Computational approaches in drug designing

Flavone-C-glycosides from Cassia auriculata L. as possible inhibitors of phosphodiesterase-5 (PDE5): in vitro, molecular docking and molecular dynamics studies

Phosphodiesterase-5 (PDE5) is a homodimeric enzyme that specifically targets cyclic guanosine monophosphate (cGMP), that mediates many downstream effects such as vasodilation, neurotransmission, and calcium homeostasis. Considering the functions of cGMP, inhibition of PDE5 has been established to have several therapeutic effects in disease conditions such as cancer, cardiovascular diseases and Alzheimer's disease. Consequently, many PDE5 inhibitors were developed but with severe adverse effects such as non-arteritic anterior ischemic optic neuropathy (NAION), priapism, etc. Hence, in our study for the identification of new PDE5 inhibitors from alternative sources, Cassia auriculata L. was identified as a potential PDE5 inhibitors with 56.23% inhibition at 100 μg/mL in vitro. In addition, the respective phytoconstituents were evaluated through molecular docking, interaction studies and MM/GBSA binding free energy calculations, identifying two potential flavone C-glycosides, lucenin-II (-15.977, dG bind = -38.8), stellarin-II (-15.099, dG bind = -34.59), and a flavan derivative (2S)-7,4-dihydroxyflavan(4β-8)-catechin, in comparison to sildenafil (-10.890, dG bind = -75.4) and having frequent contacts with Phe 786, Phe 820, Ser 663, Tyr 664, and other crucial residues at the catalytic site of PDE5. Molecular dynamics simulations performed for 100 ns showed structural stability and compactness of the candidates through RMSD, RMSF which showed less fluctuations. The ADMET analysis revealed favorable pharmacokinetics, and pharmacodynamic properties with no subsequent toxicity in normal cells. The biological target class prediction identified enzymes with similar properties and icariin, which is a well-established natural PDE5 inhibitor was identified as a structurally similar analogue. These findings could lead to the development of novel natural product based PDE5 inhibitors.

Molecular insights and inhibitory dynamics of flavonoids in targeting Pim-1 kinase for cancer therapy

Pim-1 kinase, a serine/threonine kinase, is often overexpressed in various cancers, contributing to disease progression and poor prognosis. In this study, we explored the potential of flavonoids as inhibitors of Pim-1 kinase using a combination of molecular docking and steered molecular dynamics (SMD) simulations. Our docking studies revealed two main binding orientations for the flavonoid molecules. The SMD simulations showed that the binding mode with higher pulling forces was linked to stronger inhibitory activity, with a strong positive correlation (R 2 ≈ 0.92) between pulling forces and IC50 values. Quercetin stood out as the most potent inhibitor, showing a pulling force of about 820 pN and an IC_(5) 0 of less than 6 µM. Further dynamic simulations indicated that quercetin's hydroxyl groups at the C3, C-5 and C-7 positions formed stable hydrogen bonds with key residues GLU-121, Leu-44 and Val-126, respectively enhancing its binding stability and effectiveness. Our results emphasized the critical role of the hydroxyl group at the C-3 position, which plays a pivotal function in effectively anchoring these molecules in the active site of Pim-1 kinase. Principal component analysis (PCA) of Pim-1 kinase's conformational changes revealed that potent inhibitors like quercetin, galangin, and kaempferol significantly restricted the enzyme's flexibility, suggesting potential inhibitory effect. These findings provide insights into the structural interactions between flavonoids and Pim-1 kinase, offering a foundation for future experimental investigations. However, further studies, including in vitro and in vivo validation, are necessary to assess the pharmacological relevance and specificity of flavonoids in cancer therapy.

Developing phytocompound-based new drugs against multi-drug-resistant Staphylococcus aureus

Staphylococcus aureus, a prevalent component of the human microbiota, is associated with skin infections to life-threatening diseases, presenting challenges in treatment options and necessitating the development of effective treatments. This study integrated computational and in vitro approaches to identify promising phytocompounds with therapeutic potential. Staphopain B emerged as a target protein for its role in immune evasion, exhibiting stability during molecular dynamic simulation (MDS) with a root mean square deviation value of 2.376 Å. Screening 115 phytocompounds with antibacterial properties from the PubChem database identified 12 with drug-like properties, nine of which showed superior binding affinity to Staphopain B compared to a commercial antibiotic, doxycycline (-7.8 kcal mol-1). Notably, epoxyazadiradione and nimbolide displayed higher estimated free energy of binding scores (-7.91 and -7.93 kcal mol-1, respectively), indicating strong protein-ligand interactions. The root mean square fluctuation values for epoxyazadiradione and nimbolide were 1.097 and 1.034 Å, respectively, which was confirmed through MDS. Crude ethanolic extracts (100% and 70%) of neem (Azadirachta indica) leaves demonstrated narrow inhibition against the bacteria in comparison to doxycycline in the disc-diffusion assay. This study underscores the potential of phytocompounds as therapeutic agents against S. aureus; however, further in vitro experiments and testing of the phytocompounds in vivo are required.

