Molecular dynamics-driven drug discovery: leaping forward with confidence

Pomegranate Peel Extract as 6-Phosphogluconate Dehydrogenase (6PGD) Inhibitor for Treatment of Breast Cancer

Targeting the enzymes of Pentose Phosphate Pathway (PPP) has been emerged as a novel strategy for treatment of cancer. 6-phosphogluconate dehydrogenase (6PGD) is third enzyme of PPP and converts 6-phosphogluconate (6-PG) into ribulose 5-phosphate (R-5-P) and produces NADPH. The overexpression of 6PGD has been reported in many human cancers especially in breast cancer and is emerged as the potential anti-cancer drug target. The current study is focused to screen an already established library of plant extracts against 6PGD, among which Pomegranate peel extract showed significant 6PGD inhibitory activity with IC50 value = 0.090 μg/mL. Pomegranate peel competitively inhibited NADP+ and 6-phosphogluconate to 6PGD enzyme having Ki constant value = 12.72 ± 5.54 ng/mL. Moreover, anti-breast cancer activity against MCF-7 cells determined Pomegranate peel as the potent inhibitor of cancerous cells with IC50 value = 3.138 μg/mL. Toxicity profiling of pomegranate peel extract (2000mg/kg) did not show any adverse effect on mice. Moreover, Ont the base of literature a library of known compounds of pomegranate was prepared and established and screened against 6PGD for the identification of actual responsible phytochemicals of 6PGD activity by using molecular docking. Computational tools were used to evaluate selected potent hits. Out of 26 compounds, three potent phytochemicals (Procyanidin, Delphinidin and Cyanidin) exhibited the best binding affinities with 6PGD. In addition, these phytochemicals displayed the best favorable hydrogen bonding, binding energy, and protein-ligand interactions as compare to 3PG. Molecular dynamics simulation suggested that these hits form a stable binding complex with the active site of 6PGD. These findings suggest that Pomegranate peel and its secondary metabolites as the potent inhibitors of 6PGD and the best drug candidate for treatment of breast cancer.

Natural flavonoids as promising lactate dehydrogenase A inhibitors: Comprehensive in vitro and in silico analysis

The inhibitory potential of 17 flavonoids on lactate dehydrogenase A (LDHA), a key enzyme in the downstream process of aerobic glycolysis in cancer cells, is investigated. Fisetin exhibited excellent inhibitory activity (IC50 = 0.066 µM). Quercetin 3-β-D-glucoside, quercetin 3-galactoside, luteolin, neoeriocitrin, and luteolin 7-O-β-D-glucoside showed good inhibitory activity (IC50 = 1.397-15.730 µM). Biochanin A, baicalein, quercetin, scutellarein-7-glucuronide, diosmetin, baicalein 7-O-β-D-glucuronide, and apigenin 7-apioglucoside demonstrated moderate inhibitory activity (IC50 = 33.007-86.643 µM). Eriodictyol, quercetin 7-O-β-D-glucoside, apigenin 7-O-β-D-glucoside, and epicatechin were inactive. The Lineweaver-Burk plot showed that fisetin competitively inhibits NADH binding (Ki = 0.024 µM). Ki values for other compounds were calculated using the Cheng-Prusoff equation (Ki = 0.2799-2.1661 µM). The study revealed that the inhibitory effect of flavonoids varies with the number and position of OH groups and bound sugars. Molecular docking analyses indicated that flavonoids exhibited strong interactions with the NADH binding site of LDHA through hydrophobic interactions and hydrogen bonds. Molecular dynamic simulations tested the stability of the fisetin-LDHA complex over 100 ns and showed fisetin's high binding affinity to LDHA, maintaining strong hydrogen bonds. The binding energy of fisetin with LDHA was -33.928 kcal/mol, indicating its effectiveness as an LDHA inhibitor. Consequently, flavonoids identified as strong inhibitors could be potential cancer treatment sources through LDHA inhibition.

Linker-assisted engineering of chimeric xylanase-phytase for improved thermal tolerance of feed enzymes

Biological enzymes are multifunctional macromolecules that can perform hundreds of reactions simultaneously. An enzyme must possess specific characteristics to meet industrial needs, such as stability over a wide pH and temperature range and high specific activity. A phytase and xylanase mixture is generally added to poultry feed to improve the bird's health and productivity. Despite this, animal farmers have noticed no difference in productivity, and a leading cause is the high temperature at which feed is pulverized, which inactivates enzymes. A thermo-stable enzyme system can overcome these hitches. Commonly, coatings and immobilization reduce losses caused by physical-chemical factors in feed processing and digestion. To this end, we engineered the multifunctional xylanase-phytase domains on a single polypeptide fused by a helical linker. First, the ideal linker sequence was chosen by computing each selected linker's root mean square deviation (RMSD). The selected helical linker provides sufficient structural flexibility for substrate binding and product release evaluated by molecular docking and molecular dynamic simulation studies. Furthermore, a domain-domain interaction has stabilized the bridging partners, attaining the thermal optima for xylanase and phytase at 90 °C. Even at the above-optimal temperature (100 °C), the recombinant PLX was relatively stable and retained 64.2% and 59.2% activity for xylanase and phytase, respectively, when surveyed for ten hours. So far, to this date, this is the highest degree of thermostability achieved by any recombinant phytase or xylanase.Communicated by Ramaswamy H. Sarma.

