New machine learning and physics-based scoring functions for drug discovery

Convergent Protocols for Computing Protein-Ligand Interaction Energies Using Fragment-Based Quantum Chemistry

Fragment-based quantum chemistry methods offer a means to sidestep the steep nonlinear scaling of electronic structure calculations so that large molecular systems can be investigated using high-level methods. Here, we use fragmentation to compute protein-ligand interaction energies in systems with several thousand atoms, using a new software platform for managing fragment-based calculations that implements a screened many-body expansion. Convergence tests using a minimal-basis semiempirical method (HF-3c) indicate that two-body calculations, with single-residue fragments and simple hydrogen caps, are sufficient to reproduce interaction energies obtained using conventional supramolecular electronic structure calculations, to within 1 kcal/mol at about 1% of the computational cost. We also demonstrate that the HF-3c results are illustrative of trends obtained with density functional theory in basis sets up to augmented quadruple-ζ quality. Strategic deployment of fragmentation facilitates the use of converged biomolecular model systems alongside high-quality electronic structure methods and basis sets, bringing ab initio quantum chemistry to systems of hitherto unimaginable size. This will be useful for generation of high-quality training data for machine learning applications.

Integrating structure-guided and fragment-based inhibitor design to combat bedaquiline resistant Mycobacterium tuberculosis: a molecular dynamics study

The first FDA approved, MDR-TB inhibitory drug bedaquiline (BDQ), entraps the c-ring of the proton-translocating F0 region of enzyme ATP synthase of Mycobacterium tuberculosis, thus obstructing successive ATP production. Present-day BDQ-resistance has been associated with cardiotoxicity and mutation(s) in the atpE gene encoding the c subunit of ATP synthase (ATPc) generating five distinct ATPc mutants: Ala63→Pro, Ile66→Met, Asp28→Gly, Asp28→Val and Glu61→Asp. We created three discrete libraries, first by repurposing bedaquiline via scaffold hopping approach, second one having natural plant compounds and the third being experimentally derived analogues of BDQ to identify one drug candidate that can inhibit ATPc activity more efficiently with less toxic properties. For this purpose, we adopted techniques like molecular dynamics simulation, virtual screening, PCA, DCCM, binding affinity analysis to gauge structure-function relationship of the L136-ATPc complexes. L136 was found to induce a distinguishable conformational change in the bound ATPc which captivated the c9 rotor ring. L136 displays a binding free energy of -57.294, -59.027, -57.273, -58.726, -55.889 and -58.651 kcal/mol for ATPc_WT and the five respective mutants. The pIC50 value for the L136 ligand for the same proteins was unveiled to be 6.760, 7.285, 6.898, 7.222, 6.987 and 7.687. Moreover, L136 exhibited a strong ADMET profile. Furthermore, we discovered that the change in the hydrophobic platform in ATPc mutants hinders BDQ binding, which is overcome by L136, ensuring efficient binding and providing an assessment of L136's mechanism of ATPc inhibition. L136 provides a scope for in vivo test for future clinical drug trials.

Zileuton, a 5-Lypoxigenase Inhibitor, is Antiparasitic and Prevents Inflammation in the Chronic Stage of Heart Chagas Disease

Chronic Chagas cardiomyopathy is associated with an unbalanced immune response and impaired heart function, and available drugs do not prevent its development. Zileuton (Zi), a 5-lypoxigenase inhibitor, affects inflammatory/pro-resolution mediators. Herein, Zi treatment in the early phase of infection reduced parasitemia associated mainly with the direct effect of Zi on the parasite, and the enzyme epoxide hydrolase was the potential molecular target behind the trypanocidal effect. In the intermediate acute phase of infection, Zi reduced the number of innate and adaptive inflammatory cells, increased the level of SOCS2 expression in the heart associated with lower inflammation, and improved cardiac function. Zi treatment initiated in the chronic stage increased the level of SOCS2 expression in the heart, reduced inflammation, and improved cardiac function. Our data suggest that Zi protects against Trypanosoma cruzi infection by acting directly on the parasite and reducing heart damage and is a promising option for the treatment of Chagas disease.

Improved Prediction of Ligand-Protein Binding Affinities by Meta-modeling

The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between ligands and proteins. Many computational models for binding affinity prediction have been developed, but with varying results across targets. Given that ensembling or meta-modeling approaches have shown great promise in reducing model-specific biases, we develop a framework to integrate published force-field-based empirical docking and sequence-based deep learning models. In building this framework, we evaluate many combinations of individual base models, training databases, and several meta-modeling approaches. We show that many of our meta-models significantly improve affinity predictions over base models. Our best meta-models achieve comparable performance to state-of-the-art deep learning tools exclusively based on 3D structures while allowing for improved database scalability and flexibility through the explicit inclusion of features such as physicochemical properties or molecular descriptors. We further demonstrate improved generalization capability by our models using a large-scale benchmark of affinity prediction as well as a virtual screening application benchmark. Overall, we demonstrate that diverse modeling approaches can be ensembled together to gain meaningful improvement in binding affinity prediction.

Computational Assessment of the Phytochemicals of Panax ginseng C.A. Meyer Against Dopamine Receptor D1 for Early Huntington's Disease Prophylactics

A herb, Panax ginseng C.A. Meyer has been used traditionally for the treatment of various diseases. In this work, its chemical components have been explored by computational methods for the possibility of therapeutic potential against early Huntington's disease. The molecular docking calculations against dopamine receptor D1 (PDB ID: 7X2F) involved in pathogenesis of early Huntington's disease gave the binding affinities (kcal/mol) of schizandrin (-10.530), ergosterol (-10.124), protopanaxadiol (-9.650), panaxydol (-9.399), diphenhydramine (-9.358), and panasenoside (-9.358). The values for native ligand (-7.748) and some selected drugs, Nefazodone (-9.880), Risperidone (-9.752), and Haloperidol (-9.712) were higher revealing weaker interactions. The stability assessment of top protein-ligand adducts in terms of various geometrical and thermodynamical parameters extracted from 200 ns molecular dynamics simulations pointed to schizandrin, protopanaxadiol, and panasenoside as hit molecules. The minimal translational and rotational motion of the docked ligands at orthosteric pocket of the receptor at near physiological conditions hinted at the probability of it restricting or inhibiting over-activation of DRD1. The sustained thermodynamic spontaneity of complex formation reaction augmented the inferences derived from spatial results. The phytochemicals from Panax ginseng could be used in the prophylactics of early Huntington's disease and recommendation is made for further evaluation by experimental work.

Brazilian red propolis reduces the adhesion of oral biofilm cells and the Toxoplasma gondii intracellular proliferation

Infectious diseases remain as a significant cause of thousands of deaths annually worldwide. Therefore, this study aimed to investigate the antimicrobial and antiparasitic activity of the crude hydroalcoholic extract and compounds isolated from Brazilian Red Propolis (BRP) against oral pathogens and Toxoplasma gondii, using in vitro, in vivo and in silico approaches. Antimicrobial and synergistic activities were determined using the broth dilution method and the checkerboard assay, respectively. Antibiofilm activity was evaluated by staining with 2 % crystal violet and counting microorganisms. In vivo infection was carried out in Caenorhabditis elegans AU37 larvae and in silico analysis was performed using molecular docking simulations. The effect on growth modulation of T. gondii was evaluated through a β-galactosidase colorimetric assay. Minimum Inhibitory Concentration values ranged from 3.12 to 400 µg/mL. Biofilm Minimum Inhibitory Concentration (MICB50) values ranged from 6.25 to 375 µg/mL, with a significant reduction in the number of viable cells. Furthermore, Guttiferone E and the crude extract reduced cell aggregation and caused damage to the biofilm cell wall. The highest concentrations of the crude extract and Guttiferone E increased the survival and reduced the risk of death of infected and treated larvae. Guttiferone E and Oblongifolin B inhibited the intracellular proliferation of T. gondii and demonstrated several targets of action against bacteria and T. gondii through in silico analysis. These data demonstrate that BRP has antimicrobial and antiparasitic activity against pathogens of clinical relevance, and can be used in the future as phytomedicines.

