In silico pharmacology for drug discovery: applications to targets and beyond

Targeting SARS-CoV2 spike glycoprotein: molecular insights into phytocompounds binding interactions - in-silico molecular docking

This study utilized small molecular characterization and docking study to evaluate the binding affinity of seven antiviral phytocompounds with the SARS CoV-2 variants (SARS-CoV-2 Spike Glycoprotein, SARS-CoV-2 Spike Protein Variant in 1-RBD, Alpha Variant SARS-CoV2- Spike Protein). The results revealed that five of seven compounds, possesses excellent drug lead property reveled through in-silico ADMET analysis. In addition, six of seven except D-Glucosamine, exhibited excellent binding affinity. Six ligands possess significant binding affinity towards SARS-CoV-2 variants 6VXX, 7LWV and 7R13, which is certainly greater than Remdesivir. Fagaronine found to be the best drug candidate against SARS-CoV-2 variants, It was found that -7.4, -5.6 and -6.3 is the docking score respectively. Aranotin, Beta aescin, Gliotoxin, and Fagaronine formed hydrogen bonds with specific amino acids and exhibited significant binding interactions. These findings suggest that these phytocompounds could be promising candidates for developing antiviral therapies against SARS-CoV-2. Moreover, the study underscores the importance of molecular docking in understanding protein-ligand interactions and its role in drug discovery. The documented pharmacological properties of these compounds in the literature further support their potential therapeutic relevance in various diseases.

Molecular docking and antimicrobial activities of photoexcited inhibitors in antimicrobial photodynamic therapy against Enterococcus faecalis biofilms in endodontic infections

Antimicrobial photodynamic therapy (aPDT) is a promising approach to combat antibiotic resistance in endodontic infections. It eliminates residual bacteria from the root canal space and reduces the need for antibiotics. To enhance its effectiveness, an in silico and in vitro study was performed to investigate the potential of targeted aPDT using natural photosensitizers, Kojic acid and Parietin. This approach aims to inhibit the biofilm formation of Enterococcus faecalis, a frequent cause of endodontic infections, by targeting the Ace and Esp proteins. After determining the physicochemical characteristics of Ace and Esp proteins and model quality assessment, the molecular dynamic simulation was performed to recognize the structural variations. The stability and physical movement of the protein-ligand complexes were evaluated. In silico molecular docking was conducted, followed by ADME/Tox profiling, pharmacokinetics characteristics, and assessment of drug-likeness properties of the natural photosensitizers. The study also investigated the changes in the expression of genes (esp and ace) involved in E. faecalis biofilm formation. The results showed that both Kojic acid and Parietin complied with Lipinski's rule of five and exhibited drug-like properties. In silico analysis indicated stable complexes between Ace and Esp proteins and the natural photosensitizers. The molecular docking studies demonstrated good binding affinity. Additionally, the expression of the ace and esp genes was significantly downregulated in aPDT using Kojic acid and Parietin with blue light compared to the control group. This investigation concluded that Kojic acid and Parietin with drug-likeness could efficiently interact with Ace and Esp proteins with a strong binding affinity. Hence, natural photosensitizers-mediated aPDT can be considered a promising adjunctive treatment against endodontic infections.

In-silico assessment of novel peptidomimetics inhibitor targeting STAT3 and STAT4 N-terminal domain dimerization: A comprehensive study using molecular docking, molecular dynamics simulation, and binding free energy analysis

Dysregulation in Janus kinase-Signal Transducer and Activation of Transcription (JAK-STAT) pathway is closely linked to various cancer types. The N-terminal domain (NTD) of STAT proteins, upon dimerization, assumes a multifaceted role with remarkable adaptability in mediating interactions between proteins. Consequently, the strategic targeting of the N-terminal domain of STATs has emerged as a promising tactic for disrupting dimerization and impeding the translocation of STAT proteins. In this study, we have deployed an integrated in-silico methodology to rationally design Peptidomimetic foldamers as inhibitors of the N-terminal domains of STAT3 and STAT4, with the objective of disrupting protein dimerization. Consequently, we have judiciously designed a series of peptidomimetics that encompass β3-amino acids, bearing side chains that mimic the residues within interface II of the dimeric structures of the NTDs. Employing molecular docking techniques; we have assessed the binding affinity of these designed peptidomimetics toward both the NTDs. Furthermore, we have conducted an evaluation of the stability and conformational alterations within the docked complexes over an extensive Molecular Dynamics, subsequently computing the binding free energy utilizing MM/PBSA calculations. Our findings unequivocally demonstrate that the peptidomimetic foldamers we have devised (Peptide-A, Peptide-B, and Peptide-C) exhibit a propensity to bind to and impede the dimerization process of the NTDs of both STAT3 and STAT4. These outcomes serve to underscore the potential of these meticulously designed peptidomimetics as potential candidates meriting further exploration in the realm of cancer prevention and management.

A review on computational studies and bioinformatics analysis of potential drugs against monkeypox virus

Monkeypox, a viral disease that is caused by monkeypox virus and occurs mainly in central and western Africa. However, recently it is spreading worldwide and took the focus of the scientific world towards it. Therefore, we made an attempt to cluster all the related information that may make it easy for the researchers to get the information easily and carry out their research smoothly to find prophylaxis against this emerging virus. There are very few researches found available on monkeypox. Almost all the studies were focused on smallpox virus and the recommended vaccines and therapeutics for monkeypox virus were originally developed for smallpox virus. Though these are recommended for emergency cases, they are not fully effective and specific against monkeypox. For this, here we also took the help of bioinformatics tools to screen potential drug candidates against this growing burden. Some potential antiviral plant metabolites, inhibitors and available drugs were scrutinized that can block the essential survival proteins of this virus. All the compounds Amentoflavone, Pseudohypericin, Adefovirdipiboxil, Fialuridin, Novobiocin and Ofloxacin showed elite binding efficiency with suitable ADME properties and Amentoflavone and Pseudohypericin showed stability in MD simulation study indicating their potency as probable drugs against this emerging virus.Communicated by Ramaswamy H. Sarma.

Developing Dynamic Structure-Based Pharmacophore and ML-Trained QSAR Models for the Discovery of Novel Resistance-Free RET Tyrosine Kinase Inhibitors Through Extensive MD Trajectories and NRI Analysis

Activation of RET tyrosine kinase plays a critical role in the pathogenesis of various cancers, including non-small cell lung cancer, papillary thyroid cancers, multiple endocrine neoplasia type 2A and 2B (MEN2A, MEN2B), and familial medullary thyroid cancer. Gene fusions and point mutations in the RET proto-oncogene result in constitutive activation of RET signaling pathways. Consequently, developing effective inhibitors to target RET is of utmost importance. Small molecules have shown promise as inhibitors by binding to the kinase domain of RET and blocking its enzymatic activity. However, the emergence of resistance due to single amino acid changes poses a significant challenge. In this study, a structure-based dynamic pharmacophore-driven approach using E-pharmacophore modeling from molecular dynamics trajectories is proposed to select low-energy favorable hypotheses, and ML-trained QSAR models to predict pIC50 values of compounds. For this aim, extensive small molecule libraries were screened using developed ligand-based models, and potent compounds that are capable of inhibiting RET activation were proposed.

