

METHOD

In the present study, many TCAs (dibenzoazepine) and phenoxyphenylpropylamine derivatives are taken into consideration for pharmacophore feature generation followed by pharmacophoric distant related descriptors based QSAR modeling. Further, several five new congeners have been designed which are subjected to the prediction of biological activities in terms of serotonin receptor affinity utilizing validated QSAR models developed by us.

RESULTS

An important pharmacophoric feature point C followed by the generation of a topography of the TCAs and phenoxyphenylpropylamine has been predicted. The developed pharmacophoric feature-based QSAR can explain 64.2% of the variances of 5-HT receptor antagonism. The best training model has been statistically validated by the prediction of test set compounds. This training model has been used for the prediction of some newly designed congeneric compounds which are comparable with the existed drugs.

CONCLUSION

The newly designed compounds may be proposed for further synthesis and biological screening as antidepressant agents.

Combined Ligand-Based and Structure-Based Virtual Screening Approach for Identification of New Dipeptidyl Peptidase 4 Inhibitors

BACKGROUND

Dipeptidyl Peptidase 4 (DPP 4) enzyme cleaves an incretin-based glucoregulatory hormone Glucagon Like Peptide -1 from N-terminal where penultimate amino acid is either alanine or proline. Several DPP 4 inhibitors, "gliptins", are approved for the management of Type 2 Diabetes or are under clinical trial. In the present study, combined pharmacophore and docking-based virtual screening protocol were used for the identification of new hits from the Specs Database, which would inhibit DPP 4.

METHODS

The entire computational studies were performed using the Discovery Studio v. 4.1 software package, Pipeline Pilot v. 9.2 (Accelrys Inc.) and FRED v. 2.2.5 (OpenEye Scientific Software). Common feature pharmacophore model was generated from known DPP 4 inhibitors and validated by Receiver Operating curve analysis and GH-scoring method. Database search of Specs commercial database was performed using validated pharmacophore. Hits obtained from pharmacophore search were further docked into the binding site of DPP 4. Based on the analysis of docked poses of hits, 10 compounds were selected for in- vitro DPP 4 enzyme inhibition assay.

RESULTS

Based on docking studies, virtual hits were predicted to form interaction with essential amino acid residues of DPP 4 and have an almost similar binding orientation as that of the reference molecule. Three compounds having Specs database ID- AN-465/42837213, AP-064/42049348 and AN- 465/43369427 were found to inhibit DPP 4 enzyme moderately.

CONCLUSION

The present study demonstrates a successful utilization of in-silico tools in the identification of new DPP 4 inhibitor, which can serve as a starting point for the development of novel DPP 4 inhibitors.

Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning

A significant percentage of Duchenne muscular dystrophy (DMD) cases are caused by premature termination codon (PTC) mutations in the dystrophin gene, leading to the production of a truncated, non-functional dystrophin polypeptide. PTC-suppressing compounds (PTCSC) have been developed in order to restore protein translation by allowing the incorporation of an amino acid in place of a stop codon. However, limitations exist in terms of efficacy and toxicity. To identify new compounds that have PTC-suppressing ability, we selected and clustered existing PTCSC, allowing for the construction of a common pharmacophore model. Machine learning (ML) and deep learning (DL) models were developed for prediction of new PTCSC based on known compounds. We conducted a search of the NCI compounds database using the pharmacophore-based model and a search of the DrugBank database using pharmacophore-based, ML and DL models. Sixteen drug compounds were selected as a consensus of pharmacophore-based, ML, and DL searches. Our results suggest notable correspondence of the pharmacophore-based, ML, and DL models in prediction of new PTC-suppressing compounds.