Design and investigation of interactions of novel peptide conjugates of purine and pyrimidine derivatives with EGFR and its mutant T790M/L858R: an in silico and laboratory study

Peptide-based therapeutics have been gaining attention due to their ability to actively target tumor cells. Additionally, several varieties of nucleotide derivatives have been developed to reduce cell proliferation and induce apoptosis of tumor cells. In this work, we have developed novel peptide conjugates with newly designed purine analogs and pyrimidine derivatives and explored the binding interactions with the kinase domain of wild-type EGFR and its mutant EGFR [L858R/ T790M] which are known to be over-expressed in tumor cells. The peptides explored included WNWKV (derived from sea cucumber) and LARFFS, which in previous work was predicted to bind to Domain I of EGFR. Computational studies conducted to explore binding interactions include molecular docking studies, molecular dynamics simulations and MMGBSA to investigate the binding abilities and stability of the complexes. The results indicate that conjugation enhanced binding capabilities, particularly for the WNWKV conjugates. MMGBSA analysis revealed nearly twofold higher binding toward the T790M/L858R double mutant receptor. Several conjugates were shown to have strong and stable binding with both wild-type and mutant EGFR. As a proof of concept, we synthesized pyrimidine conjugates with both peptides and determined the KD values using SPR analysis. The results corroborated with the computational analyses. Additionally, cell viability and apoptosis studies with lung cancer cells expressing the wild-type and double mutant proteins revealed that the WNWKV conjugate showed greater potency than the LARFFS conjugate, while LARFFS peptide alone showed poor binding to the kinase domain. Thus, we have designed peptide conjugates that show potential for further laboratory studies for developing therapeutics for targeting the EGFR receptor and its mutant T790M/L858R.

Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph

The investigation of molecular interactions between ligands and their target molecules is becoming more significant as protein structure data continues to develop. In this study, we introduce PLA-STGCNnet, a deep fusion spatial-temporal graph neural network designed to study protein-ligand interactions based on the 3D structural data of protein-ligand complexes. Unlike 1D protein sequences or 2D ligand graphs, the 3D graph representation offers a more precise portrayal of the complex interactions between proteins and ligands. Research studies have shown that our fusion model, PLA-STGCNnet, outperforms individual algorithms in accurately predicting binding affinity. The advantage of a fusion model is the ability to fully combine the advantages of multiple different models and improve overall performance by combining their features and outputs. Our fusion model shows satisfactory performance on different data sets, which proves its generalization ability and stability. The fusion-based model showed good performance in protein-ligand affinity prediction, and we successfully applied the model to drug screening. Our research underscores the promise of fusion spatial-temporal graph neural networks in addressing complex challenges in protein-ligand affinity prediction. The Python scripts for implementing various model components are accessible at https://github.com/ligaili01/PLA-STGCN.

EuDockScore: Euclidean graph neural networks for scoring protein-protein interfaces

MOTIVATION

Protein-protein interactions are essential for a variety of biological phenomena including mediating biochemical reactions, cell signaling, and the immune response. Proteins seek to form interfaces which reduce overall system energy. Although determination of single polypeptide chain protein structures has been revolutionized by deep learning techniques, complex prediction has still not been perfected. Additionally, experimentally determining structures is incredibly resource and time expensive. An alternative is the technique of computational docking, which takes the solved individual structures of proteins to produce candidate interfaces (decoys). Decoys are then scored using a mathematical function that assess the quality of the system, known as scoring functions. Beyond docking, scoring functions are a critical component of assessing structures produced by many protein generative models. Scoring models are also used as a final filtering in many generative deep learning models including those that generate antibody binders, and those which perform docking.

RESULTS

In this work, we present improved scoring functions for protein-protein interactions which utilizes cutting-edge Euclidean graph neural network architectures, to assess protein-protein interfaces. These Euclidean docking score models are known as EuDockScore, and EuDockScore-Ab with the latter being antibody-antigen dock specific. Finally, we provided EuDockScore-AFM a model trained on antibody-antigen outputs from AlphaFold-Multimer (AFM) which proves useful in reranking large numbers of AFM outputs.

AVAILABILITY AND IMPLEMENTATION

The code for these models is available at https://gitlab.com/mcfeemat/eudockscore.

Translational Perspectives on the Therapeutic Potential of Hyptis Crenata Essential Oil Terpenes in Smooth Muscle Function

Monoterpenes and sesquiterpenes are classes of organic compounds found in various natural products, such as the essential oil of Hyptis crenata (EOHc). The therapeutic potential of these terpenes present in EOHc is evidenced by their effect on smooth muscle and potential clinical applications. Among the highlighted monoterpenes, such as sabinene, α-pinene, and β-pinene, a relaxing effect on rat intestinal smooth muscles is observed, attributed to interaction with calcium channels. Furthermore, monoterpenoids like borneol, cineole, and linalool also demonstrate vasorelaxant properties, suggesting potential in the treatment of cardiovascular conditions. Sesquiterpenes, such as caryophyllene and aromadendrenes, exhibit relaxing effects in various smooth muscle tissues, such as rat uterus and guinea pig ileum, indicating pharmacological potential in these areas. The translational exploration of targets, such as calcium channels and G protein-coupled receptors, highlights the importance of these compounds in discovering new therapies based on natural products for treating various medical conditions.

Potent Biological Activity of Fluorinated Derivatives of 2-Deoxy-d-Glucose in a Glioblastoma Model

BACKGROUND

One defining feature of various aggressive cancers, including glioblastoma multiforme (GBM), is glycolysis upregulation, making its inhibition a promising therapeutic approach. One promising compound is 2-deoxy-d-glucose (2-DG), a d-glucose analog with high clinical potential due to its ability to inhibit glycolysis. Upon uptake, 2-DG is phosphorylated by hexokinase to 2-DG-6-phosphate, which inhibits hexokinase and downstream glycolytic enzymes. Unfortunately, therapeutic use of 2-DG is limited by poor pharmacokinetics, suppressing its efficacy.

METHODS

To address these issues, we synthesized novel halogenated 2-DG analogs (2-FG, 2,2-diFG, 2-CG, and 2-BG) and evaluated their glycolytic inhibition in GBM cells. Our in vitro and computational studies suggest that these derivatives modulate hexokinase activity differently.

RESULTS

Fluorinated compounds show the most potent cytotoxic effects, indicated by the lowest IC50 values. These effects were more pronounced in hypoxic conditions. 19F NMR experiments and molecular docking confirmed that fluorinated derivatives bind hexokinase comparably to glucose. Enzymatic assays demonstrated that all halogenated derivatives are more effective HKII inhibitors than 2-DG, particularly through their 6-phosphates. By modifying the C-2 position with halogens, these compounds may overcome the poor pharmacokinetics of 2-DG. The modifications seem to enhance the stability and uptake of the compounds, making them effective at lower doses and over prolonged periods.

CONCLUSIONS

This research has the potential to reshape the treatment landscape for GBM and possibly other cancers by offering a more targeted, effective, and metabolically focused therapeutic approach. The application of halogenated 2-DG analogs represents a promising advancement in cancer metabolism-targeted therapies, with the potential to overcome current treatment limitations.