Advances in structure-based drug design targeting membrane protein markers in prostate cancer

Prostate cancer (PCa) is one of the leading cancers in men and the lack of suitable biomarkers or their modulators results in poor prognosis. Membrane proteins (MPs) have a crucial role in the development and progression of PCa and can be attractive therapeutic targets. However, experimental limitations in targeting MPs hinder effective biomarker and inhibitor discovery. To overcome this barrier, computational methods can yield structural insights and screen large libraries of compounds, accelerating lead identification and optimization. In this review, we examine current breakthroughs in computer-aided drug design (CADD), with emphasis on structure-based approaches targeting the most relevant membrane-bound PCa biomarkers.

Towards development of new antimalarial compounds through in silico and in vitro assays

Malaria is one of most widespread infectious disease in world. The antimalarial therapy presents a series of limitations, such as toxicity and the emergence of resistance, which makes the search for new drugs urgent. Thus, it becomes necessary to explore essential and exclusive therapeutic targets of the parasite to achieve selective inhibition. Enoyl-ACP reductase is an enzyme of the type II fatty acid biosynthetic pathway and is responsible for the rate-limiting step in the fatty acid elongation cycle. In this work, we use hierarchical virtual screening and drug repositioning strategies to prioritize compounds for phenotypic assays and molecular dynamics studies. The molecules were tested against chloroquine-resistant W2 strain of Plasmodium falciparum (EC50 between 330.05 and 13.92 µM). Nitrofurantoin was the best antimalarial activity at low micromolar range (EC50 = 13.92 µM). However, a hit compound against malaria must have a biological activity value below 1 µM. A large number of molecules present problems with permeability in biological membranes and reaching an effective concentration in their target's microenvironment. Nitrofurantoin derivatives with inclusions of groups which confer increased lipid solubility (methyl groups, halogens and substituted and unsubstituted aromatic rings) have been proposed. These derivatives were pulled through the lipid bilayer in molecular dynamics simulations. Molecules 14, 18 and 21 presented lower free energy values than nitrofurantoin when crossing the lipid bilayer.

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

The Role of AI in Drug Discovery

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

Synthesis, biological evaluation, molecular docking, and MD simulation of novel 2,4-disubstituted quinazoline derivatives as selective butyrylcholinesterase inhibitors and antioxidant agents

Alzheimer's disease is the most prevalent neurodegenerative disorder characterized by significant memory loss and cognitive impairments. Studies have shown that the expression level and activity of the butyrylcholinesterase enzyme increases significantly in the late stages of Alzheimer's disease, so butyrylcholinesterase can be considered as a promising therapeutic target for potential Alzheimer's treatments. In the present study, a novel series of 2,4-disubstituted quinazoline derivatives (6a-j) were synthesized and evaluated for their inhibitory activities against acetylcholinesterase (AChE) and butyrylcholinestrase (BuChE) enzymes, as well as for their antioxidant activities. The biological evaluation revealed that compounds 6f, 6h, and 6j showed potent inhibitory activities against eqBuChE, with IC50 values of 0.52, 6.74, and 3.65 µM, respectively. These potent compounds showed high selectivity for eqBuChE over eelAChE. The kinetic study demonstrated a mixed-type inhibition pattern for both enzymes, which revealed that the potent compounds might be able to bind to both the catalytic active site and peripheral anionic site of eelAChE and eqBuChE. In addition, molecular docking studies and molecular dynamic simulations indicated that potent compounds have favorable interactions with the active sites of BuChE. The antioxidant screening showed that compounds 6b, 6c, and 6j displayed superior scavenging capabilities compared to the other compounds. The obtained results suggest that compounds 6f, 6h, and 6j are promising lead compounds for the further development of new potent and selective BuChE inhibitors.