The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications

Recent advances in machine learning (ML) have led to newer model architectures including transformers (large language models, LLMs) showing state of the art results in text generation and image analysis as well as few-shot learning (FSLC) models which offer predictive power with extremely small datasets. These new architectures may offer promise, yet the 'no-free lunch' theorem suggests that no single model algorithm can outperform at all possible tasks. Here, we explore the capabilities of classical (SVR), FSLC, and transformer models (MolBART) over a range of dataset tasks and show a 'goldilocks zone' for each model type, in which dataset size and feature distribution (i.e. dataset "diversity") determines the optimal algorithm strategy. When datasets are small ( < 50 molecules), FSLC tend to outperform both classical ML and transformers. When datasets are small-to-medium sized (50-240 molecules) and diverse, transformers outperform both classical models and few-shot learning. Finally, when datasets are of larger and of sufficient size, classical models then perform the best, suggesting that the optimal model to choose likely depends on the dataset available, its size and diversity. These findings may help to answer the perennial question of which ML algorithm is to be used when faced with a new dataset.

Quantifying the efficacy of voltage protocols in characterising ion channel kinetics: A novel information-theoretic approach

Voltage-clamp experiments are commonly utilised to characterise cellular ion channel kinetics. In these experiments, cells are stimulated using a known time-varying voltage, referred to as the voltage protocol, and the resulting cellular response, typically in the form of current, is measured. Parameters of models that describe ion channel kinetics are then estimated by solving an inverse problem which aims to minimise the discrepancy between the predicted response of the model and the actual measured cell response. In this paper, a novel framework to evaluate the information content of voltage-clamp protocols in relation to ion channel model parameters is presented. Additional quantitative information metrics that allow for comparisons among various voltage protocols are proposed. These metrics offer a foundation for future optimal design frameworks to devise novel, information-rich protocols. The efficacy of the proposed framework is evidenced through the analysis of seven voltage protocols from the literature. By comparing known numerical results for inverse problems using these protocols with the information-theoretic metrics, the proposed approach is validated. The essential steps of the framework are: (i) generate random samples of the parameters from chosen prior distributions; (ii) run the model to generate model output (current) for all samples; (iii) construct reduced-dimensional representations of the time-varying current output using proper orthogonal decomposition (POD); (iv) estimate information-theoretic metrics such as mutual information, entropy equivalent variance, and conditional mutual information using non-parametric methods; (v) interpret the metrics; for example, a higher mutual information between a parameter and the current output suggests the protocol yields greater information about that parameter, resulting in improved identifiability; and (vi) integrate the information-theoretic metrics into a single quantitative criterion, encapsulating the protocol's efficacy in estimating model parameters.

Exploring the Anticancer Potential of Semisynthetic Derivatives of 7α-Acetoxy-6β-hydroxyroyleanone from Plectranthus sp.: An In Silico Approach

The diterpene 7α-acetoxy-6β-hydroxyroyleanone isolated from Plectranthus grandidentatus demonstrates promising antibacterial, anti-inflammatory and anticancer properties. However, its bioactivity may be enhanced via strategic structural modifications of such natural products through semisynthesis. The anticancer potential of 7α-acetoxy-6β-hydroxyroyleanone and five derivatives was analyzed in silico via the prediction of chemicals absorption, distribution, metabolism, excretion, and toxicity (ADMET), quantum mechanical calculations, molecular docking and molecular dynamic simulation. The protein targets included regulators of apoptosis and cell proliferation. Additionally, network pharmacology was used to identify potential targets and signaling pathways. Derivatives 7α-acetoxy-6β-hydroxy-12-O-(2-fluoryl)royleanone and 7α-acetoxy-6β-(4-fluoro)benzoxy-12-O-(4-fluoro)benzoylroyleanone achieved high predicted binding affinities towards their respective protein panels, with stable molecular dynamics trajectories. Both compounds demonstrated favorable ADMET parameters and toxicity profiles. Their stability and reactivity were confirmed via geometry optimization. Network analysis revealed their involvement in cancer-related pathways. Our findings justify the inclusion of 7α-acetoxy-6β-hydroxy-12-O-(2-fluoryl)royleanone and 7α-acetoxy-6β-(4-fluoro)benzoxy-12-O-(4-fluoro)benzoylroyleanone in in vitro analyses as prospective anticancer agents. Our binding mode analysis and stability simulations indicate their potential as selective inhibitors. The data will guide studies into their structure optimization, enhancing efficacy and drug-likeness.

Finding new analgesics: Computational pharmacology faces drug discovery challenges

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.

Specifically targeting antimicrobial peptides for inhibition of Candidatus Liberibacter asiaticus

AIMS

Huanglongbing (citrus greening) is a plant disease putatively caused by the unculturable Gram-negative bacterium Candidatus Liberibacter asiaticus (CLas), and it has caused severe damage to citrus plantations worldwide. There are no definitive treatments for this disease, and conventional disease control techniques have shown limited efficacy. This work presents an in silico evaluation of using specifically targeting anti-microbial peptides (STAMPs) consisting of a targeting segment and an antimicrobial segment to inhibit citrus greening by inhibiting the BamA protein of CLas, which is an outer membrane protein crucial for bacterial viability.

METHODS AND RESULTS

Initially, a set of peptides with a high affinity toward BamA protein were screened and evaluated via molecular docking and molecular dynamics simulations and were verified in vitro via bio-layer interferometry (BLI). In silico studies and BLI experiments indicated that two peptides, HASP2 and HASP3, showed stable binding to BamA. Protein structures for STAMPs were created by fusing known anti-microbial peptides (AMPs) with the selected short peptides. The binding of STAMPs to BamA was assessed using molecular docking and binding energy calculations. The attachment of high-affinity short peptides significantly reduced the free energy of binding for AMPs, suggesting that it would make it easier for the STAMPs to bind to BamA. Efficacy testing in vitro using a closely related CLas surrogate bacterium showed that STAMPs had greater inhibitory activity than AMP alone.

CONCLUSIONS

In silico and in vitro results indicate that the STAMPs can inhibit CLas surrogate Rhizobium grahamii more effectively compared to AMPs, suggesting that STAMPs can achieve better inhibition of CLas, potentially via enhancing the site specificity of AMPs.

New thiazole, thiophene and 2-pyridone compounds incorporating dimethylaniline moiety: synthesis, cytotoxicity, ADME and molecular docking studies

Various sets of thiazole, thiophene, and 2-pyridone ring structures containing a dimethylaniline component were synthesized. Substituted thiazoles 2-3 and thiophenes 5-7 were produced by reacting thiocarbamoyl compound 4 with α-halogenated reagents in different basic conditions. Also, a series of 2-pyridone derivatives 9a-f substituted with dimethylaniline was synthesized through Michael addition of malononitrile to α,β-unsaturated nitrile derivatives 8a-f. The synthesized products were structurally proven by spectroscopic methods such as IR, 1H NMR, 13C NMR, and MS data. Furthermore, the anti-cancer efficacy of the compounds was assessed using the MTT assay on two cell lines: hepatocellular carcinoma (HepG-2) and breast cancer (MDA-MB-231). The results showed the highest growth inhibition for derivatives 2, 6, 7, and 9c, which were further examined for their IC50 values. The IC50 for compound 2 showed equipotent activity (IC50 = 1.2 µM) against the HepG-2 cell line compared to Doxorubicin (IC50 = 1.1 µM). Compounds 2, 6, 7 and 9c showed very good ADME assessments for further drug administration. Moreover, the PASS theoretical prediction for the compounds showed high antimitotic and antineoplastic activities for compounds 2, 6, 7, and 9c, as well as potent inhibition activity for the insulysin enzyme (IDE). Molecular docking stimulations were performed on CDK1/CyclinB1/CKS2 (PDB ID: 4y72) and BPTI (PDB ID: 2ra3). When docked into (PDB ID: 4y72), all of the tested compounds showed considerable inhibition, and the 2-pyridone derivative 9d had the maximum binding affinity (- 8.1223 kcal/mol). While thiophene derivative 6 offered the maximum binding affinity (- 7.5094 kcal/mol) when docked into (PDB ID: 2ra3).