The protective effects of Myrtus communis subsp. on ovariectomized diabetic rats' renal and intestinal tissues: in vivo and in silico approaches

INTRODUCTION

Postmenopausal diabetes is a condition that affects millions of women and their quality of life. Also, kidney and small intestine tissues are damaged due to diabetes. The present study aimed to examine the protective effects of an extract prepared from Myrtus communis leaves on kidney and small intestine tissues against experimentally created postmenopausal diabetes.

METHODS

For this purpose, experimental rats were randomly divided into six groups (Control; ovariectomy:OVX, diabetic:D, ovariectomy + diabetic:OVX + D, ovariectomy + diabetic + oestrogen:OVX + D+E2, ovariectomy + diabetic + MC: OVX + D+MC) and kidney and small intestine tissues were taken after the experimental procedure.

RESULTS

Evaluations of biochemical parameters (glutathione and glutathione-related enzymes, antioxidant enzymes, etc.) showed that MC had a protective effect on kidney and small intestine tissues in diabetes and ovariectomy groups.

CONCLUSION

It can be suggested that MC extract has a protective effect on small intestine and kidney tissues in postmenopausal diabetes and may be a good herbal source for this purpose.

Characterization of Mycobacterium smegmatis Glutaminase-Free Asparaginase (MSMEG_3173)

l-asparaginase is an enzyme catalyzing the hydrolysis of l-asparagine into l-aspartate and ammonia, which is of great therapeutic importance in tumor treatment. However, commercially available enzymes are associated with adverse effects, and searching for a new l-asparaginase with better pharmaceutical properties was the aim of this work. The coding sequence for Mycobacterium smegmatisl-asparaginase (MsA) was cloned and expressed. The recombinant protein showed high activity toward l-asparagine, whereas none was detected for l-glutamine. The enzymatic properties (K m = 1.403 ± 0.24 mM and k cat = 708.1 ± 25.05 s-1) indicate that the enzyme would be functional within the expected blood l-asparagine concentration, with good activity, as shown by k cat. The pH and temperature profiles suggest its use as a biopharmaceutical in humans. Molecular dynamics analysis of the MsA model reveals the formation of a hydrogen bond network involving catalytic residues with l-asparagine. However, the same is not observed with l-glutamine, mainly due to steric hindrance. Additionally, the structural feature of residue 119 being a serine rather than a proline has significant implications. These findings help explain the low glutaminase activity observed in MsA, like what is described for the Wolinella succinogenes enzyme. This establishes mycobacterial asparaginases as key scaffolds to develop biopharmaceuticals against acute lymphocytic leukemia.

Ergostanes from the mushroom Trametes versicolor and their cancer cell inhibition: In vitro and in silico evaluation

In this study, five steroid compounds were isolated from the fruiting bodies mushroom Trametes versicolor. The compounds, 9,19-cyclolanostane-3,29-diol (3), ergosta-7,22-dien-3-acetate (4), and ergosta-8(14),22-dien-3β,5α,6β,7α-tetrol (5), were identified from T. versicolor for the first time. The five compounds were evaluated for their activity against cancer cell lines. Compound 5α,8α-epidioxyergosta-6,22-dien-3β-ol (1) was found to be the most effective against most of the cancer cell lines tested. In silico studies showed that compound 1 has good binding affinities to different cancer targets, namely cyclin-dependent kinase 2 (cdk2), human cyclin-dependent kinase 6 (cdk6), Human Topo IIa ATPase/AMP-PNP, anti-apoptotic protein Bcl-2, and Vegfr-2. It's also druglike based on Lipinski's rule of five and it's ADME/Tox properties. Therefore, compound 1 is a good candidate in the management of cancer. These results further show that T. versicolor is a potential source of drugs or drug leads for cancer treatment.

In silico evidence of bitopertin's broad interactions within the SLC6 transporter family

The Glycine Transporter Type 1 (GlyT1) significantly impacts central nervous system functions, influencing glycinergic and glutamatergic neurotransmission. Bitopertin, the first GlyT1 inhibitor in clinical trials, was developed for schizophrenia treatment but showed limited efficacy. Despite this, bitopertin's repositioning could advance treating various pathologies. This study aims to understand bitopertin's mechanism of action using computational methods, exploring off-target effects, and providing a comprehensive pharmacological profile. Similarity Ensemble Approach (SEA) and SwissTargetPrediction initially predicted targets, followed by molecular modeling on SWISS-MODEL and GalaxyWeb servers. Binding sites were identified using PrankWeb, and molecular docking was performed with DockThor and GOLD software. Molecular dynamics analyses were conducted on the Visual Dynamics platform. Reverse screening on SEA and SwissTargetPrediction identified GlyT1 (SLC6A9), GlyT2 (SLC6A5), PROT (SLC6A7), and DAT (SLC6A3) as potential bitopertin targets. Homology modeling on SwissModel generated high-resolution models, optimized further on GalaxyWeb. PrankWeb identified similar binding sites in GlyT1, GlyT2, PROT, and DAT, indicating potential interaction. Docking studies suggested bitopertin's interaction with GlyT1 and proximity to GlyT2 and PROT. Molecular dynamics confirmed docking results, highlighting bitopertin's target stability beyond GlyT1. The study concludes that bitopertin potentially interacts with multiple SLC6 family targets, indicating a broader pharmacological property.

N121T and N121S substitutions on the SARS-CoV-2 spike protein impact on serum neutralization

The N121 site on the spike protein of SARS-CoV-2 is associated with heme and its metabolite, biliverdin, which can affect antibody binding. Both N121T and N121S substitutions have been observed in natural conditions and in a hamster model of dual infection with SARS-CoV-2 and Influenza A virus. Serum pseudotype neutralization assays against HIV-1 particles carrying wild-type, N121T, and N121S spikes with immune mouse and human sera revealed that N121T and N121S mutations had a greater impact on serum neutralization than biliverdin treatment. Although N121T and N121S substitutions are not currently major SARS-CoV-2 variants of concern, this study could provide fundamental information to prepare for potential future mutations at the N121 site of SARS-CoV-2.

DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening

The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein-ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein-ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.

Design of Promising Thiazoloindazole-Based Acetylcholinesterase Inhibitors Guided by Molecular Docking and Experimental Insights

Alzheimer's disease is characterized by a progressive deterioration of cognitive function and memory loss, and it is closely associated with the dysregulation of cholinergic neurotransmission. Since acetylcholinesterase (AChE) is a critical enzyme in the nervous system, responsible for breaking down the neurotransmitter acetylcholine, its inhibition holds a significant interest in the treatment of various neurological disorders. Therefore, it is crucial to develop efficient AChE inhibitors capable of increasing acetylcholine levels, ultimately leading to improved cholinergic neurotransmission. The results reported here represent a step forward in the development of novel thiazoloindazole-based compounds that have the potential to serve as effective AChE inhibitors. Molecular docking studies revealed that certain of the evaluated nitroindazole-based compounds outperformed donepezil, a well-known AChE inhibitor used in Alzheimer's disease treatment. Sustained by these findings, two series of compounds were synthesized. One series included a triazole moiety (Tl45a-c), while the other incorporated a carbazole moiety (Tl58a-c). These compounds were isolated in yields ranging from 66 to 87% through nucleophilic substitution and Cu(I)-catalyzed azide-alkyne 1,3-dipolar cycloaddition (CuAAC) reactions. Among the synthesized compounds, the thiazoloindazole-based 6b core derivatives emerged as selective AChE inhibitors, exhibiting remarkable IC50 values of less than 1.0 μM. Notably, derivative Tl45b displays superior performance as an AChE inhibitor, boasting the lowest IC50 (0.071 ± 0.014 μM). Structure-activity relationship (SAR) analysis indicated that derivatives containing the bis(trifluoromethyl)phenyl-triazolyl group demonstrated the most promising activity against AChE, when compared to more rigid substituents such as carbazolyl moiety. The combination of molecular docking and experimental synthesis provides a suitable and promising strategy for the development of new efficient thiazoloindazole-based AChE inhibitors.

Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets

In drug discovery, molecular docking methods face challenges in accurately predicting energy. Scoring functions used in molecular docking often fail to simulate complex protein-ligand interactions fully and accurately leading to biases and inaccuracies in virtual screening and target predictions. We introduce the "Docking Score ML", developed from an analysis of over 200,000 docked complexes from 155 known targets for cancer treatments. The scoring functions used are founded on bioactivity data sourced from ChEMBL and have been fine-tuned using both supervised machine learning and deep learning techniques. We validated our approach extensively using multiple data sets such as validation of selectivity mechanism, the DUDE, DUD-AD, and LIT-PCBA data sets, and performed a multitarget analysis on drugs like sunitinib. To enhance prediction accuracy, feature fusion techniques were explored. By merging the capabilities of the Graph Convolutional Network (GCN) with multiple docking functions, our results indicated a clear superiority of our methodologies over conventional approaches. These advantages demonstrate that Docking Score ML is an efficient and accurate tool for virtual screening and reverse docking.

Exploring the Therapeutic Potential of Camellia longii Orel & Luu Leaf Extracts for Memory Loss in Alzheimer's Disease: Novel Findings and Functional Food Applications

Novel research on the chemical compositions and biochemical activities of Camellia longii Orel and Luu leaf extracts revealed valuable resources with potential applications in Alzheimer's disease treatment. Qualitative phytochemicals detected various compound groups, including polyphenols, saponins, tannins, flavonoids, alkaloids, amino acids, coumarins, and polysaccharides. HPLC-MS identified 23 compounds in C. longii leaves with compounds found at significant levels, including epicatechin gallate (17.12%), tryptophan (13.73%), isovitexin (12.91%), gallic acid (3.06%), and quercetin (3.06%). Interestingly, the ethanol extract (CLL-Ew) exhibited the highest extraction yield (26.6%) and potent antioxidant and acetylcholinesterase (AChE) inhibitory effects in vitro. In the Drosophila melanogaster model, CLL-Ew improved longevity, movement, and memory by reducing malondialdehyde and increasing glutathione levels. Docking simulations suggested that the above compounds bind tightly to AChE's active site, potentially contributing to memory enhancement. Interestingly, observations of male and female mice after administration of a dose of 5000 mg/kg C. longii leaf extract were recorded normally throughout the 14 day experiment. These findings highlight the potential of C. longii leaf extracts in functional foods and therapeutic interventions for memory impairment prevention and treatment.

Binding Affinity Determination in Drug Design: Insights from Lock and Key, Induced Fit, Conformational Selection, and Inhibitor Trapping Models

Binding affinity is a fundamental parameter in drug design, describing the strength of the interaction between a molecule and its target protein. Accurately predicting binding affinity is crucial for the rapid development of novel therapeutics, the prioritization of promising candidates, and the optimization of their properties through rational design strategies. Binding affinity is determined by the mechanism of recognition between proteins and ligands. Various models, including the lock and key, induced fit, and conformational selection, have been proposed to explain this recognition process. However, current computational strategies to predict binding affinity, which are based on these models, have yet to produce satisfactory results. This article explores the connection between binding affinity and these protein-ligand interaction models, highlighting that they offer an incomplete picture of the mechanism governing binding affinity. Specifically, current models primarily center on the binding of the ligand and do not address its dissociation. In this context, the concept of ligand trapping is introduced, which models the mechanisms of dissociation. When combined with the current models, this concept can provide a unified theoretical framework that may allow for the accurate determination of the ligands' binding affinity.

Investigating molecular descriptors in cell-penetrating peptides prediction with deep learning: Employing N, O, and hydrophobicity according to the Eisenberg scale

Cell-penetrating peptides comprise a group of molecules that can naturally cross the lipid bilayer membrane that protects cells, sharing physicochemical and structural properties, and having several pharmaceutical applications, particularly in drug delivery. Investigations of molecular descriptors have provided not only an improvement in the performance of classifiers but also less computational complexity and an enhanced understanding of membrane permeability. Furthermore, the employment of new technologies, such as the construction of deep learning models using overfitting treatment, promotes advantages in tackling this problem. In this study, the descriptors nitrogen, oxygen, and hydrophobicity on the Eisenberg scale were investigated, using the proposed ConvBoost-CPP composed of an improved convolutional neural network with overfitting treatment and an XGBoost model with adjusted hyperparameters. The results revealed favorable to the use of ConvBoost-CPP, having as input nitrogen, oxygen, and hydrophobicity together with ten other descriptors previously investigated in this research line, showing an increase in accuracy from 88% to 91.2% in cross-validation and 82.6% to 91.3% in independent test.

Airfoil aerodynamic performance prediction using machine learning and surrogate modeling

In recent times, machine learning algorithms have gained significant traction in addressing aerodynamic challenges. These algorithms prove invaluable for predicting the aerodynamic performance, specifically the Lift-to-Drag ratio of airfoil datasets, when the dataset is sufficiently large and diverse. In this paper, we delve into an exploration of five machine learning algorithms: Random Forest, Gradient Boosting Regression, Decision Tree Regressor, AdaBoost Algorithm, and Linear Regression. These algorithms are scrutinized within the context of various train/test ratios to predict a crucial aerodynamic performance metric-the lift-to-drag ratio-for different angle of attack values. Our evaluation encompasses an array of metrics including R2, Mean Square Error, Training time, and Evaluation time. Upon analysis, the Random Forest Method, with a train/test ratio of 0.2, emerges as the frontrunner, showcasing superior predictive performance when compared to its counterparts. Conversely, the Linear Regression algorithm distinguishes itself by excelling in training and evaluation times among the algorithms under scrutiny.

An investigation of binding interactions of tumor-targeted peptide conjugated polyphenols with the kinase domain of ephrin B4 and B2 receptors

Recent studies have shown that Ephrin receptors may be upregulated in several types of cancers including breast, ovarian and endometrial cancers, making them a target for drug design. In this work, we have utilized a target-hopping approach to design new natural product-peptide conjugates and examined their interactions with the kinase-binding domain of EphB4 and EphB2 receptors. The peptide sequences were generated through point mutations of the known EphB4 antagonist peptide TNYLFSPNGPIA. Their anticancer properties and secondary structures were analyzed computationally. Conjugates of most optimum of peptides were then designed by binding the N-terminal of the peptides with the free carboxyl group of the polyphenols sinapate, gallate and coumarate, which are known for their inherent anticancer properties. To investigate if these conjugates have a potential to bind to the kinase domain, we carried out docking studies and MMGBSA free energy calculations of the trajectories based on the molecular dynamics simulations, with both the apo and the ATP bound kinase domains of both receptors. In most cases binding interactions occurred within the catalytic loop region, while in some cases the conjugates were found to spread out across the N-lobe and the DFG motif region. The conjugates were further tested for prediction of pharmacokinetic properties using ADME studies. Our results indicated that the conjugates were lipophilic and MDCK permeable with no CYP interactions. These findings provide an insight into the molecular interactions of these peptides and conjugates with the kinase domain of the EphB4 and EphB2 receptor. As a proof of concept, we synthesized and carried out SPR analysis with two of the conjugates (gallate-TNYLFSPNGPIA and sinapate-TNYLFSPNGPIA). Results indicated that the conjugates showed higher binding with the EphB4 receptor and minimal binding to EphB2 receptor. Sinapate-TNYLFSPNGPIA showed inhibitory activity against EphB4. These studies reveal that some of the conjugates may be developed for further investigation into in vitro and in vivo studies and potential development as therapeutics.