Unraveling the therapeutic potential of quercetin and quercetin-3-O-glucuronide in Alzheimer's disease through network pharmacology, molecular docking, and dynamic simulations

Quercetin is a flavonoid with notable pharmacological effects and promising therapeutic potential. Quercetin plays a significant role in neuroinflammation, which helps reduce Alzheimer's disease (AD) severity. Quercetin (Q) and quercetin 3-O-glucuronide (Q3OG) are some of the most potent antioxidants available from natural sources. However, the natural form of quercetin converted into Q3OG when reacted with intestinal microbes. The study aims to ensure the therapeutic potential of Q and Q3OG. In this study, potential molecular targets of Q and Q3OG were first identified using the Swiss Target Prediction platform and pathogenic targets of AD were identified using the DisGeNET database. Followed by compound and disease target overlapping, 77 targets were placed in that AKT1, EGFR, MMP9, TNF, PTGS2, MMP2, IGF1R, MCL1, MET and PARP1 was the top-ranked target, which was estimated by CytoHubba plug-in. The Molecular docking was performed for Q and Q3OG towards the PDB:1UNQ target. The binding score of Q and Q3OG was - 6.2 kcal/mol and - 6.58 kcal/mol respectively. Molecular dynamics simulation was conducted for Q and Q3OG towards the PDB:1UNQ target at 200 ns. This study's results help identify the multiple target sites for the bioactive compounds. Thus, synthesizing new chemical entity-based quercetin on structural modification may aid in eradicating AD complications.

Integrated Anchored Stapling and Hierarchical Dynamics: MSICDA-Driven CREBBP Bromodomain Inhibition

Despite recent success in the computational approaches of cyclic peptide design, current studies face challenges in modeling noncanonical amino acids and nonstandard cyclizations due to limited data. To address this challenge, we developed an integrated framework for the tailored design of stapled peptides (SPs) targeting the bromodomain of CREBBP (CREBBP-BrD). We introduce a powerful combination of anchored stapling and hierarchical molecular dynamics to design and optimize SPs by employing the MultiScale integrative conformational dynamics assessment (MSICDA) strategy, which involves an initial virtual screening of over 1.5 million SPs, followed by comprehensive simulations amounting to 154.54 μs across 5418 of instances. The MSICDA method provides a detailed and holistic stability view of peptide-protein interactions, systematically isolated optimized peptides and identified two leading candidates, DA#430 and DA#99409, characterized by their enhanced stability, optimized binding, and high affinity toward the CREBBP-BrD. In cell-free assays, DA#430 and DA#99409 exhibited 2- to 12-fold greater potency than inhibitor SGC-CBP30. Cell studies revealed higher peptide selectivity for cancerous versus normal cells over small molecules. DA#430 combined with (+)-JQ-1 showed promising synergistic effects. Our approach enables the identification of peptides with optimized binding, high affinity, and enhanced stability, leading to more precise and effective cyclic peptide design, thereby establishing MSICDA as a generalizable and transformative tool for uncovering novel targeted drug development in various therapeutic areas.

Predicting binding events in very flexible, allosteric, multi-domain proteins

Knowledge of the structures formed by proteins and small ligands is of fundamental importance for understanding molecular principles of chemotherapy and for designing new and more effective drugs. Due to the still high costs and to the several limitations of experimental techniques, it is most often desirable to predict these ligand-protein complexes in silico, particularly when screening for new putative drugs from databases of millions of compounds. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology aimed at generating bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites. Validation was performed on the enzyme adenylate kinase (ADK), a paradigmatic example of proteins that undergo very large conformational changes upon ligand binding. By only exploiting the unbound structure and the putative binding sites of the protein, we generated a significant fraction of bound-like structures, which employed in ensemble-docking calculations allowed to find native-like poses of substrates, inhibitors, and catalytically incompetent binders. Our protocol provides a general framework for the generation of bound-like conformations of flexible proteins that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein's activity. We foresee applications in virtual screening for difficult targets, prediction of the impact of amino acid mutations on structure and dynamics, and protein engineering.

Virtual screening of FOXO3a activators from natural product-like compound library

FOXO3a is an inevitable transcription factor, which is involved in the regulation of biological processes such as proliferation, DNA damage repair, cell cycle arrest and cell death. Previous studies confirmed that FOXO3a is an excellent tumor suppressor and in cancer cells, it gets phosphorylated followed by proteasomal degradation. FOXO3a is found to be inactivated in cancer cells, whereas in normal cells it gets activated and upregulates its downstream targets, which induces apoptotic pathways. Hence, activation of FOXO3a can be implicated in cancer prevention and treatment. A variety of commercially available FOXO3a activators such as doxorubicin and metformin possess undesirable adverse effects to normal cells and tissues, which are their major limitations. Natural bioactive compounds, eliminating the limitations of such compounds, become an excellent choice for the treatment and prevention of cancer. In this study, a library of natural product-like compounds was screened for their FOXO3a activation potential through in silico approach, which included the use of several bioinformatics tools and processes. Other molecular interaction studies as well as binding and specificity studies were carried out with the help of molecular dynamics simulation. Virtual screening of 7700 small molecules from the Natural Products-like Compound Library revealed the top three FOXO3a activators F3385-6269, F2183-0033 and F3351-0330. Further validation studies are warranted to confirm these findings.