Identification of the natural compound inhibitors against Plasmodium falciparum plasmepsin-II via common feature based screening and molecular dynamics simulations

Malaria is counted amongst the deadly disease caused by Plasmodium falciparum. Recently, plasmepsin-II enzyme has gained much importance as an attractive drug target for the exploration of antimalarials. Therefore, the common feature pharmacophore models were generated from the crystallized complexes of the plasmepsin-II proteome. These models were subjected to a series of validation procedures, i.e. test set and Güner Henry studies to enlist the representative models. The selected representative hypotheses incorporating the most essential chemical features (common ZHHA) were screened against the natural product database to retrieve the potential candidates. To ensure the selection of the drug-like candidates, prior to screening, filtering steps (Drug-likeness and ADMET filters) were employed on the selected database. To study the interaction pattern of the candidates within the protein, these molecules were advanced to the molecular docking studies. Subsequently, based on the selected cut-off criteria obtained via redocking of the reference (4Z22), 15 compounds showed higher docking score (> -16.05 kcal/mol), and displayed the presence of hydrogen bonding with the crucial amino acids, i.e. Asp34 and Asp214. Further, the stability of the docked molecules was scrutinized via molecular dynamics simulations, and the results were compared with the reference compound 4Z22. All the docked compounds showed stable dynamics behaviour. Thus, in the present contribution, the combination of screening and stability procedures resulted in the identification of 15 hits that can serve as a new chemical space in the designing of the novel antimalarials.Communicated by Ramaswamy H. Sarma.

Structural insight into TNF-α inhibitors through combining pharmacophore-based virtual screening and molecular dynamic simulation

Tumor Necrosis Factor-alpha (TNF-α), a multifunctional cytokine responsible for providing resistance against infections, inflammation, and cancers. TNF-α has emerged as a promising drug target against several autoimmune and inflammatory disorders. Several synthetic antibodies (Infliximab, Etanercept, and Adalimumab) are available, but their potential to cause severe side effects has prompted them to develop alternative small molecules-based therapies for inhibition of TNF-α. In the present study, combined in silico approaches based on pharmacophore modeling, virtual screening, molecular docking, and molecular dynamics studies were employed to understand significant direct interactions between TNF-α protein and small molecule inhibitors. Initially, four different small molecule libraries (∼17.5 million molecules) were virtually screened against the selected pharmacophore model. The identified hits were further subjected to molecular docking studies. The three potent lead compounds (ZINC05848961, ZINC09402309, ZINC04502991) were further subjected to 100 ns molecular dynamic studies to examine their stability. Our docking and molecular dynamic analysis revealed that the selected lead compounds target the TNF receptor (TNFR) and efficiently block the production of TNF. Moreover, in silico ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) analysis revealed that all the predicted compounds have good pharmacokinetic properties with high gastrointestinal absorption and a decent bioavailability score. Furthermore, toxicity profiles further evidenced that these compounds have no risk of being mutagenic, tumorigenic, reproductive and irritant except ZINC11915498. In conclusion, the present study could serve as the starting point to develop new therapeutic regimens to treat various TNF- related diseases. Communicated by Ramaswamy H. Sarma.

Exploring the Characteristics of an Aroma-Blending Mixture by Investigating the Network of Shared Odors and the Molecular Features of Their Related Odorants

The perception of aroma mixtures is based on interactions beginning at the peripheral olfactory system, but the process remains poorly understood. The perception of a mixture of ethyl isobutyrate (Et-iB, strawberry-like odor) and ethyl maltol (Et-M, caramel-like odor) was investigated previously in both human and animal studies. In those studies, the binary mixture of Et-iB and Et-M was found to be configurally processed. In humans, the mixture was judged as more typical of a pineapple odor, similar to allyl hexanoate (Al-H, pineapple-like odor), than the odors of the individual components. To explore the key features of this aroma blend, we developed an in silico approach based on molecules having at least one of the odors—strawberry, caramel or pineapple. A dataset of 293 molecules and their related odors was built. We applied the notion of a “social network” to describe the network of the odors. Additionally, we explored the structural properties of the molecules in this dataset. The network of the odors revealed peculiar links between odors, while the structural study emphasized key characteristics of the molecules. The association between “strawberry” and “caramel” notes, as well as the structural diversity of the “strawberry” molecules, were notable. Such elements would be key to identifying potential odors/odorants to form aroma blends.

Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective

Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.