Comparative Analysis of Leishmania major Nucleoside Hydrolases Toward Selecting Multi-target Strategy

PURPOSE

Leishmania causes multiple types of leishmaniasis in different parts of the world. It has a lack of metabolic machine to produce purine bases. Therefore, the parasite produces purine bases through the breakdown of nutritional nucleotides and it makes the nucleoside hydrolases (NHs) good drug targets. They have different substrate-preferring (SP) types. Our objectives were modeling and comparative analysis of these protein structures for Leishmania major.

METHOD

In this work, available sequences for all SP types of L. major NH enzymes including inosine-uridine preferring NH (IUNH), inosine-guanosine preferring NH (IGNH), and inosine-adenosine-guanosine preferring NH (IAGNH) were used to make 24 structural models via SWISS-MODEL and LOMETS. After evaluating the structural models, three enzyme structures were finalized and used to analyze substrate-binding pockets.

RESULTS

The three SP types of L. major NH enzymes that can breakdown purine nucleosides were highly different in terms of sequence, structure, and profile of interacting residues within the substrate-binding pockets. In this study, new enzyme structures have been presented for three SP types and they have been compared in different aspects and it indicated that they were very different from each other.

CONCLUSION

Although, previously indicated that from these three SP types in genera other than Leishmania, the role of IGNH and IAGNH was greater than IUNH in supplying purine bases, till this work, just IUNH has been structurally studied and used in drug-design investigations for Leishmania. Therefore, we are offering to use all three SP types of NHs as multi-target strategy in anti-leishmaniosis drug-design studies.

Targeting with Structural Analogs of Natural Products the Purine Salvage Pathway in Leishmania (Leishmania) infantum by Computer-Aided Drug-Design Approaches

Visceral Leishmaniasis (VL) has a high death rate, with 500,000 new cases and 50,000 deaths occurring annually. Despite the development of novel strategies and technologies, there is no adequate treatment for the disease. Therefore, the purpose of this study is to find structural analogs of natural products as potential novel drugs to treat VL. We selected structural analogs from natural products that have shown antileishmanial activities, and that may impede the purine salvage pathway using computer-aided drug-design (CADD) approaches. For these, we started with the vastly studied target in the pathway, the adenine phosphoribosyl transferase (APRT) protein, which alone is non-essential for the survival of the parasite. Keeping this in mind, we search for a substance that can bind to multiple targets throughout the pathway. Computational techniques were used to study the purine salvage pathway from Leishmania infantum, and molecular dynamic simulations were used to gather information on the interactions between ligands and proteins. Because of its low homology to human proteins and its essential role in the purine salvage pathway proteins network interaction, the findings further highlight the significance of adenylosuccinate lyase protein (ADL) as a therapeutic target. An analog of the alkaloid Skimmianine, N,N-diethyl-4-methoxy-1-benzofuran-6-carboxamide, demonstrated a good binding affinity to APRT and ADL targets, no expected toxicity, and potential for oral route administration. This study indicates that the compound may have antileishmanial activity, which was granted in vitro and in vivo experiments to settle this finding in the future.

The evaluations of the inhibition of orlistat on Clostridium perfringens sialidase (NanI) activity by in vitro and in silico approaches

Clostridium perfringens (C. perfringens) is a bacterium that causes serious problems in humans and animals such as food poisoning, gas gangrene and infections. C. perfringens has three sialidases (NanH, NanI, NanJ) and inhibition of NanI constitutes an approach in the treatment of C. perfringens since NanI provides the carbohydrate source necessary for the growth of bacteria. In our study, the inhibition effect of some drugs belonging to different drug groups on NanI activity was investigated. Among these drugs, orlistat (0.21±0.05 μM) was determined to have a lower IC50 value than the positive control quercetin (15.58±1.59 μM). It was determined in vitro by spectrofluorometric method. Additionally, NanI molecular docking studies with orlistatand quercetin were performed using iGemdock, DockThor and SwissDock. Orlistat (-93.93, -8.649 and -10.03 kcal/mol, respectively) was found to have a higher binding affinity than quercetin (-92.68, -7.491 and -8.70 kcal/mol, respectively), and the results were in line with in vitro studies. The results may suggest that orlistat is a molecule with drug potential for C. perfringens because it inhibits the drug target NanI, and that the inhibition efficiency can be increased by studies with orlistat derivatives.

Spectroscopic characterization of eupalitin-3-O-β-D-galactopyranoside from Boerhavia procumbens: In vivo hepato-protective potential in rat model

The liver is one of the most important organs responsible for detoxifying biomolecules and xenobiotics. Herein, we report the isolation, characterization, and hepatoprotective effect of the Boerhavia procumbens-derived eupalitin-3-O-β-D-galactopyranoside (EGP) compound. The structure of the EGP compound was deduced by using NMR spectroscopic techniques and mass spectrometry. The EGP hepatoprotective activities were evaluated with HepG2 cell viability and LDH assays in vitro, and CCl4-induced toxicity was investigated in vivo in the rat model. Compared to the CCl4-treated group, cells exposed to the EGP compound at 200 µg/ml showed increased cell viability (60.52 ± 1.22 %) and decreased LDH levels (23.81 ± 1.89 U/ml). The serum levels of SGPT, SGOT, ALP, and total bilirubin in the CCl4-treated group were substantially higher than those in the control group (64 ± 1.89 U/ml, 86 ± 1.47 U/ml, 252.6 ± 2.96 U/ml, and 5.45 ± 0.32 mg/dl, respectively). When compared to animals treated with CCl4 alone, the EGP compound's in vivo hepatoprotective effect at 60 mg/kg with CCl4 significantly (p < 0.01) decreased the levels of SGPT and SGOT (26 ± 1.34 U/ml and 42.92 ± 1.6 U/ml) respectively. Furthermore, our histological study showed a significant response in restoring and maintaining the normal morphological appearance of the liver. Thus, our results show that the EGP compound is a promising and novel lead molecule for better hepatotoxicity control and therapy.

Naturally occurring quercetin and myricetin as potent inhibitors for human ectonucleotide pyrophosphatase/phosphodiesterase 1

Ecto-nucleotide pyrophosphatases/phosphodiesterases 1 (ENPP1) is a key enzyme in purinergic signaling pathways responsible for cell-to-cell communications and regulation of several fundamental pathophysiological processes. In this study, Kyoto Green, a rapid chemical sensor of pyrophosphate, was employed to screen for effective ENPP1 inhibitors among five representative flavonoids (quercetin, myricetin, morin, kaempferol, and quercetin-3-glucoside), five nucleosides (adenosine, guanosine, inosine, uridine, and cytidine), and five deoxynucleosides (2'- and 3'-deoxyadenosine, 2'-deoxyguanosine, 2'-deoxyinosine, and 2'-deoxyuridine). Conventional colorimetric, fluorescence, and bioluminescence assays revealed that ENPP1 was effectively inhibited by quercetin (Ki ~ 4 nM) and myricetin (Ki ~ 32 nM) when ATP was used as a substrate at pH 7.4. In silico analysis indicated that the presence of a chromone scaffold, particularly one containing a hydroxyl group at the 3' position on the B ring, may promote binding to the active site pocket of ENPP1 and enhance inhibition. This study demonstrated that the naturally derived quercetin and myricetin could effectively inhibit ENPP1 enzymatic activity and may offer health benefits in arthritis management.

Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.

Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.

Synthesis and In vitro and In silico Anti-inflammatory Activity of New Thiazolidinedione-quinoline Derivatives

BACKGROUND

Inflammation is a series of complex defense-related reactions. The inflammation cascade produces various pro-inflammatory mediators. Unregulated production of these pro-inflammatory mediators can lead to a wide range of diseases, including rheumatoid arthritis, sepsis, and inflammatory bowel disease. In the literature, the anti-inflammatory action of quinoline and thiazolidinedione nuclei are well established, alone, and associated with other nuclei. The synthesis of hybrid molecules is a strategy for obtaining more efficient molecules due to the union of pharmacophoric nuclei known to be related to pharmacological activity.

OBJECTIVES

Based on this, this work presents the synthesis of thiazolidinedione-quinoline molecular hybrids and their involvement in the modulation of cytokines involved in the inflammatory reaction cascade.

METHODS

After synthesis and characterization, the compounds were submitted to cell viability test (MTT), ELISA IFN-γ and TNF-α, adipogenic differentiation, and molecular docking assay with PPARy and COX-2 targets.

RESULTS

LPSF/ZKD2 and LPSF/ZKD7 showed a significant decrease in the concentration of IFN- γ and TNF-α, with a dose-dependent behavior. LPSF/ZKD4 at a concentration of 50 μM significantly reduced IL-6 expression. LPSF/ZKD4 demonstrates lipid accumulation with significant differences between the untreated and negative control groups, indicating a relevant agonist action on the PPARγ receptor. Molecular docking showed that all synthesized compounds have good affinity with PPARγ e COX-2, with binding energy close to -10,000 Kcal/mol.

CONCLUSION

These results demonstrate that the synthesis of quinoline-thiazolidinedione hybrids may be a useful strategy for obtaining promising candidates for new anti-inflammatory agents.

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Multi-and many-objective optimization: present and future in de novo drug design

de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.

In silico studies of anti-oxidative and hot temperament-based phytochemicals as natural inhibitors of SARS-CoV-2 Mpro

Main protease (Mpro) of SARS-CoV-2 is considered one of the key targets due to its role in viral replication. The use of traditional phytochemicals is an important part of complementary/alternative medicine, which also accompany the concept of temperament, where it has been shown that hot medicines cure cold and cold medicines cure hot, with cold and hot pattern being associated with oxidative and anti-oxidative properties in medicine, respectively. Molecular docking in this study has demonstrated that a number of anti-oxidative and hot temperament-based phytochemicals have high binding affinities to SARS-CoV-2 Mpro, both in the monomeric and dimeric deposited states of the protein. The highest ranking phytochemicals identified in this study included savinin, betulinic acid and curcumin. Complexes of savinin, betulinic acid, curcumin as well as Nirmatrelvir (the only approved inhibitor, used for comparison) bound to SARS-CoV-2 Mpro were further subjected to molecular dynamics simulations. Subsequently, RMSD, RMSF, Rg, number of hydrogen bonds, binding free energies and residue contributions (using MM-PBSA) and buried surface area (BSA), were analysed. The computational results suggested high binding affinities of savinin, betulinic acid and curcumin to both the monomeric and dimeric deposited states of Mpro, while highlighting the lower binding energy of betulinic acid in comparison with savinin and curcumin and even Nirmatrelvir, leading to a greater stability of the betulinic acid-SARS-CoV-2 Mpro complex. Overall, based on the increasing mutation rate in the spike protein and the fact that the SARS-CoV-2 Mpro remains highly conserved, this study provides an insight into the use of phytochemicals against COVID-19 and other coronavirus diseases.

Nature's soothing solution: Harnessing the potential of food-derived polysaccharides to control inflammation

Reducing inflammation by diet is a major goal for prevention or lowering symptoms of a variety of diseases, such as auto-immune reactions and cancers. Natural polysaccharides are increasingly gaining attention due to their potential immunomodulating capacity. Structures of those molecules are highly important for their effects on the innate immune system, cytokine production and secretion, and enzymes in immune cells. Such polysaccharides include β-glucans, pectins, fucoidans, and fructans. To better understand the potential of these immunomodulatory molecules, it is crucial to enhance dedicated research in the area. A bibliometric analysis was performed to set a starting observation point. Major pillars of inflammation, such as pattern recognition receptors (PRRs), enzymatic production of inflammatory molecules, and involvement in specific pathways such as Nuclear-factor kappa-B (NF-kB), involved in cell transcription, survival, and cytokine production, and mitogen-activated protein kinase (MAPK), a regulator of genetic expression, mitosis, and cell differentiation. Therefore, the outcomes from polysaccharide applications in those scenarios are discussed.

Nuclear receptor superfamily structural diversity in pacific oyster: In silico identification of estradiol binding candidates

The increasing presence of anthropogenic contaminants in aquatic environments poses challenges for species inhabiting contaminated sites. Due to their structural binding characteristics to ligands that inhibit or activate gene transcription, these xenobiotic compounds frequently target the nuclear receptor superfamily. The present work aims to understand the potential interaction between the hormone 17-β-estradiol, an environmental contaminant, and the nuclear receptors of Crassostrea gigas, the Pacific oyster. This filter-feeding, sessile oyster species is subject to environmental changes and exposure to contaminants. In the Pacific oyster, the estrogen-binding nuclear receptor is not able to bind this hormone as it does in vertebrates. However, another receptor may exhibit responsiveness to estrogen-like molecules and derivatives. We employed high-performance in silico methodologies, including three-dimensional modeling, molecular docking and atomistic molecular dynamics to identify likely binding candidates with the target moecule. Our approach revealed that among the C. gigas nuclear receptor superfamily, candidates with the most favorable interaction with the molecule of interest belonged to the NR1D, NR1H, NR1P, NR2E, NHR42, and NR0B groups. Interestingly, NR1H and NR0B were associated with planktonic/larval life cycle stages, while NR1P, NR2E, and NR0B were associated with sessile/adult life stages. The application of this computational methodological strategy demonstrated high performance in the virtual screening of candidates for binding with the target xenobiotic molecule and can be employed in other studies in the field of ecotoxicology in non-model organisms.

Capparis cartilaginea decne (capparaceae): isolation of flavonoids by high-speed countercurrent chromatography and their anti-inflammatory evaluation

Introduction: Capparis cartilaginea Decne. (CC) originates from the dry regions of Asia and the Mediterranean basin. In traditional medicine, tea of CC leaves is commonly used to treat inflammatory conditions such as rheumatism, arthritis, and gout. Due to the limited studies on the phytochemistry and biological activity of CC compared to other members of the Capparaceae family, this work aims to: 1) Identify the chemical composition of CC extract and 2) Investigate the potential anti-inflammatory effect of CC extract, tea and the isolated compounds. Methods: To guarantee aim 1, high-speed countercurrent chromatography (HSCC) method; Nuclear Magnetic Resonance (NMR) and High-Performance Liquid Chromatography coupled to Electrospray Ionisation and Quadrupole Time-of-Flight Mass Spectrometry (HPLC-ESIQTOF-MS/MS) were employed for this purpose. To guarantee aim 2, we studied the effect of the isolated flavonoids on matrix metalloproteinases (MMPs) -9 and -2 in murine macrophages. Molecular docking was initially performed to assess the binding affinity of the isolated flavonoids to the active site of MMP-9. Results and discussion: In silico model was a powerful tool to predict the compounds that could strongly bind and inhibit MMPs. CC extract and tea have shown to possess a significant antioxidant and anti-inflammatory effect, which can partially explain their traditional medicinal use.