Neurotoxic effects of carbamazepine on the mosquitofish Gambusia affinis

In recent years, the presence of pharmaceuticals in the aquatic environment has gained a significant attention. Carbamazepine, a commonly prescribed antiepileptic drug, has been consistently found in aquatic environments at concentrations ranging from nanograms to micrograms, raising concerns about its potential negative impacts on aquatic organisms. The study examined the acute and chronic neurotoxic effects of environmentally relevant and sublethal concentrations of carbamazepine in the mosquitofish Gambusia affinis. After a 96-hour exposure period, the median lethal concentration (LC50) of carbamazepine for G. affinis was determined as 24 mg L - 1. For the current study, sublethal concentrations i.e., one-tenth (2.4 mg L - 1) and one-fifth (4.8 mg L - 1) of the LC50 value were chosen for assessing the neurotoxic effects along with the environmentally relevant concentration (13 ng L - 1). The research findings indicated that carbamazepine had a disruptive impact on the typical growth and behavior of the fish. During the acute exposure phase, physical deformities were observed in the fish, resulting in neonatal and postneonatal fatalities. Furthermore, the neurotoxic effects of carbamazepine were clearly demonstrated through alterations in various neurological parameters, including acetylcholinesterase, dopamine, gamma-aminobutyric acid, serotonin, monoamine oxidase, 5-hydroxyindole acetic acid, adrenaline, and nor-adrenaline. These findings raise concerns about the survival of fish populations in their natural environment.

Design, synthesis, biological evaluation and molecular docking studies of quinoline-anthranilic acid hybrids as potent anti-inflammatory drugs

Despite the high global prevalence, rheumatoid arthritis lacks a satisfactory treatment. Hence, the present study is undertaken to design and synthesize novel anti-inflammatory compounds. For this, quinoline and anthranilic acid, two medicinally-privileged moieties, were linked by pharmacophore hybridization, and following their computational assessments, three hybrids 5a-c were synthesized in good over all yields. The in vitro and in vivo anti-inflammatory potential of these hybrids was determined by anti-denaturation and anti-proteinase, and carrageenan-induced paw edema models. The computational studies of these hybrids revealed their drug-likeness, optimum pharmacokinetics, and less toxicity. Moreover, they demonstrated high binding affinity (-9.4 to -10.6 kcal mol-1) and suitable binding interactions for TNF-α, FLAP, and COX-II. A three-step synthetic route resulted in the hybrids 5a-c with 83-86% yield of final step. At 50 μg mL-1, the antiprotease and anti-denaturation activity of compound 5b was significantly higher than 5a and 5c. Furthermore, 5b significantly reduced the edema in the right paw of the rats that received carrageenan. The results of this study indicate the medicinal worth of the novel hybrids in treating inflammatory disorders such as rheumatoid arthritis.

Biophysical and Biochemical Characterization of Structurally Diverse Small Molecule Hits for KRAS Inhibition

We describe six compounds as early hits for the development of direct inhibitors of KRAS, an important anticancer drug target. We show that these compounds bind to KRAS with affinities in the low micromolar range and exert different effects on its interactions with binding partners. Some of the compounds exhibit selective binding to the activated form of KRAS and inhibit signal transduction through both the MAPK or the phosphatidylinositide 3-kinase PI3K-protein kinase B (AKT) pathway in cells expressing mutant KRAS. Most inhibit intrinsic and/or SOS-mediated KRAS activation while others inhibit RAS-effector interaction. We propose these compounds as starting points for the development of non-covalent allosteric KRAS inhibitors.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Centaurea mersinensis phytochemical composition and multi-dimensional bioactivity properties supported by molecular modeling

Various studies conducted on Centaurea species indicate that the relevant plant is good source of bioactive phytochemicals. In this study, in vitro studies were used to determine bioactivity properties of methanol extract of Centaurea mersinensis - endemic species in Turkey - on extensive basis. Furthermore, the interaction of target molecules, identified for breast cancer and phytochemicals in the extract, was investigated via in silico analyses to support findings received in vitro. Scutellarin, quercimeritrin, chlorogenic acid and baicalin were primary phytochemicals in the extract. Methanol extract and scutellarin had higher cytotoxic effects against MCF-7 (IC50=22.17 µg/mL, and IC50=8.25 µM, respectively), compared to other breast cancer cell lines (MDA-MB-231, SKBR-3). The extract had strong antioxidant properties and inhibited target enzymes, especially α-amylase (371.69 mg AKE/g extract). The results of molecular docking indicate that main compounds of extract show high-strength bonding to the c-Kit tyrosine among target molecules identified in breast cancer, compared to other target molecules (MMP-2, MMP-9, VEGFR2 kinase, Aurora-A kinase, HER2). The tyrosinase kinase (1T46)-Scutellarin complex showed considerable stability in 150 ns simulation as per MD findings, and it was coherent with optimal docking findings. Docking findings and HOMO-LUMO analysis results corresponds with in vitro experiments. Medicinal properties of phytochemicals, which was determined to be suitable for oral use along with ADMET, were found to be within normal limits except for their polarity properties. In conclusion, in vitro and in silico studies indicated that the relevant plant yields promising results regarding its potential to develop novel and effective medicational products.Communicated by Ramaswamy H. Sarma.