[Virtual screen of effective AChE inhibitory constituents from Glycyrrhizae Radix et Rhizoma based on pharmacophore and molecular docking]

This research is to predict anti-Alzheimer's disease active constituents on the target of acetylcholinesterase(AChE) from Glycyrrhizae Radix et Rhizoma with the help of pharmacophore and molecular docking. AChE ligand-based pharmacophore model was set up and the molecular library of the constituents from Glycyrrhizae Radix et Rhizoma were established by collecting literature. Then the constituents from Glycyrrhizae Radix et Rhizoma were screen for the potential AChE inhibitory potency in silico through matching with the best pharmacophore model. The flexible docking was used to evaluate the interactions between compounds screened from pharmacophore model and AChE protein(PDB ID:4 EY7). The interactions were expressed including but not limited to CDOCKER interaction energy, hydrogen bonds and non-bonding interactions. The molecular library of Glycyrrhizae Radix et Rhizoma contains 44 chemical constituents. As for the pharmacophore model, six kinds of potential AChE inhibitory constituents from Glycyrrhizae Radix et Rhizoma were considered to be the promising compounds according to the results of searching 3 D database of pharmacophore model. The molecular docking was possessed and the interaction patterns were given to show the detail interactions. The compounds screening from the pharmacophore model were consistent with the existing studies to some degree, indicating that the virtual screen protocols of AChE inhibitory constituents from Glycyrrhizae Radix et Rhizoma based on pharmacophore and molecular docking was reliable.

Computational screening for potential drug candidates against the SARS-CoV-2 main protease

Background: SARS-CoV-2 is the causal agent of the current coronavirus disease 2019 (COVID-19) pandemic. They are enveloped, positive-sense, single-stranded RNA viruses of the Coronaviridae family. Proteases of SARS-CoV-2 are necessary for viral replication, structural assembly, and pathogenicity. The approximately 33.8 kDa M pro protease of SARS-CoV-2 is a non-human homologue and is highly conserved among several coronaviruses, indicating that M pro could be a potential drug target for Coronaviruses. Methods: Herein, we performed computational ligand screening of four pharmacophores (OEW, remdesivir, hydroxychloroquine and N3) that are presumed to have positive effects against SARS-CoV-2 M pro protease (6LU7), and also screened 50,000 natural compounds from the ZINC Database dataset against this protease target. Results: We found 40 pharmacophore-like structures of natural compounds from diverse chemical classes that exhibited better affinity of docking as compared to the known ligands. The 11 best selected ligands, namely ZINC1845382, ZINC1875405, ZINC2092396, ZINC2104424, ZINC44018332, ZINC2101723, ZINC2094526, ZINC2094304, ZINC2104482, ZINC3984030, and ZINC1531664, are mainly classified as beta-carboline, alkaloids, and polyflavonoids, and all displayed interactions with dyad CYS145 and HIS41 from the protease pocket in a similar way as other known ligands. Conclusions: Our results suggest that these 11 molecules could be effective against SARS-CoV-2 protease and may be subsequently tested in vitro and in vivo to develop novel drugs against this virus.

Computational screening for potential drug candidates against the SARS-CoV-2 main protease

Background: SARS-CoV-2 is the causal agent of the current coronavirus disease 2019 (COVID-19) pandemic. They are enveloped, positive-sense, single-stranded RNA viruses of the Coronaviridae family. Proteases of SARS-CoV-2 are necessary for viral replication, structural assembly, and pathogenicity. The approximately 33.8 kDa M pro protease of SARS-CoV-2 is a non-human homologue and is highly conserved among several coronaviruses, indicating that M pro could be a potential drug target for Coronaviruses. Methods: Herein, we performed computational ligand screening of four pharmacophores (OEW, remdesivir, hydroxychloroquine and N3) that are presumed to have positive effects against SARS-CoV-2 M pro protease (6LU7), and also screened 50,000 natural compounds from the ZINC Database dataset against this protease target. Results: We found 40 pharmacophore-like structures of natural compounds from diverse chemical classes that exhibited better affinity of docking as compared to the known ligands. The 11 best selected ligands, namely ZINC1845382, ZINC1875405, ZINC2092396, ZINC2104424, ZINC44018332, ZINC2101723, ZINC2094526, ZINC2094304, ZINC2104482, ZINC3984030, and ZINC1531664, are mainly classified as beta-carboline, alkaloids, and polyflavonoids, and all displayed interactions with dyad CYS145 and HIS41 from the protease pocket in a similar way as other known ligands. Conclusions: Our results suggest that these 11 molecules could be effective against SARS-CoV-2 protease and may be subsequently tested in vitro and in vivo to develop novel drugs against this virus.