Screening Marine Microbial Metabolites as Promising Inhibitors of Borrelia garinii: A Structural Docking Approach towards Developing Novel Lyme Disease Treatment

Lyme disease caused by the Borrelia species is a growing health concern in many parts of the world. Current treatments for the disease may have side effects, and there is also a need for new therapies that can selectively target the bacteria. Pathogens responsible for Lyme disease include B. burgdorferi, B. afzelii, and B. garinii. In this study, we employed structural docking-based screening to identify potential lead-like inhibitors against the bacterium. We first identified the core essential genome fraction of the bacterium, using 37 strains. Later, we screened a library of lead-like marine microbial metabolites (n = 4730) against the arginine deiminase (ADI) protein of Borrelia garinii. This protein plays a crucial role in the survival of the bacteria, and inhibiting it can kill the bacterium. The prioritized lead compounds demonstrating favorable binding energies and interactions with the active site of ADI were then evaluated for their drug-like and pharmacokinetic parameters to assess their suitability for development as drugs. Results from molecular dynamics simulation (100 ns) and other scoring parameters suggest that the compound CMNPD18759 (common name: aureobasidin; IUPAC name: 2-[(4R,6R)-4,6-dihydroxydecanoyl]oxypropan-2-yl (3S,5R)-3,5-dihydroxydecanoate) holds promise as a potential drug candidate for the treatment of Lyme disease, caused by B. garinii. However, further experimental studies are needed to validate the efficacy and safety of this compound in vivo.

An assessment system for clinical and biological interpretability in ulcerative colitis

Ulcerative colitis (UC) is a serious inflammatory bowel disease (IBD) with high morbidity and mortality worldwide. As the traditional diagnostic techniques have various limitations in the practice and diagnosis of early ulcerative colitis, it is necessary to develop new diagnostic models from molecular biology to supplement the existing methods. In this study, we developed a machine learning-based synthesis to construct an artificial intelligence diagnostic model for ulcerative colitis, and the correctness of the model is verified using an external independent dataset. According to the significantly expressed genes related to the occurrence of UC in the model, an unsupervised quantitative ulcerative colitis related score (UCRScore) based on principal coordinate analysis was established. The UCRScore is not only highly generalizable across UC bulk cohorts at different stages, but also highly generalizable across single-cell datasets, with the same effect in terms of cell numbers, activation pathways and mechanisms. As an important role of screening genes in disease occurrence, based on connectivity map analysis, 5 potential targeting molecular compounds were identified, which can be used as an additional supplement to the therapeutic of UC. Overall, this study provides a potential tool for differential diagnosis and assessment of bio-pathological changes in UC at the macroscopic level, providing an opportunity to optimize the diagnosis and treatment of UC.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Uncovering potential diagnostic and pathophysiological roles of α-synuclein and DJ-1 in melanoma

BACKGROUND

Melanoma, the most lethal skin cancer type, occurs more frequently in Parkinson's disease (PD), and PD is more frequent in melanoma patients, suggesting disease mechanisms overlap. α-synuclein, a protein that accumulates in PD brain, and the oncogene DJ-1, which is associated with PD autosomal recessive forms, are both elevated in melanoma cells. Whether this indicates melanoma progression or constitutes a protective response remains unclear. We hereby investigated the molecular mechanisms through which α-synuclein and DJ-1 interact, suggesting novel biomarkers and targets in melanoma.

METHODS

The Cancer Genome Atlas (TCGA) expression profiles derived from UCSC Xena were used to obtain α-synuclein and DJ-1 expression and correlated with survival in skin cutaneous melanoma (SKCM). Immunohistochemistry determined the expression in metastatic melanoma lymph nodes. Protein-protein interactions (PPIs) and molecular docking assessed protein binding and affinity with chemotherapeutic drugs. Further validation was performed using in vitro cellular models and ELISA immunoassays.

RESULTS

α-synuclein and DJ-1 were upregulated in primary and metastatic SKCM. Aggregated α-synuclein was selectively detected in metastatic melanoma lymph nodes. α-synuclein overexpression in SK-MEL-28 cells induced the expression of DJ-1, supporting PPI and a positive correlation in melanoma patients. Molecular docking revealed a stable protein complex, with differential binding to chemotherapy drugs such as temozolomide, dacarbazine, and doxorubicin. Parallel reduction of both proteins in temozolomide-treated SK-MEL-28 spheroids suggests drug binding may affect protein interaction and/or stability.

CONCLUSION

α-synuclein, together with DJ-1, may play a role in melanoma progression and chemosensitivity, constituting novel targets for therapeutic intervention, and possible biomarkers for melanoma.

Identification of Novel EGFR Inhibitors for the Targeted Therapy of Colorectal Cancer Using Pharmacophore Modelling, Docking, Molecular Dynamic Simulation and Biological Activity Prediction

BACKGROUND

Colorectal cancer (CRC) is considered the second deadliest cancer in the world. One of the reasons for the occurrence of this cancer is the deregulation of the Epidermal Growth Factor Receptor (EGFR), which plays a critical role in regulating cell division, persistence, differentiation, and migration. The overexpression of the EGFR protein leads to its dysregulation and causes CRC.

OBJECTIVES

Hence, this work aims to identify and validate novel EGFR inhibitors for the treatment of colorectal cancer employing various computer aided techniques such as pharmacophore modeling, docking, molecular dynamic simulation and Quantitative Structure-Activity Relationship (QSAR) analysis.

METHODS

In this work, a shared-featured ligand-based pharmacophore model was generated using the known inhibitors of EGFR. The best model was validated and screened against ZincPharmer and Maybridge databases, and 143 hits were obtained. Pharmacokinetic and toxicological properties of these hits were studied, and the acceptable ligands were docked against EGFR. The best five protein-ligand complexes with binding energy less than -5 kcal/mol were selected. The molecular dynamic simulation studies of these complexes were conducted for 100 nanoseconds (ns), and the results were analyzed. The biological activity of this ligand was calculated using QSAR analysis.

RESULTS

The best complex with Root Mean Square Deviation (RMSD) 3.429 Å and Radius of Gyration (RoG) 20.181 Å was selected. The Root Mean Square Fluctuations (RMSF) results were also found to be satisfactory. The biological activity of this ligand was found to be 1.38 μM.

CONCLUSION

This work hereby proposes the ligand 2-((1,6-dimethyl-4-oxo-1,4-dihydropyridin-3-yl)oxy)-N- (1H-indol-4-yl)acetamide as a potential EGFR inhibitor for the treatment of colorectal cancer. The wet lab analysis must be conducted, however, to confirm this hypothesis.