Design, synthesis and evaluation of aminothiazole derivatives as potential anti-Alzheimer's candidates

Aim: The objective of the present study was to design, synthesize and evaluate diverse Schiff bases and thiazolidin-4-one derivatives of aminothiazole as key pharmacophores possessing acetylcholinesterase inhibitory activity. Materials & methods: Two series of compounds (13 each) were synthesized and evaluated for their acetylcholinesterase inhibition and antioxidant activity. Molecular docking of all compounds was performed to provide an insight into their binding interactions. Results: Compounds 2j (IC50 = 0.03 μM) and 3e (IC50 = 1.58 μM) were found to be the best acetylcholinesterase inhibitors among compounds of their respective series. Molecular docking analysis supported the results of in vitro activity by displaying good docking scores with the binding pocket of human acetylcholinesterase (Protein Data Bank ID: 4EY7). Conclusion: Compound 2j emerged as a potential lead compound with excellent acetylcholinesterase inhibition, antioxidant and chelation activity.

Computational insights into KRAS G12C inhibition: exploring possible repurposing of Azacitidine and Ribavirin

Kirsten rat sarcoma (KRAS) stands out as the most prevalent mutated oncogene, playing a crucial role in the initiation and progression of various cancer types, including colorectal, lung and pancreatic cancer. The oncogenic modifications of KRAS are intricately linked to tumor development and are identified in 22% of cancer patients. This has spurred the necessity to explore inhibition mechanisms, with the aim of investigating and repurposing existing drugs for diagnosing cancers dependent on KRAS G12C In this investigation, 26 nucleoside-based drugs were collected from literature to assess their effectiveness against KRAS G12C. The study incorporates in-silico molecular simulations and molecular docking examinations of these nucleoside-derived drugs with the KRAS G12C protein using Protein Data Bank (PDB) ID: 5V71. The docking outcomes indicated that two drugs, Azacitidine and Ribavirin, exhibited substantial binding affinities of -8.7 and -8.3 kcal/mol, respectively. These drugs demonstrated stability in binding to the active site of the protein during simulation studies. Root mean square deviation (RMSD) analyses indicated that the complexes closely adhered to an equilibrium RMSD value ranging from 0.17 to 0.2 nm. Additionally, % occupancies, bond angles and the length of hydrogen bonds were calculated. These findings suggest that Azacitidine and Ribavirin may potentially serve as candidates for repurposing in individuals with KRAS-dependent cancers.Communicated by Ramaswamy H. Sarma.

Rational engineering and insight for a L-glutaminase activity reduced type II L-asparaginase from Bacillus licheniformis and its antileukemic activity in vitro

Type II L-asparaginase (ASNase) has been approved by the FDA for treating acute lymphoid leukemia (ALL), but its therapeutic effect is limited by low catalytic efficiency and L-glutaminase (L-Gln) activity. This study utilized free energy based molecular dynamics calculations to identify residues associated with substrate binding in Bacillus licheniformis L-asparaginase II (BLASNase) with high catalytical activity. After saturation and combination mutagenesis, the mutant LGT (74 L/75G/111 T) with intensively reduced l-glutamine catalytic activity was generated. The l-glutamine/L-asparagine activity (L-Gln/L-Asn) of LGT was only 6.6 % of parent BLASNase, whereas the L-asparagine (L-Asn) activity was preserved >90 %. Furthermore, structural comparison and molecular dynamics calculations indicated that the mutant LGT had reduced binding ability and affinity towards l-glutamine. To evaluate its effect on acute leukemic cells, LGT was supplied in treating MOLT-4 cells. The experimental results demonstrated that LGT was more cytotoxic and promoted apoptosis compared with commercial Escherichia coli ASNase. Overall, our findings firstly provide insights into reducing l-glutamine activity without impacting L-asparagine activity for BLASNase to possess remarkable potential for anti-leukemia therapy.

CarsiDock: a deep learning paradigm for accurate protein-ligand docking and screening based on large-scale pre-training

The expertise accumulated in deep neural network-based structure prediction has been widely transferred to the field of protein-ligand binding pose prediction, thus leading to the emergence of a variety of deep learning-guided docking models for predicting protein-ligand binding poses without relying on heavy sampling. However, their prediction accuracy and applicability are still far from satisfactory, partially due to the lack of protein-ligand binding complex data. To this end, we create a large-scale complex dataset containing ∼9 M protein-ligand docking complexes for pre-training, and propose CarsiDock, the first deep learning-guided docking approach that leverages pre-training of millions of predicted protein-ligand complexes. CarsiDock contains two main stages, i.e., a deep learning model for the prediction of protein-ligand atomic distance matrices, and a translation, rotation and torsion-guided geometry optimization procedure to reconstruct the matrices into a credible binding pose. The pre-training and multiple innovative architectural designs facilitate the dramatically improved docking accuracy of our approach over the baselines in terms of multiple docking scenarios, thereby contributing to its outstanding early recognition performance in several retrospective virtual screening campaigns. Further explorations demonstrate that CarsiDock can not only guarantee the topological reliability of the binding poses but also successfully reproduce the crucial interactions in crystalized structures, highlighting its superior applicability.