Pharmacophore Generation, Quantitative Structure-Activity Relationship (QSAR), and Molecular Dynamic Simulation of Newly Substituted N-(6- Chloro-3-cyano-4-phenyl-4H-chromen-2-yl)-2-(4-chloro-phenoxy)-acetamide for Anticancer Activity

OBJECTIVE

The main objective of the study was to develop the Quantitative Structure- Activity Relationship (QSAR) and pharmacophore model by using data obtained from HT-29 cells to develop potent lead molecule for the scientific community.

MATERIALS AND METHODS

Common pharmacophore model, atom-based 3D-QSAR, and molecular dynamic (MD) simulation were carried out via computational techniques by using 4H-chromene derivatives.

RESULTS

The reliable common pharmacophoric hypothesis, DHH13 was generated and 3.95 survival value was also found. Furthermore, the statistically significant 3D-QSAR model was developed where r²=0.52 was found by using the Partial least squares (PLS) regression method. Phase predicted activity and Log GI⁵⁰ demonstrated an important atomic position in the structure of ligands to ascertain anti colon cancer activity. Also, MD simulation was carried out between top rank leads targeting IL-6 that provided better binding conformational and complex stability into the active pocket site of the target throughout the MD simulation.

CONCLUSION

The outcome of this design shows that the pharmacophore model and 3D-QSAR might be helpful for researchers in the field of medicinal chemistry to design and develop potential anti colon cancer compounds.

Discovery of a new ATP-citrate lyase (ACLY) inhibitor identified by a pharmacophore-based virtual screening study

ATP citrate lyase (ACLY) is an important enzyme that catalyzes the conversion of citrate to acetyl-CoA in normal cells, facilitating the de novo fatty acid synthesis. Lipids and fatty acids were found to be accumulated in different types of tumors, such as brain, breast, rectal and ovarian cancer, representing a great source of energy for cancer cell growth and metabolism. Since ACLY-mediated conversion of citrate to acetyl-CoA constitutes the basis for fatty acid synthesis, ACLY seems to be quite an unexplored and promising therapeutic target for anticancer drug design. A pharmacophore-based virtual screening (VS) protocol with the aid of hierarchical docking, consensus docking (CD), molecular dynamics (MD) simulations and ligand-protein binding free energy calculations led to the identification of compound VS1, which showed a moderate but promising inhibitory activity, demonstrating to be 2.5 times more potent than reference inhibitor 2-hydroxycitrate. These results validate the reliability of our VS workflow and pave the way for the design of novel and more potent ACLY inhibitors.Communicated by Ramaswamy H. Sarma.

α-Substituted Lactams and Acetamides: Ion Channel Modulators that Show Promise in Treating Drug-resistant Epilepsy

The two main problems in the pharmacotherapy of epilepsy are resistance to currently available first-line medications (which occurs in about one third of patients) and the high incidence of side effects. To address these two challenges, extensive efforts are being undertaken to design new, structurally distinct antiepileptic drugs with a broad spectrum of anticonvulsant activity. Tests in animal models of epilepsy indicate that α-substituted lactams and acetamides show a broad spectrum of anticonvulsant activity (including very promising activity in drug-resistant models) as well as an excellent safety profile. Limited clinical results confirm these preclinical findings. In the first part of this review, pharmacology and toxicology of α-substituted lactams and acetamides and their putative protein targets in the brain have been discussed. This is followed by a discussion of structure-activity relationships among α-alkyl-, α-aryl-, and α-aryl-α-alkyl-substituted derivatives. The most promising structures seem to be those related to 3-ethyl-3-phenylpyrrolidin-2-one, 2-phenylbutyramide, and 2- sec-butylvaleramide. The information presented in this review is expected to facilitate rational drug design and development efforts for α-substituted lactams and acetamides.

Insilico analysis of marine indole alkaloids for design of adenosine A2A receptor antagonist

Neurological disease is the disease associated with most of geriatric population in the world. The diseases like Alzheimer's disease and Parkinson's disease are associated with the change in the life style in current era. Treatment of these diseases normally focused on the agents which can able to manipulate the neurotransmitter release, so it is associated with severe side effects. Adenosine receptors are the upcoming targets for the inflammatory as well as neurological diseases as agents like istradefylline are in the clinical use. Marine natural products are the rich source of the valuable drug like substances, number marine alkaloids are known for their ability to pass blood brain barrier (BBB) which is major hurdle in the neurological drug discovery. Here, we report the virtual screening of some marine alkaloids for adenosine 2 receptor binding potential. Results indicated topsentin C, 6'-debromohamacanthin, 6-hydroxydiscodermindole and discodermindole are having excellent binding affinity towards the adenosine 2A receptor than other selected alkaloids.Communicated by Ramaswamy H. Sarma.