Identification of Fungicide Combinations for Overcoming Plasmopara viticola and Botrytis cinerea Fungicide Resistance

Fungal diseases, including downy mildew (caused by Plasmopara viticola) and gray mold (caused by Botrytis cinerea), significantly impact the marketable yield of grapes produced worldwide. Cytochrome b of the mitochondrial respiratory chain of these two fungi is a key target for Quinone outside inhibitor (QoI)-based fungicide development. Since the mode of action (MOA) of QoI fungicides is restricted to a single site, the extensive usage of these fungicides has resulted in fungicide resistance. The use of fungicide combinations with multiple targets is an effective way to counter and slow down the development of fungicide resistance. Due to the high cost of in planta trials, in silico techniques can be used for the rapid screening of potential fungicides. In this study, a combination of in silico simulations that include Schrödinger Glide docking, molecular dynamics, and Molecular Mechanism-Generalized Born Surface Area calculation were used to screen the most potent QoI and non-QoI-based fungicide combinations to wild-type, G143A-mutated, F129L-mutated, and double-mutated versions that had both G143A and F129L mutations of fungal cytochrome b. In silico docking studies indicated that mandestrobin, famoxadone, captan, and thiram have a high affinity toward WT cytochrome b of Botrytis cinerea. Although the QoIs mandestrobin and famoxadone were effective for WT based on in vitro results, they were not broadly effective against G143A-mutated isolates. Famoxadone was only effective against one isolate with G143A-mutated cytochrome b. The non-QoI fungicides thiram and captan were effective against both WT and isolates with G143A-mutated cytochrome b. Follow-up in silico docking and molecular dynamics studies suggested that fungicide combinations consisting of famoxadone, mandestrobin, fenamidone, and thiram should be considered in field testing targeting Plasmopara viticola and Botrytis cinerea fungicide resistance.

Computational modeling of in-stent restenosis: Pharmacokinetic and pharmacodynamic evaluation

Persistence of the pathology of in-stent restenosis even with the advent of drug-eluting stents warrants the development of highly resolved in silico models. These computational models assist in gaining insights into the transient biochemical and cellular mechanisms involved and thereby optimize the stent implantation parameters. Within this work, an already established fully-coupled Lagrangian finite element framework for modeling the restenotic growth is enhanced with the incorporation of endothelium-mediated effects and pharmacological influences of rapamycin-based drugs embedded in the polymeric layers of the current generation drug-eluting stents. The continuum mechanical description of growth is further justified in the context of thermodynamic consistency. Qualitative inferences are drawn from the model developed herein regarding the efficacy of the level of drug embedment within the struts as well as the release profiles adopted. The framework is then intended to serve as a tool for clinicians to tune the interventional procedures patient-specifically.

Quantitative Structure-Activity Relationship Modeling of Pea Protein-Derived Acetylcholinesterase and Butyrylcholinesterase Inhibitory Peptides

The aim of this work was to determine the structural requirements for peptides that inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) activities. The data set used consisted of 19 oligopeptides that had been identified through mass spectrometry analysis of enzymatic digests of yellow field pea protein. The structure-function relationship was analyzed by partial least squares regression using the 5z scores. A nine-component model was created from 16 peptides for AChE inhibitory peptides (Q2 = 67.2% and R2 = 0.9974), while three data sets were prepared for BuChE inhibitory peptides to improve the quality of the models (Q2 = 26.7-46.4% and R2 = 0.9577-0.9958). The most active peptides from the PLS models have threonine, leucine, alanine, and valine at the N terminal, asparagine, histidine, proline, and arginine at the second position, with aspartic acid and serine at the third, and arginine at the C terminal.

Molecular modelling of SdiA protein by selected flavonoid and terpenes compounds to attenuate virulence in Klebsiella pneumoniae

Klebsiella pneumoniae is one of the perturbing multidrug resistant (MDR) and ESKAPE pathogens contributing to the mounting morbidity, mortality and extended rate of hospitalization. Its virulence, often regulated by quorum sensing (QS) reinforces the need to explore alternative and prospective antivirulence agents, relatively from plants secondary metabolites. Computer aided drug discovery using molecular modelling techniques offers advantage to investigate prospective drugs to combat MDR pathogens. Thus, this study employed virtual screening of selected terpenes and flavonoids from medicinal plants to interrupt the QS associated SdiA protein in K. pneumoniae to attenuate its virulence. 4LFU was used as a template to model the structure of SdiA. ProCheck, Verify3D, Ramachandran plot scores, and ProSA-Web all attested to the model's good quality. Since SdiA protein in K. pneumoniae leads to the expression of virulence, 31 prospective bioactive compounds were docked for antagonistic potential. The stability of the protein-ligand complex, atomic motions and inter-atomic interactions were further investigated through molecular dynamics simulations (MDS) at 100 ns production runs. The binding free energy was estimated using the molecular mechanics/poisson-boltzmann surface area (MM/PB-SA). Furthermore, the drug-likeness properties of the studied compounds were validated. Docking studies showed phytol possesses the highest binding affinity (-9.205 kcal/mol) while glycitein had -9.752 kcal/mol highest docking score. The MDS of the protein in complex with the best-docked compounds revealed phytol with the highest binding energy of -44.2625 kcal/mol, a low root-mean-square deviation (RMSD) value of 1.54 Å and root-mean-square fluctuation (RMSF) score of 1.78 Å. Analysis of the drug-likeness properties prediction and bioavailability of these compounds revealed their conformed activity to lipinski's rules with bioavailability scores of 0.55 F. The studied terpenes and flavonoids compounds effectively thwart SdiA protein, therefore regulate inter- or intra cellular communication and associated in virulence Enterobacteriaceae, serving as prospective antivirulence drugs.Communicated by Ramaswamy H. Sarma.

Assessing the cytotoxicity of phenolic and terpene fractions extracted from Iraqi Prunus arabica on AMJ13 and SK-GT-4 human cancer cell lines

Background: Breast and esophageal cancer are the most aggressive and prominent causes of death worldwide. In addition, these cancers showed resistance to current chemotherapy regimens with limited success rates and fatal outcomes. Recently many studies reported the significant cytotoxic effects of phenolic and terpene fractions extracted from various Prunus species against different cancer cell lines. As a result, it has a good chance to be tested as a complement or replacement for standard chemotherapies. Methods: The study aimed to evaluate the cytotoxicity of phenolic and terpene fractions extracted from Iraqi Prunus arabica on breast (AMJ13) and esophageal (SK-GT-4) cancer cell lines by using the MTT assay (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide). Analysis using the Chou-Talalay method was performed to assess the synergistic effect between the extracted fractions and chemotherapeutic agent (docetaxel). Moreover, high-performance liquid chromatography (HPLC) analysis was conducted for the quantitative determination of different bioactive molecules of both phenolic and terpene fractions in the extract. Results: According to the findings, the treatment modalities significantly decreased cancer cell viability of AMJ13 and SK-GT-4 and had insignificant cytotoxicity on the normal cells (normal human fibroblast cell line) (all less than 50% cytotoxicity). Analysis with Chou-Talalay showed a strong synergism with docetaxel on both cancer cell lines (higher cytotoxicity even in low concentrations) and failed to induce cytotoxicity on the normal cells. Important flavonoid glycosides and terpenoids were detected by HPLC, in particularly, ferulic acid, catechin, chlorogenic acid, β-sitosterol, and campesterol. Conclusions: In conclusion, the extracted fractions selectively inhibited the proliferation of both cancer cell lines and showed minimal cytotoxicity on normal cells. These fractions could be naturally derived drugs for treating breast and esophageal cancers.