Integrated virtual screening and molecular dynamics simulation approaches revealed potential natural inhibitors for DNMT1 as therapeutic solution for triple negative breast cancer

Triple negative breast cancers (TNBC) are clinically heterogeneous but mostly aggressive malignancies devoid of expression of the estrogen, progesterone, and HER2 (ERBB2 or NEU) receptors. It accounts for 15-20% of all cases. Altered epigenetic regulation including DNA hypermethylation by DNA methyltransferase 1 (DNMT1) has been implicated as one of the causes of TNBC tumorigenesis. The antitumor effect of DNMT1 has also been explored in TNBC that currently lacks targeted therapies. However, the actual treatment for TNBC is yet to be discovered. This study is attributed to the identification of novel drug targets against TNBC. A comprehensive docking and simulation analysis was performed to optimize promising new compounds by estimating their binding affinity to the target protein. Molecular dynamics simulation of 500 ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between compound and binding pockets of DNMT1. In a nutshell, our study uncovered that Beta-Mangostin, Gancaonin Z, 5-hydroxysophoranone, Sophoraflavanone L, and Dorsmanin H showed maximum binding affinity with the active sites of DNMT1. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with TNBC, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

Machine learning methods applied for the prediction of biological activities of triple reuptake inhibitors

Major depressive disorder (MDD) is characterized by a series of disabling symptoms like anhedonia, depressed mood, lack of motivation for daily tasks and self-extermination thoughts. The monoamine deficiency hypothesis states that depression is mainly caused by a deficiency of monoamine at the synaptic cleft. Thus, major efforts have been made to develop drugs that inhibit serotonin (SERT), norepinephrine (NET) and dopamine (DAT) transporters and increase the availability of these monoamines. Current gold standard treatment of MDD uses drugs that target one or more monoamine transporters. Triple reuptake inhibitors (TRIs) can target SERT, NET, and DAT simultaneously, and are believed to have the potential to be early onset antidepressants. Quantitative structure-activity relationship models were developed using machine learning algorithms in order to predict biological activities of a series of triple reuptake inhibitor compounds that showed in vitro inhibitory activity against multiple targets. The results, using mostly interpretable descriptors, showed that the internal and external predictive ability of the models are adequate, particularly of the DAT and NET by Random Forest and Support Vector Machine models. The current work shows that models developed from relatively simple, chemically interpretable descriptors can predict the activity of TRIs with similar structure in the applicability domain using ML methods.Communicated by Ramaswamy H. Sarma.

Preclinical data on morpholine (3,5-di-tertbutyl-4-hydroxyphenyl) methanone induced anxiolysis

Trimetozine is used to be indicated for the treatment of mental illnesses, particularly anxiety. The present study provides data on the pharmacological profile of trimetozine derivative morpholine (3,5-di-tert-butyl-4-hydroxyphenyl) methanone (LQFM289) which was designed from molecular hybridization of trimetozine lead compound and 2,6-di-tert-butyl-hydroxytoluene to develop new anxiolytic drugs. Here, we conduct molecular dynamics simulations, docking studies, receptor binding assays, and in silico ADMET profiling of LQFM289 before its behavioral and biochemical assessment in mice within the dose range of 5-20 mg/kg. The docking of LQFM289 showed strong interactions with the benzodiazepine binding sites and matched well with receptor binding data. With the ADMET profile of this trimetozine derivative that predicts a high intestinal absorption and permeability to blood-brain barrier without being inhibited by the permeability glycoprotein, the oral administration of LQFM289 10 mg/kg consistently induced anxiolytic-like behavior of the mice exposed to the open field and light-dark box apparatus without eliciting motor incoordination in the wire, rotarod, and chimney tests. A decrease in the wire and rotarod´s fall latency coupled with an increase in the chimney test´s climbing time and a decrease in the number of crossings in the open field apparatus at the dose of 20 mg/kg of this trimetozine derivative suggest sedative or motor coordination impairment at this highest dose. The attenuation of the anxiolytic-like effects of LQFM289 (10 mg/kg) by flumazenil pretreatment implicates the participation of benzodiazepine binding sites. The lowering of corticosterone and tumor necrosis factor alpha (cytokine) in LQFM289-treated mice at a single oral (acute) dose of 10 mg/kg suggests that the anxiolytic-like effect of this compound also involves the recruitment of non-benzodiazepine binding sites/GABAergic molecular machinery.