Targeting pharmacophore with probe-reactivity-guided fractionation to precisely identify electrophilic sesquiterpenes and its activity of anti-TNBC

INTRODUCTION

Innovative strategy is urgently needed to precisely discover novel natural products as lead compounds for development of new drugs against orphan diseases such as triple-negative breast cancer (TNBC). Herein, we describe a targeting pharmacophore with probe-reactivity-guided strategy for the discovery of electrophilic sesquiterpene (ES), a class of bioactive natural product.

OBJECTIVE

This study aimed to identify pharmacophore, based on pharmacophore with probe-reactivity-guided strategy for precisely discovering ESs from ethyl acetate extract of Eupatorium chinense L. (EEEChL) METHODOLOGY: MTT assay combined with ultra-performance liquid chromatography (UPLC) analysis was used to identify pharmacophore. UPLC-mass spectrometry (MS) was applied to carefully compare the intrinsic reactivity characteristics of two chemoselective nucleophilic probes: glutathione (GSH) and 4-bromothiophenol (BTP) reaction with ESs. ESs was isolated and identified from EEEChL by phytochemical methods. Furthermore, stoichiometric ratio and binding site of one typical ES 8β-[4'-hydroxytigloyloxy]-5-desoxy-8-desacyleuparotin (HDDE) reaction with BTP were studied by UPLC-quadrupole time-of-flight (Q-TOF)-MS and two-dimensional nuclear magnetic resonance (NMR).

RESULTS

Eleven ESs were identified from EEEChL, MTT assay illustrated that all of the 11 ESs possess fairly good anti-TNBC activity CONCLUSIONS: Electrophilic groups were confirmed as pharmacophore of bioactive compounds contained in EEEChL. An optimised halogenated aromatic probe BTP furnishes ES-BTP conjugates that are highly conspicuous via MS by virtue of a unique isotopic bromine signature, conjugates also have a considerable separation on C18 column. The new probe-reactivity-guided strategy can effectively improve the traditional bioassay-guided approaches, and significantly increase the probability of obtaining designated bioactive compounds.

Discovery of Potential Species-Specific Green Insecticides Targeting the Lepidopteran Ryanodine Receptor

Ryanodine receptors (RyRs) are homotetrameric intracellular calcium (Ca²⁺) release channels responsible for excitation-contraction coupling of muscle cells. Diamide insecticides specifically act on RyRs of Lepidoptera and Coleoptera pests and are safe for nontargeted organisms, generating big worldwide sales. Despite their popularity, several devastating agricultural pests have been reported to be resistant to them because of mutations in a small transmembrane region of their RyRs, hinting a binding pocket nearby. A potential solution to overcome resistance is to develop new insecticides targeting different binding sites in pest RyRs. Based on a high-resolution crystal structure of diamondback moth (DBM) RyR N-terminal domain (NTD) determined by our group, we carried out extensive structure-based insecticide screening targeting the intersubunit interface. We identified eight lead compounds that selectively target the open conformation of DBM RyR, which are predicted to act as channel activators similar to diamide insecticides. Binding mode analysis shows selective binding to a hydrophobic pocket of DBM NTD-A but not to the pocket of its mammalian counterpart. We tested three available compounds on the HEK293 cell lines stably expressing DBM or mammalian RyR, one of which shows good potency and selectivity against DBM RyR. The insecticidal effect of the compound was also confirmed using fruit flies. The detailed binding mode, toxicity, absorption, distribution, metabolism, and excretion, and reactivity of the compound were predicted by bioinformatic methods. Together, our study lays a foundation for developing a new class of selective RyR-targeting insecticides to control both wild-type and resistant pests.