Rewiring Drug Research and Development through Human Data-Driven Discovery (HD3)

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through the pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather on the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, the rational design of combination therapies and the global response to the COVID-19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human-focused, systems-based approach to drug discovery and research.

Flavonoid as possible therapeutic targets against COVID-19: a scoping review of in silico studies

OBJECTIVES

This scoping review aims to present flavonoid compounds' promising effects and possible mechanisms of action on potential therapeutic targets in the SARS-CoV-2 infection process.

METHODS

A search of electronic databases such as PubMed and Scopus was carried out to evaluate the performance of substances from the flavonoid class at different stages of SARS-CoV-2 infection.

RESULTS

The search strategy yielded 382 articles after the exclusion of duplicates. During the screening process, 265 records were deemed as irrelevant. At the end of the full-text appraisal, 37 studies were considered eligible for data extraction and qualitative synthesis. All the studies used virtual molecular docking models to verify the affinity of compounds from the flavonoid class with crucial proteins in the replication cycle of the SARS-CoV-2 virus (Spike protein, PLpro, 3CLpro/ MPro, RdRP, and inhibition of the host's ACE II receptor). The flavonoids with more targets and lowest binding energies were: orientin, quercetin, epigallocatechin, narcissoside, silymarin, neohesperidin, delphinidin-3,5-diglucoside, and delphinidin-3-sambubioside-5-glucoside.

CONCLUSION

These studies allow us to provide a basis for in vitro and in vivo assays to assist in developing drugs for the treatment and prevention of COVID-19.

DeepCancerMap: A versatile deep learning platform for target- and cell-based anticancer drug discovery

Discovering new anticancer drugs has been widely concerned and remains an open challenge. Target- and phenotypic-based experimental screening represent two mainstream anticancer drug discovery methods, which suffer from time-consuming, labor-intensive, and high experimental costs. In this study, we collected 485,900 compounds involving in 3,919,974 bioactivity records against 426 anticancer targets and 346 cancer cell lines from academic literature, as well as 60 tumor cell lines from NCI-60 panel. A total of 832 classification models (426 target- and 406 cell-based predictive models) were then constructed to predict the inhibitory activity of compounds against targets and tumor cell lines using FP-GNN deep learning method. Compared to the classical machine learning and deep learning methods, the FP-GNN models achieve considerable overall predictive performance, with the highest AUC values of 0.91, 0.88, 0.91 for the test sets of targets, academia-sourced and NCI-60 cancer cell lines, respectively. A user-friendly webserver called DeepCancerMap and its local version were developed based on these high-quality models, enabling users to perform anticancer drug discovery-related tasks including large-scale virtual screening, profiling prediction of anticancer agents, target fishing, and drug repositioning. We anticipate this platform to accelerate the discovery of anticancer drugs in the field. DeepCancerMap is freely available at https://deepcancermap.idruglab.cn.

Assessing the cytotoxicity of phenolic and terpene fractions extracted from Iraqi Prunus arabica on AMJ13 and SK-GT-4 human cancer cell lines

Background: Breast and esophageal cancer are the most aggressive and prominent causes of death worldwide. In addition, these cancers showed resistance to current chemotherapy regimens with limited success rates and fatal outcomes. Recently many studies reported the significant cytotoxic effects of phenolic and terpene fractions extracted from various Prunus species against different cancer cell lines. As a result, it has a good chance to be tested as a complement or replacement for standard chemotherapies. Methods: The study aimed to evaluate the cytotoxicity of phenolic and terpene fractions extracted from Iraqi Prunus arabica on breast (AMJ13) and esophageal (SK-GT-4) cancer cell lines by using the MTT assay (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide). Analysis using the Chou-Talalay method was performed to assess the synergistic effect between the extracted fractions and chemotherapeutic agent (docetaxel). Moreover, high-performance liquid chromatography (HPLC) analysis was conducted for the quantitative determination of different bioactive molecules of both phenolic and terpene fractions in the extract. Results: According to the findings, the treatment modalities significantly decreased cancer cell viability of AMJ13 and SK-GT-4 and had insignificant cytotoxicity on the normal cells (normal human fibroblast cell line) (all less than 50% cytotoxicity). Analysis with Chou-Talalay showed a strong synergism with docetaxel on both cancer cell lines (higher cytotoxicity even in low concentrations) and failed to induce cytotoxicity on the normal cells. Important flavonoid glycosides and terpenoids were detected by HPLC, in particularly, ferulic acid, catechin, chlorogenic acid, β-sitosterol, and campesterol. Conclusions: In conclusion, the extracted fractions selectively inhibited the proliferation of both cancer cell lines and showed minimal cytotoxicity on normal cells. These fractions could be naturally derived drugs for treating breast and esophageal cancers.

A Review of Fibraurea tinctoria and Its Component, Berberine, as an Antidiabetic and Antioxidant

Diabetes mellitus is a group of metabolic disorders characterized by hyperglycemia caused by resistance to insulin action, inadequate insulin secretion, or excessive glucagon production. Numerous studies have linked diabetes mellitus and oxidative stress. People with diabetes usually exhibit high oxidative stress due to persistent and chronic hyperglycemia, which impairs the activity of the antioxidant defense system and promotes the formation of free radicals. Recently, several studies have focused on exploring natural antioxidants to improve diabetes mellitus. Fibraurea tinctoria has long been known as the native Borneo used in traditional medicine to treat diabetes. Taxonomically, this plant is part of the Menispermaceae family, widely known for producing various alkaloids. Among them are protoberberine alkaloids such as berberine. Berberine is an isoquinoline alkaloid with many pharmacological activities. Berberine is receiving considerable interest because of its antidiabetic and antioxidant activities, which are based on many biochemical pathways. Therefore, this review explores the pharmacological effects of Fibraurea tinctoria and its active constituent, berberine, against oxidative stress and diabetes, emphasizing its mechanistic aspects. This review also summarizes the pharmacokinetics and toxicity of berberine and in silico studies of berberine in several diseases and its protein targets.