CORAL: probing the structural requirements for α-amylase inhibition activity of 5-(3-arylallylidene)-2-(arylimino)thiazolidin-4-one derivatives based on QSAR with correlation intensity index, molecular docking, molecular dynamics, and ADMET studies

The present study aims to examine the structural requirements governing α-amylase inhibitory activity of 5-(3-arylallylidene)-2-(arylimino)thiazolidin-4-one derivatives and their precursors by employing a multifaceted approach combining in vitro and in silico studies. The in vitro assay findings revealed strong inhibitory effect of this class of compounds against α-amylase and compound 20 exhibited maximum percentage inhibition of 88.54 ± 0.69, 84.98 ± 0.40, 77.26 ± 0.75, 67.80 ± 0.54, and 62.93 ± 1.17 at 200, 100, 50, 25, and 12.5 µg mL-1, respectively. Multiple CORAL QSAR models were developed from the randomly distributed eight splits by employing two target functions (TF1, TF2 with WCII = 0.0 and = 0.3, respectively), and the quality of predictions by the produced models was validated with the help of various statistical parameters. The model M-4 (R2Val = 0.8799) and model M-11 (R2Val = 0.9064) were the leading models developed by using TF1 and TF2. We designed five new congeneric inhibitors (D-1 to D-5) by incorporating SMILES features positively correlating with the activity. Molecular docking experiments were carried out to confirm the binding of these new inhibitors with the biological receptor α-amylase (PDB ID: 7TAA). Furthermore, molecular dynamic simulations provided a thorough knowledge of the binding process by shedding insight into the dynamic behavior and stability of the ligand-receptor complex over time. The results of this study highlight the key structural characteristics needed for improved α-amylase inhibitory efficacy and provide a rational basis for the development of more effective inhibitors.Communicated by Ramaswamy H. Sarma.

Network pharmacology combined with molecular docking and in vitro verification reveals the therapeutic potential of Delphinium roylei munz constituents on breast carcinoma

Delphinium roylei Munz is an indigenous medicinal plant to India where its activity against cancer has not been previously investigated, and its specific interactions of bioactive compounds with vulnerable breast cancer drug targets remain largely unknown. Therefore, in the current study, we aimed to evaluate the anti-breast cancer activity of different extracts of D. roylei against breast cancer and deciphering the molecular mechanism by Network Pharmacology combined with Molecular Docking and in vitro verification. The experimental plant was extracted with various organic solvents according to their polarity index. Phytocompounds were identified by High resolution-liquid chromatography-mass spectrometry (HR-LC/MS) technique, and SwissADME programme evaluated their physicochemical properties. Next, target(s) associated with the obtained bioactives or breast cancer-related targets were retrieved by public databases, and the Venn diagram selected the overlapping targets. The networks between overlapping targets and bioactive were visualized, constructed, and analyzed by STRING programme and Cytoscape software. Finally, we implemented a molecular docking test (MDT) using AutoDock Vina to explore key target(s) and compound(s). HR-LC/MS detected hundreds of phytocompounds, and few were accepted by Lipinski's rules after virtual screening and therefore classified as drug-like compounds (DLCs). A total of 464 potential target genes were attained for the nine quantitative phytocompounds and using Gene Cards, OMIM and DisGeNET platforms, 12063 disease targets linked to breast cancer were retrieved. With Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment, a total of 20 signalling pathways were manifested, and a hub signalling pathway (PI3K-Akt signalling pathway), a key target (Akt1), and a key compound (8-Hydroxycoumarin) were selected among the 20 signalling pathways via molecular docking studies. The molecular docking investigation revealed that among the nine phytoconstituents, 8-hydroxycoumarin showed the best binding energy (-9.2 kcal/mol) with the Akt1 breast cancer target. 8-hydroxycoumarin followed all the ADME property prediction using SwissADME, and 100 nanoseconds (ns) MD simulations of 8-hydroxycoumarin complexes with Akt1 were found to be stable. Furthermore, D. roylei extracts also showed significant antioxidant and anticancer activity through in vitro studies. Our findings indicated for the first time that D. roylei extracts could be used in the treatment of BC.