Synthesis of Pyrrolidine Monocyclic Analogues of Pochonicine and Its Stereoisomers: Pursuit ofSimplified Structures and Potent β-N-Acetylhexosaminidase Inhibition

Ten pairs of pyrrolidine analogues of pochonicine and its stereoisomers have been synthesized from four enantiomeric pairs of polyhydroxylated cyclic nitrones. Among the ten N-acetylamino pyrrolidine analogues, only compounds with 2,5-dideoxy-2,5-imino-d-mannitol (DMDP) and pochonicine (1) configurations showed potent inhibition of β-N-acetylhexosaminidases (β-HexNAcases); while 1-amino analogues lost almost all their inhibitions towards the tested enzymes. The assay results reveal the importance of the N-acetylamino group and the possible right configurations of pyrrolidine ring required for this type of inhibitors.

A novel hybrid method named electron conformational genetic algorithm as a 4D QSAR investigation to calculate the biological activity of the tetrahydrodibenzazosines

To understand the structure-activity correlation of a group of tetrahydrodibenzazocines as inhibitors of 17β-hydroxysteroid dehydrogenase type 3, we have performed a combined genetic algorithm (GA) and four-dimensional quantitative structure-activity relationship (4D-QSAR) modeling study. The computed electronic and geometry structure descriptors were regulated as a matrix and named as electron-conformational matrix of contiguity (ECMC). A chemical property-based pharmacophore model was developed for series of tetrahydrodibenzazocines by EMRE software package. GA was employed to choose an optimal combination of parameters. A model has been developed for estimating anticancer activity quantitatively. All QSAR models were established with 40 compounds (training set), then they were considered for selective capability with additional nine compounds (test set). A statistically valid 4D-QSAR ( R training 2 = 0.856 , R test 2 = 0.851 and q² = 0.650) with good external set prediction was obtained.

Discovery of Kynurenines Containing Oligopeptides as Potent Opioid Receptor Agonists

Kynurenine (kyn) and kynurenic acid (kyna) are well-defined metabolites of tryptophan catabolism collectively known as "kynurenines", which exert regulatory functions in host-microbiome signaling, immune cell response, and neuronal excitability. Kynurenine containing peptides endowed with opioid receptor activity have been isolated from natural organisms; thus, in this work, novel opioid peptide analogs incorporating L-kynurenine (L-kyn) and kynurenic acid (kyna) in place of native amino acids have been designed and synthesized with the aim to investigate the biological effect of these modifications. The kyna-containing peptide (KA1) binds selectively the m-opioid receptor with a Ki = 1.08 ± 0.26 (selectivity ratio m/d/k = 1:514:10000), while the L-kyn-containing peptide (K6) shows a mixed binding affinity for m, d, and k-opioid receptors, with efficacy and potency (E_max = 209.7 + 3.4%; LogEC₅₀ = -5.984 + 0.054) higher than those of the reference compound DAMGO. This novel oligopeptide exhibits a strong antinociceptive effect after i.c.v. and s.c. administrations in in vivo tests, according to good stability in human plasma (t_1/2 = 47 min).

Structure-Based Virtual Screening of Pseudomonas aeruginosa LpxA Inhibitors Using Pharmacophore-Based Approach

Multidrug resistance in Pseudomonas aeruginosa is a noticeable and ongoing major obstacle for inhibitor design. In P. aeruginosa, uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) acetyltransferase (PaLpxA) is an essential enzyme of lipid A biosynthesis and an attractive drug target. PaLpxA is a homotrimer, and the binding pocket for its substrate, UDP-GlcNAc, is positioned between the monomer A-monomer B interface. The uracil moiety binds at one monomer A, the GlcNAc moiety binds at another monomer B, and a diphosphate form bonds with both monomers. The catalytic residues are conserved and display a similar catalytic mechanism across orthologs, but some distinctions exist between pocket sizes, residue differences, substrate positioning and specificity. The analysis of diversified pockets, volumes, and ligand positions was determined between orthologues that could aid in selective inhibitor development. Thenceforth, a complex-based pharmacophore model was generated and subjected to virtual screening to identify compounds with similar pharmacophoric properties. Docking and general Born-volume integral (GBVI) studies demonstrated 10 best lead compounds with selective inhibition properties with essential residues in the pocket. For biological access, these scaffolds complied with the Lipinski rule, no toxicity and drug likeness properties, and were considered as lead compounds. Hence, these scaffolds could be helpful for the development of potential selective PaLpxA inhibitors.