Mangosteen Metabolites as Promising Alpha-Amylase Inhibitor Candidates: In Silico and In Vitro Evaluations

Diabetes is a chronic metabolic disorder characterized by raised glucose levels in the blood, resulting in grave damage over time to various body organs, including the nerves, heart, kidneys, eyes, and blood vessels. One of its therapeutic treatment approaches involves the inhibition of enzymes accountable for carbohydrate digestion and absorption. The present work is aimed at evaluating the potential of some reported metabolites from Garcinia mangostana (mangosteen, Guttiferae) as alpha-amylase inhibitors. Forty compounds were assessed for their capacity to inhibit alpha-amylase using in silico studies as well as in vitro assays. Molecular docking was carried out to analyze their binding capacities in the 3D structure of alpha-amylase (PDB ID: 4GQR). Among the tested compounds, 6-O-β-D-glucopyranosyl-2,4,6,3',4',6'-hexahydroxybenzophenone (8), aromadendrin-8-C-glucoside (5), epicatechin (6), rhodanthenone (4), and garcixanthone D (40) had a high XP G.score and a Glide G.score of -12.425, -11.855, -11.135, and -11.048 Kcal/mol, respectively. Compound 8 possessed the XP and Glide docking score of -12.425 Kcal/mol compared to the reference compounds myricetin and acarbose which had an XP and Glide docking score of -12.319 and 11.201 Kcal/mol, respectively. It interacted through hydrogen bond formations between its hydroxyl groups and the residues His 101, Asp 197, Glu 233, Asp 300, and His 305, in addition to water bridges and hydrophobic interactions. Molecular mechanics-generalized born surface area (MM-GBSA) was used to calculate the binding free energy and molecular dynamic studies that indicated the stability of the alpha-amylase-compound 8 complex during the 100 ns simulation in comparison with myricetin- and acarbose-alpha-amylase complexes. Additionally, the in vitro alpha-amylase inhibition assay findings validated the in silico study's findings. This could further validate the potential of G. mangostana as a candidate for diabetes management.

Nature-Derived Compounds as Potential Bioactive Leads against CDK9-Induced Cancer: Computational and Network Pharmacology Approaches

Given the importance of cyclin-dependent kinases (CDKs) in the maintenance of cell development, gene transcription, and other essential biological operations, CDK blockers have been generated to manage a variety of disorders resulting from CDK irregularities. Furthermore, CDK9 has a crucial role in transcription by regulating short-lived anti-apoptotic genes necessary for cancer cell persistence. Addressing CDK9 with blockers has consequently emerged as a promising treatment for cancer. This study scrutinizes the effectiveness of nature-derived compounds (geniposidic acid, quercetin, geniposide, curcumin, and withanolide C) against CDK9 through computational approaches. A molecular docking study was performed after preparing the protein and the ligands. The selected blockers of the CDK9 exerted reliable binding affinities (−8.114 kcal/mol to −13.908 kcal/mol) against the selected protein, resulting in promising candidates compared to the co-crystallized ligand (LCI). The binding affinity of geniposidic acid (−13.908 kcal/mol) to CDK9 is higher than quercetin (−10.775 kcal/mol), geniposide (−9.969 kcal/mol), curcumin (−9.898 kcal/mol), withanolide C (−8.114 kcal/mol), and the co-crystallized ligand LCI (−11.425 kcal/mol). Therefore, geniposidic acid is a promising inhibitor of CDK9. Moreover, the molecular dynamics studies assessed the structure–function relationships and protein–ligand interactions. The network pharmacology study for the selected ligands demonstrated the auspicious compound–target–pathway signaling pathways vital in developing tumor, tumor cell growth, differentiation, and promoting tumor cell progression. Moreover, this study concluded by analyzing the computational approaches the natural-derived compounds that have potential interacting activities against CDK9 and, therefore, can be considered promising candidates for CKD9-induced cancer. To substantiate this study’s outcomes, in vivo research is recommended.

A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery

Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.

Small Molecular Drug Screening Based on Clinical Therapeutic Effect

Virtual screening can significantly save experimental time and costs for early drug discovery. Drug multi-classification can speed up virtual screening and quickly predict the most likely class for a drug. In this study, 1019 drug molecules with actual therapeutic effects are collected from multiple databases and documents, and molecular sets are grouped according to therapeutic effect and mechanism of action. Molecular descriptors and molecular fingerprints are obtained through SMILES to quantify molecular structures. After using the Kennard–Stone method to divide the data set, a better combination can be obtained by comparing the combined results of five classification algorithms and a fusion method. Furthermore, for a specific data set, the model with the best performance is used to predict the validation data set. The test set shows that prediction accuracy can reach 0.862 and kappa coefficient can reach 0.808. The highest classification accuracy of the validation set is 0.873. The more reliable molecular set has been found, which could be used to predict potential attributes of unknown drug compounds and even to discover new use for old drugs. We hope this research can provide a reference for virtual screening of multiple classes of drugs at the same time in the future.

Functional divergence of oligoadenylate synthetase 1 (OAS1) proteins in Tetrapods

OASs play critical roles in immune response against virus infection by polymerizing ATP into 2-5As, which initiate the classical OAS/RNase L pathway and induce degradation of viral RNA. OAS members are functionally diverged in four known innate immune pathways (OAS/RNase L, OASL/IRF7, OASL/RIG-I, and OASL/cGAS), but how they functionally diverged is unclear. Here, we focus on evolutionary patterns and explore the link between evolutionary processes and functional divergence of Tetrapod OAS1. We show that Palaeognathae and Primate OAS1 genes are conserved in genomic and protein structures but differ in function. The former (i.e., ostrich) efficiently synthesized long 2-5A and activated RNase L, while the latter (i.e., human) synthesized short 2-5A and did not activate RNase L. We predicted and verified that two in-frame indels and one positively selected site in the active site pocket contributed to the functional divergence of Palaeognathae and Primate OAS1. Moreover, we discovered and validated that an in-frame indel in the C-terminus of Palaeognathae OAS1 affected the binding affinity of dsRNA and enzymatic activity, and contributed to the functional divergence of Palaeognathae OAS1 proteins. Our findings unravel the molecular mechanism for functional divergence and give insights into the emergence of novel functions in Tetrapod OAS1.

A narrative review: The pharmaceutical evolution of phenolic syringaldehyde

To better understand the pharmacological characters of syringaldehyde (SA), which is a key-odorant compound of whisky and brandy, this review article is the first to compile the published literature for molecular docking that were subsequently validated by in vitro and in vivo assays to predict and develop insights into the medicinal properties of SA in terms of anti-oxidation, anti-inflammation, and anti-diabetes. The molecular docking displayed significantly binding affinity for SA towards tumor necrosis factor-α, interleukin-6, and antioxidant enzymes when inflammation from myocardial infarction and spinal cord ischemia. Moreover, SA nicely docked with dipeptidyl peptidase-IV, glucagon-like peptide 1 receptor, peroxisome proliferator-activated receptor, acetylcholine M2 receptor, and acetylcholinesterase in anti-diabetes investigations. These are associated with (1) an increase glucose utilization and insulin sensitivity to an anti-hyperglycemic effect; and (2) to potentiate intestinal contractility to abolish the α-amylase reaction when concurrently reducing retention time and glucose absorption of the intestinal tract to achieve a glucose-lowering effect. In silico screening of multi-targets concomitantly with preclinical tests could provide a potential exploration for new indications for drug discovery and development.

Network Pharmacology Approach for Medicinal Plants: Review and Assessment

Natural products have played a critical role in medicine due to their ability to bind and modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive scaffolds for the treatment of multiple disorders. The less adverse effects, affordability, and easy accessibility highlight their potential in traditional remedies. Identifying pharmacological targets from active ingredients of medicinal plants has become a hot topic for biomedical research to generate innovative therapies. By developing an unprecedented opportunity for the systematic investigation of traditional medicines, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. The advancement of network pharmacology has opened up new avenues for understanding the complex bioactive components found in various medicinal plants. This study is attributed to a comprehensive summary of network pharmacology based on current research, highlighting various active ingredients, related techniques/tools/databases, and drug discovery and development applications. Moreover, this study would serve as a protocol for discovering novel compounds to explore the full range of biological potential of traditionally used plants. We have attempted to cover this vast topic in the review form. We hope it will serve as a significant pioneer for researchers working with medicinal plants by employing network pharmacology approaches.