Hierarchical Virtual Screening of Potential New Antibiotics from Polyoxygenated Dibenzofurans against Staphylococcus aureus Strains

Staphylococcus aureus is a microorganism with high morbidity and mortality due to antibiotic-resistant strains, making the search for new therapeutic options urgent. In this context, computational drug design can facilitate the drug discovery process, optimizing time and resources. In this work, computational methods involving ligand- and structure-based virtual screening were employed to identify potential antibacterial agents against the S. aureus MRSA and VRSA strains. To achieve this goal, tetrahydroxybenzofuran, a promising antibacterial agent according to in vitro tests described in the literature, was adopted as the pivotal molecule and derivative molecules were considered to generate a pharmacophore model, which was used to perform virtual screening on the Pharmit platform. Through this result, twenty-four molecules were selected from the MolPort® database. Using the Tanimoto Index on the BindingDB web server, it was possible to select eighteen molecules with greater structural similarity in relation to commercial antibiotics (methicillin and oxacillin). Predictions of toxicological and pharmacokinetic properties (ADME/Tox) using the eighteen most similar molecules, showed that only three exhibited desired properties (LB255, LB320 and LB415). In the molecular docking study, the promising molecules LB255, LB320 and LB415 showed significant values in both molecular targets. LB320 presented better binding affinity to MRSA (-8.18 kcal/mol) and VRSA (-8.01 kcal/mol) targets. Through PASS web server, the three molecules, specially LB320, showed potential for antibacterial activity. Synthetic accessibility (SA) analysis performed on AMBIT and SwissADME web servers showed that LB255 and LB415 can be considered difficult to synthesize and LB320 is considered easy. In conclusion, the results suggest that these ligands, particularly LB320, may bind strongly to the studied targets and may have appropriate ADME/Tox properties in experimental studies.

Insights on the structural characteristics and molecular dynamic studies of methyl vanillin Schiff base bio-compounds

A new crystalline form of Schiff base, N-cyclohexyl-1-(3,4-dimethoxyphenyl)methanimine (CHADMB) was obtained from methanolic solution of cylohexylamine and (methylvanillin) 3,4dimethoxybenzaldehyde. Single crystal X-ray diffraction study reveals that the compound crystallized in monoclinic crystal system with P21/c space group having four molecules per unit cell (Z = 4). Hirshfeld surface (HS) analysis and 2D fingerprint plots reveals that weak non-covalent interactions are responsible for crystal packing. The UV-Vis spectroscopy study reveals that the optical band gap of the compound is 4.25 eV. The dielectric properties were studied as a function of frequency at room temperature and the results show that these properties can be exploited for optoelectronic applications. Thermal stability of the compound is revealed by thermogravimetric and differential thermogravimetric analysis. The in vitro antimicrobial activity against Gram negative (E. coli and P. aeruginosa and Gram positive (S. aureus ) bacterial strains and two fungal strains (C. albicans and A. niger) were studied by agar well diffusion method. It is found that the Schiff base is inhibiting the growth of the tested species to varying degrees. Molecular docking studies indicate that alkyl-pi and pi-pi weak interactions enhance the binding affinity of Schiff base-protein complexes. Molecular dynamics study reveals interaction of CHADMB complexed with bacterial protein, EC showed maximum stability which is in agreement with experimental result.Communicated by Ramaswamy H. Sarma.

A Shortcut from Genome to Drug: The Employment of Bioinformatic Tools to Find New Targets for Gastric Cancer Treatment

Gastric cancer (GC) is a highly heterogeneous, complex disease and the fifth most common cancer worldwide (about 1 million cases and 784,000 deaths worldwide in 2018). GC has a poor prognosis (the 5-year survival rate is less than 20%), but there is an effort to find genes highly expressed during tumor establishment and use the related proteins as targets to find new anticancer molecules. Data were collected from the Gene Expression Omnibus (GEO) bank to obtain three dataset matrices analyzing gastric tumor tissue versus normal gastric tissue and involving microarray analysis performed using the GPL570 platform and different sources. The data were analyzed using the GEPIA tool for differential expression and KMPlot for survival analysis. For more robustness, GC data from the TCGA database were used to corroborate the analysis of data from GEO. The genes found in in silico analysis in both GEO and TCGA were confirmed in several lines of GC cells by RT-qPCR. The AlphaFold Protein Structure Database was used to find the corresponding proteins. Then, a structure-based virtual screening was performed to find molecules, and docking analysis was performed using the DockThor server. Our in silico and RT-qPCR analysis results confirmed the high expression of the AJUBA, CD80 and NOLC1 genes in GC lines. Thus, the corresponding proteins were used in SBVS analysis. There were three molecules, one molecule for each target, MCULE-2386589557-0-6, MCULE-9178344200-0-1 and MCULE-5881513100-0-29. All molecules had favorable pharmacokinetic, pharmacodynamic and toxicological properties. Molecular docking analysis revealed that the molecules interact with proteins in critical sites for their activity. Using a virtual screening approach, a molecular docking study was performed for proteins encoded by genes that play important roles in cellular functions for carcinogenesis. Combining a systematic collection of public microarray data with a comparative meta-profiling, RT-qPCR, SBVS and molecular docking analysis provided a suitable approach for finding genes involved in GC and working with the corresponding proteins to search for new molecules with anticancer properties.

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

Atovaquone and Pibrentasvir Inhibit the SARS-CoV-2 Endoribonuclease and Restrict Infection In Vitro but Not In Vivo

The emergence of SARS-CoV-1 in 2003 followed by MERS-CoV and now SARS-CoV-2 has proven the latent threat these viruses pose to humanity. While the SARS-CoV-2 pandemic has shifted to a stage of endemicity, the threat of new coronaviruses emerging from animal reservoirs remains. To address this issue, the global community must develop small molecule drugs targeting highly conserved structures in the coronavirus proteome. Here, we characterized existing drugs for their ability to inhibit the endoribonuclease activity of the SARS-CoV-2 non-structural protein 15 (nsp15) via in silico, in vitro, and in vivo techniques. We have identified nsp15 inhibition by the drugs pibrentasvir and atovaquone which effectively inhibit SARS-CoV-2 and HCoV-OC43 at low micromolar concentrations in cell cultures. Furthermore, atovaquone, but not pibrentasvir, is observed to modulate HCoV-OC43 dsRNA and infection in a manner consistent with nsp15 inhibition. Although neither pibrentasvir nor atovaquone translate to clinical efficacy in a murine prophylaxis model of SARS-CoV-2 infection, atovaquone may serve as a basis for the design of future nsp15 inhibitors.

Using macromolecular electron densities to improve the enrichment of active compounds in virtual screening

The quest for effective virtual screening algorithms is hindered by the scarcity of training data, calling for innovative approaches. This study presents the use of experimental electron density (ED) data for improving active compound enrichment in virtual screening, supported by ED's ability to reflect the time-averaged behavior of ligands and solvents in the binding pocket. Experimental ED-based grid matching score (ExptGMS) was developed to score compounds by measuring the degree of matching between their binding conformations and a series of multi-resolution experimental ED grids. The efficiency of ExptGMS was validated using both in silico tests with the Directory of Useful Decoys-Enhanced dataset and wet-lab tests on Covid-19 3CLpro-inhibitors. ExptGMS improved the active compound enrichment in top-ranked molecules by approximately 20%. Furthermore, ExptGMS identified four active inhibitors of 3CLpro, with the most effective showing an IC50 value of 1.9 µM. We also developed an online database containing experimental ED grids for over 17,000 proteins to facilitate the use of ExptGMS for academic users.