Nigella sativa Oil Improves Motor Skill Learning of Albino Mice: In Vivo and In Silico Investigations

Experimental evidences demonstrated that Nigella sativa oil (NSO) can restore neuronal integrities and processes by increasing the neuronal density, decreasing apoptosis, preventing inflammatory processes, and improving the neurogenic cells in the hippocampus. This refurbishment enhances the learning process and memory. The antioxidant defense mechanism of NSO slows down the process of neurodegeneration and motor deficit. The present study aimed to investigate the effects of NSO on motor skill learning using the single pellet reaching task method on Swiss albino mice, followed by in silico studies. Mice (total of 16) were randomly divided into the control group and treatment group (n = 8). The treatment group received 1 ml/kg b.w. NSO orally once daily for 7 days, and a control group received 1 ml/kg normal saline instead of NSO in a similar manner. The average success rate due to ingestion of NSO in the treatment group mice increased significantly (P < 0.05) compared to controlled mice. Molecular docking analysis revealed that thymoquinone, carvacrol, thymohydroquinone, p-cymene, and t-anethole have binding affinities for the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPA-R) that ranges from (-5.1 to -6.2) kcal/mol, which is comparable to the reference ligand glutamic acid binding affinity with AMPA-R (-6.6 kcal/mol). Thymoquinone and carvacrol formed hydrogen bonds with AMPA receptor at TYR61, SER142, and SER143 residues, comparable to the binding affinity of glutamic acid. ADMET analysis reported that all the compounds have higher bioavailability (>90%) and can cross the BBB easily (logBB> 0.3). Based on our experimental data and in silico report, we concluded that the enhanced motor skill learning effects of NSO are due to presence of potent antioxidants-thymoquinone and carvacrol-which might serve as AMPA receptor agonists. These phytoconstituents may play role in synaptic strengthening and promote experience-dependent motor skill learning.

Integrated molecular modeling and dynamics approaches revealed potential natural inhibitors of NF-κB transcription factor as breast cancer therapeutics

Breast cancer is a silent killer malady among women and a serious economic burden in health care management. A case of breast cancer is diagnosed among women every 19 s, and every 74 s, a woman dies of breast cancer somewhere in the world. Despite the pop-up of progressive research, advanced treatment approaches, and preventive measures, breast cancer remains amplifying ailment. The nuclear factor kappa B (NF-κB) is a key transcription factor that links inflammation with cancer and is demonstrated as being involved in the tumorigenesis of breast cancer. The NF-κB transcription factor family in mammals consists of five proteins; c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52). The antitumor effect of NF-κB has also been explored in breast cancer, however, the actual treatment for breast cancer is yet to be discovered. This study is attributed to the identification of novel drug targets against breast cancer by targeting c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52) proteins. To identify the putative active compounds, a structure-based 3D pharmacophore model to the protein active site cavity was generated followed by virtual screening, molecular docking, and molecular dynamics (MD) simulation. Initially, a library of 45000 compounds were docked against the target protein and five compounds namely Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 were selected for further analysis. The relative binding affinity of Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 with NF-κB1 (p50), NF-κB2 (p52), RelA (p65), RelB, and c-Rel proteins were -6.8, -8, -7.0, -6.9, and -7.2 kcal/mol, respectively which remained stable throughout the simulations of 200 ns. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with breast cancer, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

In Silico Identification and Analysis of Potentially Bioactive Antiviral Phytochemicals against SARS-CoV-2: A Molecular Docking and Dynamics Simulation Approach