In silico approach for the development of novel antiviral compounds based on SARS-COV-2 protease inhibition

The COVID-19 pandemic emerged in 2019, bringing with it the need for greater stores of effective antiviral drugs. This paper deals with the conformation-independent, QSAR model, developed by employing the Monte Carlo optimization method, as well as molecular graphs and the SMILES notation-based descriptors for the purpose of modeling the SARS-CoV-3CLpro enzyme inhibition. The main purpose was developing a reproducible model involving easy interpretation, utilized for a quick prediction of the inhibitory activity of SAR-CoV-3CLpro. The following statistical parameters were present in the best-developed QSAR model: (training set) R 2 = 0.9314, Q 2 = 0.9271; (test set) R 2 = 0.9243, Q 2 = 0.8986. Molecular fragments, defined as SMILES notation descriptors, that have a positive and negative impact on 3CLpro inhibition were identified on the basis of the results obtained for structural indicators, and were applied to the computer-aided design of five new compounds with (4-methoxyphenyl)[2-(methylsulfanyl)-6,7-dihydro-1H-[1,4]dioxino[2,3-f]benzimidazol-1-yl]methanone as a template molecule. Molecular docking studies were used to examine the potential inhibition effect of designed molecules on SARS-CoV-3CLpro enzyme inhibition and obtained results have high correlation with the QSAR modeling results. In addition, the interactions between the designed molecules and amino acids from the 3CLpro active site were determined, and the energies they yield were calculated.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11696-022-02170-8.

Identification of difluorinated curcumin molecular targets linked to traumatic brain injury pathophysiology

Traumatic brain injury (TBI) affects approximately 50% of the world population at some point in their lifetime. To date, there are no effective treatments as most of the damage occurs due to secondary effects through a variety of pathophysiological pathways. The phytoceutical curcumin has been traditionally used as a natural remedy for numerous conditions including diabetes, inflammatory diseases, and neurological and neurodegenerative disorders. We have carried out a system pharmacology study to identify potential targets of a difluorinated curcumin analogue (CDF) that overlap with those involved in the pathophysiological mechanisms of TBI. This resulted in identification of 312 targets which are mostly involved in G protein-coupled receptor activity and cellular signalling. These include adrenergic, serotonergic, opioid and cannabinoid receptor families, which have been implicated in regulation of pain, inflammation, mood, learning and cognition pathways. We conclude that further studies should be performed to validate curcumin as a potential novel treatment to ameliorate the effects of TBI.

Role of gut microbiota metabolism and biotransformation on dietary natural products to human health implications with special reference to biochemoinformatics approach

Gut microbiota contributes to diverse mammalian processes including the metabolic functions of drugs. It is a potential new territory for drug targeting, especially for dietary natural compounds such as tannins, flavonoids, steroidal glycosides, anthocyanins, lignans, alkaloids, and others. Because most herbal medicines are orally administered, the chemical profile and corresponding bioactivities of herbal medicines may be altered and implication to ailments by specific microbiota through gut microbiota metabolisms (GMMs) and gut microbiota biotransformations (GMBTs). In this review, briefly introducing the interactions between different categories of natural compounds and gut microbiota produced countless microbial degraded or fragmented metabolites with their biological significance in rodent-based models. From natural product chemistry division, thousands of molecules are produced, degraded, synthesized, and isolated from natural sources but exploited due to lack of biological significance. In this direction, we add a Bio-Chemoinformatics approach to get clues of biology through a specific microbial assault to (Natural products) NPs.

Statistical methods for in silico tools used for risk assessment and toxicology

In silico toxicology is one type of toxicity assessment that uses computational methods to visualize, analyze, simulate, and predict the toxicity of chemicals. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. Animal studies for the type of toxicological information needed are both expensive and time-consuming, and to that, ethical consideration is added. Many different types of in silico methods have been developed to characterize the toxicity of chemical materials and predict their catastrophic consequences to humans and the environment. In light of European legislation such as Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) and the Cosmetics Regulation, in silico methods for predicting chemical toxicity have become increasingly important and used extensively worldwide e.g., in the USA, Canada, Japan, and Australia. A popular problem, concerning these methods, is the deficiency of the necessary data for assessing the hazards. REACH has called for increased use of in silico tools for non-testing data as structure-activity relationships, quantitative structure-activity relationships, and read-across. The main objective of the review is to refine the use of in silico tools in a risk assessment context of industrial chemicals.

In Silico Approaches to Identify Polyphenol Compounds as α-Glucosidase and α-Amylase Inhibitors against Type-II Diabetes

Type-II diabetes mellitus (T2DM) results from a combination of genetic and lifestyle factors, and the prevalence of T2DM is increasing worldwide. Clinically, both α-glucosidase and α-amylase enzymes inhibitors can suppress peaks of postprandial glucose with surplus adverse effects, leading to efforts devoted to urgently seeking new anti-diabetes drugs from natural sources for delayed starch digestion. This review attempts to explore 10 families e.g., Bignoniaceae, Ericaceae, Dryopteridaceae, Campanulaceae, Geraniaceae, Euphorbiaceae, Rubiaceae, Acanthaceae, Rutaceae, and Moraceae as medicinal plants, and folk and herb medicines for lowering blood glucose level, or alternative anti-diabetic natural products. Many natural products have been studied in silico, in vitro, and in vivo assays to restrain hyperglycemia. In addition, natural products, and particularly polyphenols, possess diverse structures for exploring them as inhibitors of α-glucosidase and α-amylase. Interestingly, an in silico discovery approach using natural compounds via virtual screening could directly target α-glucosidase and α-amylase enzymes through Monte Carto molecular modeling. Autodock, MOE-Dock, Biovia Discovery Studio, PyMOL, and Accelrys have been used to discover new candidates as inhibitors or activators. While docking score, binding energy (Kcal/mol), the number of hydrogen bonds, or interactions with critical amino acid residues have been taken into concerning the reliability of software for validation of enzymatic analysis, in vitro cell assay and in vivo animal tests are required to obtain leads, hits, and candidates in drug discovery and development.

Towards machine learning discovery of dual antibacterial drug-nanoparticle systems

Artificial Intelligence/Machine Learning (AI/ML) algorithms may speed up the design of DADNP systems formed by Antibacterial Drugs (AD) and Nanoparticles (NP). In this work, we used IFPTML = Information Fusion (IF) + Perturbation-Theory (PT) + Machine Learning (ML) algorithm for the first time to study of a large dataset of putative DADNP systems composed by >165 000 ChEMBL AD assays and 300 NP assays vs. multiple bacteria species. We trained alternative models with Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), Bayesian Networks (BNN), K-Nearest Neighbour (KNN) and other algorithms. IFPTML-LDA model was simpler with values of Sp ≈ 90% and Sn ≈ 74% in both training (>124 K cases) and validation (>41 K cases) series. IFPTML-ANN and KNN models are notably more complicated even when they are more balanced Sn ≈ Sp ≈ 88.5%-99.0% and AUROC ≈ 0.94-0.99 in both series. We also carried out a simulation (>1900 calculations) of the expected behavior for putative DADNPs in 72 different biological assays. The putative DADNPs studied are formed by 27 different drugs with multiple classes of NP and types of coats. In addition, we tested the validity of our additive model with 80 DADNP complexes experimentally synthetized and biologically tested (reported in >45 papers). All these DADNPs show values of MIC < 50 μg mL^-1 (cutoff used) better that MIC of AD and NP alone (synergistic or additive effect). The assays involve DADNP complexes with 10 types of NP, 6 coating materials, NP size range 5-100 nm vs. 15 different antibiotics, and 12 bacteria species. The IFPTML-LDA model classified correctly 100% (80 out of 80) DADNP complexes as biologically active. IFPMTL additive strategy may become a useful tool to assist the design of DADNP systems for antibacterial therapy taking into consideration only information about AD and NP components by separate.