Inhibition of Cysteine Proteases via Thiol-Michael Addition Explains the Anti-SARS-CoV-2 and Bioactive Properties of Arteannuin B

Artemisia annua is the plant that produces artemisinin, an endoperoxide-containing sesquiterpenoid used for the treatment of malaria. A. annua extracts, which contain other bioactive compounds, have been used to treat other diseases, including cancer and COVID-19, the disease caused by the virus SARS-CoV-2. In this study, a methyl ester derivative of arteannuin B was isolated when A. annua leaves were extracted with a 1:1 mixture of methanol and dichloromethane. This methyl ester was thought to be formed from the reaction between arteannuin B and the extracting solvent, which was supported by the fact that arteannuin B underwent 1,2-addition when it was dissolved in deuteromethanol. In contrast, in the presence of N-acetylcysteine methyl ester, a 1,4-addition (thiol-Michael reaction) occurred. Arteannuin B hindered the activity of the SARS CoV-2 main protease (nonstructural protein 5, NSP5), a cysteine protease, through time-dependent inhibition. The active site cysteine residue of NSP5 (cysteine-145) formed a covalent bond with arteannuin B as determined by mass spectrometry. In order to determine whether cysteine adduction by arteannuin B can inhibit the development of cancer cells, similar experiments were performed with caspase-8, the cysteine protease enzyme overexpressed in glioblastoma. Time-dependent inhibition and cysteine adduction assays suggested arteannuin B inhibits caspase-8 and adducts to the active site cysteine residue (cysteine-360), respectively. Overall, these results enhance our understanding of how A. annua possesses antiviral and cytotoxic activities.

Effect of preprocessing and simulation parameters on the performance of molecular docking studies

CONTEXT

Molecular docking is an important and rapid tool that provides a comprehensive view of different molecular mechanisms. It is often used to verify the binding interactions of many pairs of molecules and is much faster than more rigorous approaches. However, its application requires carefully preprocessing each molecule and selecting a series of simulation parameters, which is not always done correctly. We show how preprocessing and simulation parameters can positively or negatively impact molecular docking performance. For example, the inclusion of hydrogen atoms leads to better redocking scores, but molecular dynamics simulations must be performed under certain constraints; otherwise, it may worsen performance rather than improve it. This study clarifies the importance and influence of these different parameters in the simulation results.

METHODS

We analyzed the influence of different parameters on the predictive ability of molecular docking techniques using two software packages: AutoDock Vina and AutoDock-GPU. Thus, 90 receptor-ligand complexes were redocked, evaluating the root mean square deviation (RMSD) between the original position of the ligand (receptor-ligand complex obtained experimentally) and that obtained by the software for every analysis. We investigated the influence of hydrogen atoms (on the receptor and on the receptor-ligand complex), partial charges (QEq, QTPIE, EEM, EEM2015ha, MMFF94, Gasteiger-Marsili, and no charge), search boxes (size and exhaustiveness), ligand characteristics (size and number of torsions), and the use of molecular dynamics (of the receptor or the receptor-ligand complex) before docking analyses.

Protein structural insights into a rare PCSK9 gain-of-function variant (R496W) causing familial hypercholesterolemia in a Saudi family: whole exome sequencing and computational analysis

Familial hypercholesterolemia (FH) is a globally underdiagnosed genetic condition associated with premature cardiovascular death. The genetic etiology data on Arab FH patients is scarce. Therefore, this study aimed to identify the genetic basis of FH in a Saudi family using whole exome sequencing (WES) and multidimensional bioinformatic analysis. Our WES findings revealed a rare heterozygous gain-of-function variant (R496W) in the exon 9 of the PCSK9 gene as a causal factor for FH in this family. This variant was absent in healthy relatives of the proband and 200 healthy normolipidemic controls from Saudi Arabia. Furthermore, this variant has not been previously reported in various regional and global population genomic variant databases. Interestingly, this variant is classified as "likely pathogenic" (PP5) based on the variant interpretation guidelines of the American College of Medical Genetics (ACMG). Computational functional characterization suggested that this variant could destabilize the native PCSK9 protein and alter its secondary and tertiary structural features. In addition, this variant was predicted to negatively influence its ligand-binding ability with LDLR and Alirocumab antibody molecules. This rare PCSK9 (R496W) variant is likely to expand our understanding of the genetic basis of FH in Saudi Arabia. This study also provides computational structural insights into the genotype-protein phenotype relationship of PCSK9 pathogenic variants and contributes to the development of personalized medicine for FH patients in the future.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Modeling the CO2 separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks

Membranes are a potential technology to reduce energy consumption as well as environmental challenges considering the separation processes. A new class of this technology, namely mixed matrix membrane (MMM) can be fabricated by dispersing solid substances in a polymeric medium. In this way, the poly(4-methyl-1-pentene)-based MMMs have attracted great attention to capturing carbon dioxide (CO2), which is an environmental pollutant with a greenhouse effect. The CO2 permeability in different MMMs constituted of poly(4-methyl-1-pentene) (PMP) and nanoparticles was comprehensively analyzed from the experimental point of view. In addition, a straightforward mathematical model is necessary to compute the CO2 permeability before constructing the related PMP-based separation process. Hence, the current study employs multilayer perceptron artificial neural networks (MLP-ANN) to relate the CO2 permeability in PMP/nanoparticle MMMs to the membrane composition (additive type and dose) and pressure. Accordingly, the effect of these independent variables on CO2 permeability in PMP-based membranes is explored using multiple linear regression analysis. It was figured out that the CO2 permeability has a direct relationship with all independent variables, while the nanoparticle dose is the strongest one. The MLP-ANN structural features have efficiently demonstrated an appealing potential to achieve the highest accurate prediction for CO2 permeability. A two-layer MLP-ANN with the 3-8-1 topology trained by the Bayesian regulation algorithm is identified as the best model for the considered problem. This model simulates 112 experimentally measured CO2 permeability in PMP/ZnO, PMP/Al2O3, PMP/TiO2, and PMP/TiO2-NT with an excellent absolute average relative deviation (AARD) of lower than 5.5%, mean absolute error (MAE) of 6.87 and correlation coefficient (R) of higher than 0.99470. It was found that the mixed matrix membrane constituted of PMP and TiO2-NT (functionalized nanotube with titanium dioxide) is the best medium for CO2 separation.

Structural differences between hypoxanthine phosphoribosyltransferase family members highlight opportunities for antiparasitic drug design in neglected diseases

Approaches to identify novel druggable targets for treating neglected diseases include computational studies that predict possible interactions of drugs and their molecular targets. Hypoxanthine phosphoribosyltransferase (HPRT) plays a central role in the purine salvage pathway. This enzyme is essential for the survival of the protozoan parasite T. cruzi, the causal agent of Chagas disease, and other parasites related to neglected diseases. Here we found dissimilar functional behaviours between TcHPRT and the human homologue, HsHPRT, in the presence of substrate analogues that can lie in differences in their oligomeric assemblies and structural features. To shed light on this issue, we carried out a comparative structural analysis between both enzymes. Our results show that HsHPRT is considerably more resistant to controlled proteolysis than TcHPRT. Moreover, we observed a variation in the length of two key loops depending on the structural arrangement of each protein (groups D1T1 and D1T1'). Such variations might be involved in inter-subunit communication or influencing the oligomeric state. Besides, to understand the molecular basis that govern D1T1 and D1T1' folding groups, we explored the distribution of charges on the interaction surfaces of TcHPRT and HsHPRT, respectively. To know whether the rigidity degree bears effect on the active site, we studied the flexibility of both proteins. The analysis performed here illuminates the underlying reasons and significance behind each protein's preference for one or the other quaternary arrangement that can be exploited for therapeutic approaches.