SARS-CoV-2, a deadly coronavirus sparked COVID-19 pandemic around the globe. With an increased mutation rate, this infectious agent is highly transmissible inducing an escalated rate of infections and death everywhere. Hence, the discovery of a viable antiviral therapy option is urgent. Computational approaches have offered a revolutionary framework to identify novel antimicrobial treatment regimens and allow a quicker, cost-effective, and productive conversion into the health center by evaluating preliminary and safety investigations. The primary purpose of this research was to find plausible plant-derived antiviral small molecules to halt the viral entrance into individuals by clogging the adherence of Spike protein with human ACE2 receptor and to suppress their genome replication by obstructing the activity of Nsp3 (Nonstructural protein 3) and 3CLpro (main protease). An in-house library of 1163 phytochemicals were selected from the NPASS and PubChem databases for downstream analysis. Preliminary analysis with SwissADME and pkCSM revealed 149 finest small molecules from the large dataset. Virtual screening using the molecular docking scoring and the MM-GBSA data analysis revealed that three candidate ligands CHEMBL503 (Lovastatin), CHEMBL490355 (Sulfuretin), and CHEMBL4216332 (Grayanoside A) successfully formed docked complex within the active site of human ACE2 receptor, Nsp3, and 3CLpro, respectively. Dual method molecular dynamics (MD) simulation and post-MD MM-GBSA further confirmed efficient binding and stable interaction between the ligands and target proteins. Furthermore, biological activity spectra and molecular target analysis revealed that all three preselected phytochemicals were biologically active and safe for human use. Throughout the adopted methodology, all three therapeutic candidates significantly outperformed the control drugs (Molnupiravir and Paxlovid). Finally, our research implies that these SARS-CoV-2 protein antagonists might be viable therapeutic options. At the same time, enough wet lab evaluations would be needed to ensure the therapeutic potency of the recommended drug candidates for SARS-CoV-2.

Filamentous temperature sensitive mutant Z: a putative target to combat antibacterial resistance

In the pre-antibiotic era, common bacterial infections accounted for high mortality and morbidity. Moreover, the discovery of penicillin in 1928 marked the beginning of an antibiotic revolution, and this antibiotic era witnessed the discovery of many novel antibiotics, a golden era. However, the misuse or overuse of these antibiotics, natural resistance that existed even before the antibiotics were discovered, genetic variations in bacteria, natural selection, and acquisition of resistance from one species to another consistently increased the resistance to the existing antibacterial targets. Antibacterial resistance (ABR) is now becoming an ever-increasing concern jeopardizing global health. Henceforth, there is an urgent unmet need to discover novel compounds to combat ABR, which act through untapped pathways/mechanisms. Filamentous Temperature Sensitive mutant Z (FtsZ) is one such unique target, a tubulin homolog involved in developing a cytoskeletal framework for the cytokinetic ring. Additionally, its pivotal role in bacterial cell division and the lack of homologous structural protein in mammals makes it a potential antibacterial target for developing novel molecules. Approximately 2176 X-crystal structures of FtsZ were available, which initiated the research efforts to develop novel antibacterial agents. The literature has reported several natural, semisynthetic, peptides, and synthetic molecules as FtsZ inhibitors. This review provides valuable insights into the basic crystal structure of FtsZ, its inhibitors, and their inhibitory activities. This review also describes the available in vitro detection and quantification methods of FtsZ-drug complexes and the various approaches for determining drugs targeting FtsZ polymerization.

Docking-Based Prediction of Peptide Binding to MHC Proteins

Major histocompatibility complex (MHC) proteins are the most polymorphic and polygenic proteins in humans. They bind peptides, derived from cleavage of different pathogenic antigens, and are responsible for presenting them to T cells. The peptides recognized by the T cell receptors are denoted as epitopes and they trigger an immune response.In this chapter, we describe a docking protocol for predicting the peptide binding to a given MHC protein using the software tool GOLD. The protocol starts with the construction of a combinatorial peptide library used in the docking and ends with the derivation of a quantitative matrix (QM) accounting for the contribution of each amino acid at each peptide position.

Vetinformatics from functional genomics to drug discovery: Insights into decoding complex molecular mechanisms of livestock systems in veterinary science

Having played important roles in human growth and development, livestock animals are regarded as integral parts of society. However, industrialization has depleted natural resources and exacerbated climate change worldwide, spurring the emergence of various diseases that reduce livestock productivity. Meanwhile, a growing human population demands sufficient food to meet their needs, necessitating innovations in veterinary sciences that increase productivity both quantitatively and qualitatively. We have been able to address various challenges facing veterinary and farm systems with new scientific and technological advances, which might open new opportunities for research. Recent breakthroughs in multi-omics platforms have produced a wealth of genetic and genomic data for livestock that must be converted into knowledge for breeding, disease prevention and management, productivity, and sustainability. Vetinformatics is regarded as a new bioinformatics research concept or approach that is revolutionizing the field of veterinary science. It employs an interdisciplinary approach to understand the complex molecular mechanisms of animal systems in order to expedite veterinary research, ensuring food and nutritional security. This review article highlights the background, recent advances, challenges, opportunities, and application of vetinformatics for quality veterinary services.

A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19

BACKGROUND

COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed.

PURPOSE

This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases.

METHODS

A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles.

RESULTS

In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks.

CONCLUSION

Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.