Engineering of Ocriplasmin Variants by Bioinformatics Methods for the Reduction of Proteolytic and Autolytic Activities

BACKGROUND

Ocriplasmin has been developed for the induction of posterior vitreous detachment in patients with vitreomacular adhesion. At physiological pH, ocriplasmin is susceptible to autolytic and proteolytic degradation, limiting its activity duration. These undesirable properties of ocriplasmin can be reduced by site-directed mutagenesis, so that its enzymatic activities can be augmented. This study aimed to design ocriplasmin variants with improved biological/physicochemical characteristics via bioinformatics tools.

METHODS

This study was performed in Tabriz University of Medical Sciences, Tabriz, Iran, 2019. Through site-directed mutagenesis, three ocriplasmin variants were designed. Structural analysis was performed on the wild-type variant and the mutant variants using the Protein Interactions Calculator (PIC) server. The interactions between the S-2403 substrate and the ocriplasmin variants were studied by molecular docking simulations, and binding capability was evaluated by the calculation of free binding energy. The conformational features of protein-substrate complex systems for all the variants were evaluated using molecular dynamic simulations at 100 nanoseconds.

RESULTS

The structural analysis of ocriplasmin revealed that the substitution of threonine for alanine 59 significantly reduced proteolytic activity, while the substitution of glutamic acid for lysine 156 influenced autolytic function. The molecular docking simulation results indicated the appropriate binding of the substrate to the ocriplasmin variants with high-to-low affinities. The binding affinity of the wild-type variant for the substrate was higher than that between the mutant variants and the substrate. Simulation analyses, consisting of the root-mean-square deviation, the root-mean-square fluctuation, and the center-of-mass average distance showed a higher affinity of the substrate for the wild type than for the mutant variants.

CONCLUSION

The mutational analysis of ocriplasmin revealed that A59T and K156E mutagenesis could be used for the development of a new variant with higher therapeutic efficacy.

In Silico Screening of Semi-Synthesized Compounds as Potential Inhibitors for SARS-CoV-2 Papain-like Protease: Pharmacophoric Features, Molecular Docking, ADMET, Toxicity and DFT Studies

Papain-like protease is an essential enzyme in the proteolytic processing required for the replication of SARS-CoV-2. Accordingly, such an enzyme is an important target for the development of anti-SARS-CoV-2 agents which may reduce the mortality associated with outbreaks of SARS-CoV-2. A set of 69 semi-synthesized molecules that exhibited the structural features of SARS-CoV-2 papain-like protease inhibitors (PLPI) were docked against the coronavirus papain-like protease (PLpro) enzyme (PDB ID: (4OW0). Docking studies showed that derivatives 34 and 58 were better than the co-crystallized ligand while derivatives 17, 28, 31, 40, 41, 43, 47, 54, and 65 exhibited good binding modes and binding free energies. The pharmacokinetic profiling study was conducted according to the four principles of the Lipinski rules and excluded derivative 31. Furthermore, ADMET and toxicity studies showed that derivatives 28, 34, and 47 have the potential to be drugs and have been demonstrated as safe when assessed via seven toxicity models. Finally, comparing the molecular orbital energies and the molecular electrostatic potential maps of 28, 34, and 47 against the co-crystallized ligand in a DFT study indicated that 28 is the most promising candidate to interact with the target receptor (PLpro).

Pseudomonas aeruginosa Biofilm Formation and Its Control

Microbes are hardly seen as planktonic species and are most commonly found as biofilm communities in cases of chronic infections. Biofilms are regarded as a biological condition, where a large group of microorganisms gets adhered to a biotic or abiotic surface. In this context, Pseudomonas aeruginosa, a Gram-negative nosocomial pathogen is the main causative organism responsible for life-threatening and persistent infections in individuals affected with cystic fibrosis and other lung ailments. The bacteria can form a strong biofilm structure when it adheres to a surface suitable for the development of a biofilm matrix. These bacterial biofilms pose higher natural resistance to conventional antibiotic therapy due to their multiple tolerance mechanisms. This prevailing condition has led to an increasing rate of treatment failures associated with P. aeruginosa biofilm infections. A better understanding of the effect of a diverse group of antibiotics on established biofilms would be necessary to avoid inappropriate treatment strategies. Hence, the search for other alternative strategies as effective biofilm treatment options has become a growing area of research. The current review aims to give an overview of the mechanisms governing biofilm formation and the different strategies employed so far in the control of biofilm infections caused by P. aeruginosa. Moreover, this review can also help researchers to search for new antibiofilm agents to tackle the effect of biofilm infections that are currently imprudent to conventional antibiotics.

Exposure-response modeling improves selection of radiation and radiosensitizer combinations

A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.

Potential of MurA Enzyme and GBAP in Fsr Quorum Sensing System as Antibacterial Drugs Target: In vitro and In silico Study of Antibacterial Compounds from Myrmecodia pendans

Background

Increasing the resistance issue has become the reason for the development of new antibacterial in crucial condition. Many ways are tracked to determine the most effective antibacterial agent. Some proteins that are a key role in bacteria metabolism are targeted, including MurA in cell wall biosynthesis and gelatinase biosynthesis-activating pheromone (GBAP) in Fsr Quorum Sensing (QS) system.

Objective

The objective of this research is the analysis of compounds 1-4 from M. pendans as antibacterial and anti-QS activity trough protein inhibition by in silico study; focus on the structure-activity relationships, to appraise their role as an antibacterial and anti-QS agent in the molecular level.

Methods

Both activities of M. pendans compounds (1-4) were analyzed by in silico, compared to Fosfomycin, Ambuic acid, Quercetin, and Taxifolin as a standard. Chemical structures of M. pendans compounds were converted using an online program molview. The compounds were docked to MurA, GBAP, gelatinase and serine protease using Autodock Vina in Pyrx 0.8 followed PYMOL to visualization and proteis.plus program to analyze of the complex.

Results

All compounds from M. pendans bound on MurA, GBAP, gelatinase and serine protease except compound 2. This biflavonoid did not attach to MurA and serine protease yet is the favorable ligand for GBAP and gelatinase with the binding affinity of -6.9 and -9.4 Kcal/mol respectively. Meanwhile, for MurA and serine protease, compound 4 is the highest of bonding energy with values of -8.7 and -6.4 Kcal/mol before quercetin (MurA, -8.9 Kcal/mol) and taxifolin (serine protease, -6.6 Kcal/mol).

Conclusion

Based on the data, biflavonoid acts better as anti-QS than an inhibitor of MurA enzyme while the others can be acted into both of them either the therapeutic agent of anti-QS or antibacterial agent of MurA inhibitor.