Deciphering the Molecular Association of Human CRIP1a with an Agonist-Bound Cannabinoid Receptor 1

Cannabinoid receptor 1 (CB1) is a class A G-protein-coupled receptor that plays important roles in several physiological and pathophysiological processes. Therefore, targeted regulation of CB1 activity is a potential therapeutic strategy for several diseases, including neurological disorders. Apart from cannabinoid ligands, CB1 signaling can also be regulated by different CB1-associated proteins. In particular, the cannabinoid receptor interacting protein 1a (CRIP1a) associates with an activated CB1 receptor and alters the G-protein selectivity, thereby reducing the agonist-mediated signal transduction of the CB1 receptor. Experimental evidence suggests that two peptides corresponding to the distal and central C-terminal segments of CB1 could interact with CRIP1a. However, our knowledge of the molecular basis of CB1-CRIP1a recognition is still limited. In this work, we use an extensive combination of computational methods to build the first comprehensive atomistic model human CB1-CRIP1a complex. Our model provides novel structural insights into the interactions of CRIP1a with a membrane-embedded, complete, agonist-bound CB1 receptor in humans. Our results highlight the key residues that stabilize the CB1-CRIP1a complex, which will be useful to guide in vitro mutagenesis experiments. Furthermore, our human CB1-CRIP1a complex presents a model system for structure-based drug design to target this physiologically important complex for modulating CB1 activity.

Natural flavonoids as promising 6-phosphogluconate dehydrogenase inhibitor candidates: In silico and in vitro assessments

The primary strategy in the fight against cancer is to screen compounds that may be effective on different types of cancer. Compounds from plants seem to be a good source. The present study investigated the inhibitory effects of some flavonoids on the 6-phosphogluconate dehydrogenase (6-PGD) enzyme. We determined that quercetin, myricetin, fisetin, morin, apigenin, and baicalein exhibited powerful inhibition effects with IC50 values between 4.08 and 21.26 µM, while luteolin, kaempferol, apiin, galangin, and baicalin showed moderate effects with IC50 values between 54.15 and 138.91 µM. Quercetin competitively inhibited the binding of NADP and 6-phosphogluconate to the 6-PGD enzyme with Ki values of 0.527 ± 0.251 and 0.374 ± 0.138 µM, respectively. We calculated Ki values using the Cheng-Prusoff equation as between 0.44 and 14.88 µM. The possible interaction details of polyphenols with the active site of 6-PGD were analyzed with docking software. In silico and in vitro studies indicated that the -OH groups on the A and C ring of flavonoids bind to the enzyme's active site via hydrogen bonding, while the -OH groups on the C ring contributed significantly to the increase in the inhibitory potentials of the molecules. Molecular dynamic simulations tested the stability of the 6-PGD-quercetin complex during 100 ns. These phytochemicals were suitable for drug use when optimized with absorption, distribution, metabolism, excretion, and toxicity (ADMET) criteria. The effects of the studied compounds on cancer cell lines of potential targets were demonstrated by network analysis. In conclusion, this study suggests that flavonoids found to be potent inhibitors could serve as leading candidates to treat many cancers via 6-PGD inhibition.

Molecular simulation for food protein-ligand interactions: A comprehensive review on principles, current applications, and emerging trends

In recent years, investigations on molecular interaction mechanisms between food proteins and ligands have attracted much interest. The interaction mechanisms can supply much useful information for many fields in the food industry, including nutrient delivery, food processing, auxiliary detection, and others. Molecular simulation has offered extraordinary insights into the interaction mechanisms. It can reflect binding conformation, interaction forces, binding affinity, key residues, and other information that physicochemical experiments cannot reveal in a fast and detailed manner. The simulation results have proven to be consistent with the results of physicochemical experiments. Molecular simulation holds great potential for future applications in the field of food protein-ligand interactions. This review elaborates on the principles of molecular docking and molecular dynamics simulation. Besides, their applications in food protein-ligand interactions are summarized. Furthermore, challenges, perspectives, and trends in molecular simulation of food protein-ligand interactions are proposed. Based on the results of molecular simulation, the mechanisms of interfacial behavior, enzyme-substrate binding, and structural changes during food processing can be reflected, and strategies for hazardous substance detection and food flavor adjustment can be generated. Moreover, molecular simulation can accelerate food development and reduce animal experiments. However, there are still several challenges to applying molecular simulation to food protein-ligand interaction research. The future trends will be a combination of international cooperation and data sharing, quantum mechanics/molecular mechanics, advanced computational techniques, and machine learning, which contribute to promoting food protein-ligand interaction simulation. Overall, the use of molecular simulation to study food protein-ligand interactions has a promising prospect.

Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry

Protein-based therapeutics have revolutionized the pharmaceutical industry and become vital components in the development of future therapeutics. They offer several advantages over traditional small molecule drugs, including high affinity, potency and specificity, while demonstrating low toxicity and minimal adverse effects. However, the development and manufacturing processes of protein-based therapeutics presents challenges related to protein folding, purification, stability and immunogenicity that should be addressed. These proteins, like other biological molecules, are prone to chemical and physical instabilities. The stability of protein-based drugs throughout the entire manufacturing, storage and delivery process is essential. The occurrence of structural instability resulting from misfolding, unfolding, and modifications, as well as aggregation, poses a significant risk to the efficacy of these drugs, overshadowing their promising attributes. Gaining insight into structural alterations caused by aggregation and their impact on immunogenicity is vital for the advancement and refinement of protein therapeutics. Hence, in this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.

Synthesis, biological assessment, and computational investigations of nifedipine and monastrol analogues as anti-leishmanial major and anti-microbial agents

Leishmaniasis includes a range of parasitic diseases caused by numerous types of the protozoan kinetoplastid parasite. Fungal and bacterial pathogens have led to infectious illnesses causing some main public health problem in current years. A series of dihydropyridine and tetrahydropyrimidine derivatives having fluoro, bromo, and nitro substituents at para-phenyl ring on C4 of dihydropyridine and tetrahydropyrimidine rings were synthesized. Then, anti-leishmanial and antimicrobial potencies of compounds were assessed. All compounds were synthesized via Hantzsch and Biginelli reactions. All derivatives were evaluated for their anti-leishmanial and antimicrobial activities. Moreover, docking and molecular dynamics simulation calculations of the compounds in PRT1 binding site were performed to report the results of anti-leishmanial and antimicrobial activities. Compounds 4a and 4b showed the highest anti-amastigote and anti-promastigote activities. Compound 4a revealed the highest antimicrobial activity against E. coli, P. aeruginosa, and C. albicans strains. In addition, compound 4c showed the highest activity against S. aureus. The fluoro, bromo, and nitro substituents in para-position of phenyl group at C4 of dihydropyridine and tetrahydropyrimidine moieties as well as the bulk and length of the chain linking to the ester moieties are essential for anti-leishmanial and anti-microbial activities of these derivatives. Low cytotoxicity was shown by most of derivatives against macrophages. The molecular docking studies were in agreement with in vitro assay. Moreover, hydrogen binds, RMSF, RMSD, and Rg, strongly showed the steady binding of 4a and 4b compounds in PRT1 active site.

In Silico and In Vitro Analyses to Repurpose Quercetin as a Human Pancreatic α-Amylase Inhibitor

Human pancreatic α-amylase (HPA), situated at the apex of the starch digestion hierarchy, is an attractive therapeutic approach to precisely regulate blood glucose levels, thereby efficiently managing diabetes. Polyphenols offer a natural and multifaceted approach to moderate postprandial sugar spikes, with their slight modulation in carbohydrate digestion and potential secondary benefits, such as antioxidant and anti-inflammatory effects. Taking into consideration the unfavorable side effects of currently available commercial medications, we aimed to study a library of polyphenols attributed to their remarkable antidiabetic properties and screened the most potent HPA inhibitor via a comprehensive in silico study encompassing molecular docking, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) calculation, molecular dynamics (MD) simulation, density functional theory (DFT) study, and pharmacokinetic properties followed by an in vitro assay. Significant hydrogen bonding with the catalytic triad residues of HPA, prominent MM/GBSA binding energy of -27.03 kcal/mol, and the stable nature of the protein-ligand complex with regard to 100 ns MD simulation screened quercetin as the best HPA inhibitor. Additionally, quercetin showed strong reactivity in the substrate-binding pocket of HPA and exhibited favorable pharmacokinetic properties with a considerable inhibitory concentration (IC50) of 57.37 ± 0.9 μg/mL against α-amylase. This study holds prospects for HPA inhibition and suggests quercetin as an approach to therapy for diabetes; however, it is imperative to conduct further research.

Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 Mpro

Molecular dynamics (MD) simulations, which are central to drug discovery, offer detailed insights into protein-ligand interactions. However, analyzing large MD datasets remains a challenge. Current machine-learning solutions are predominantly supervised and have data labelling and standardisation issues. In this study, we adopted an unsupervised deep-learning framework, previously benchmarked for rigid proteins, to study the more flexible SARS-CoV-2 main protease (Mpro). We ran MD simulations of Mpro with various ligands and refined the data by focusing on binding-site residues and time frames in stable protein conformations. The optimal descriptor chosen was the distance between the residues and the center of the binding pocket. Using this approach, a local dynamic ensemble was generated and fed into our neural network to compute Wasserstein distances across system pairs, revealing ligand-induced conformational differences in Mpro. Dimensionality reduction yielded an embedding map that correlated ligand-induced dynamics and binding affinity. Notably, the high-affinity compounds showed pronounced effects on the protein's conformations. We also identified the key residues that contributed to these differences. Our findings emphasize the potential of combining unsupervised deep learning with MD simulations to extract valuable information and accelerate drug discovery.

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.

Asiaticoside A for the modulation of 1-TbAd- a potential target and ligand for extensive drug resistance Mycobacterium tuberculosis

In nature, terpene nucleosides are relatively rare, with 1-tuberculosinyladenosine (1-TbAd) being an exclusive feature of Mycobacterium tuberculosis (Mtb). The convergence of nucleosides and terpene pathways in the Mtb complex appears to have emerged late in its evolutionary history. 1-TbAd (PDB ID: 3WQK) is a prominent chemical marker for Mtb and may contribute to its virulence-related properties when exported extracellularly. We gathered a comprehensive set of 270 phytochemicals from diverse Ayurvedic texts and treatment traditions. Subsequently, we conducted structure-based molecular docking analyses to identify compounds exhibiting the strongest binding affinity for 1-TbAd, highlighting their potential as drug candidates. These selected compounds were further subjected to an in-vitro growth inhibition assay against the reference strain Mycobacterium tuberculosis h37rv. Among the candidates, Asiaticoside A (ASA) emerged as a promising candidate from the pool of 270 compounds. To assess the impact of ASA on 1-TbAd expression, we employed a PCR-based mRNA expression assay, revealing ASA's ability to downregulate 1-TbAd expression in extensively drug-resistant MTb strains. Remarkably, the conventional drug rifampin showed no such effectiveness in our experiments. We further conducted molecular dynamic simulations to explore the interaction between ASA and 1-TbAd in a cellular-like environment, confirming the stability of their interaction. Also, we predicted ASA's stability toward causing inducing the random mutations in the target gene. With this, we propose a novel target and its modulator to treat extensively drug-resistant MTB.

In silico structural elucidation of Nipah virus L protein and targeting RNA-dependent RNA polymerase domain by nucleoside analogs

The large (L) protein of Mononegavirales is a multi-domain protein that performs transcription and genome replication. One of the important domains in L is the RNA-dependent RNA polymerase (RdRp), a promising target for antiviral drugs. In this work, we employed rigorous computational comparative modeling to predict the structure of L protein of Nipah virus (NiV). The RdRp domain was targeted by a panel of nucleotide analogs, previously reported to inhibit different viral RNA polymerases, using molecular docking. Best binder compounds were subjected to molecular dynamics simulation to validate their binding. Molecular mechanics/generalized-born surface area (MM/GBSA) calculations estimated the binding free energy. The predicted model of NiV L has an excellent quality as judged by physics- and knowledge-based validation tests. Galidesivir, AT-9010 and Norov-29 scored the top nucleotide analogs to bind to the RdRp. Their binding free energies obtained by MM/GBSA (-31.01 ± 3.9 to -38.37 ± 4.8 kcal/mol) ranked Norov-29 as the best potential inhibitor. Purine nucleotide analogs are expected to harbor the scaffold for an effective drug against NiV. Finally, this study is expected to provide a start point for medicinal chemistry and drug discovery campaigns toward identification of effective chemotherapeutic agent(s) against NiV.Communicated by Ramaswamy H. Sarma.

Evaluation of in vitro effect, molecular docking, and molecular dynamics simulations of some dihydropyridine-class calcium channel blockers on human serum paraoxonase 1 (hPON1) enzyme activity

Paraoxonase 1 (PON1) was purified 148.80-fold in 37.92% yield by hydrophobic interaction chromatography technique. The purity of PON1 was checked by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) with a single band of 43 kDa. The in vitro effects of nine different calcium channel blockers on PON1 activity were evaluated. All drugs strongly decreased PON1 activity, and IC50 levels were between 13.987 ± 0.59 and 238.104 ± 2.14 μM, Ki values between 8.58 ± 0.36 and 111 ± 1.27 μM. The drugs with the strongest inhibitory effect were nisoldipine with 13.987 ± 0.59 μM and nicardipine with 20.158 ± 0.43 μM. The mechanism of action for the inhibition of the enzyme by nisoldipine and nicardipine was investigated through molecular docking. The stability of enzyme-ligand complexes obtained from the docking was explored through molecular dynamics simulation. The binding affinity of the ligands toward the enzyme was also investigated through MMPBSA (molecular mechanics Poisson-Boltzmann surface area method). The computational analysis demonstrated these compounds could inhibit the enzyme. Nisoldipine had the strongest binding, and its complex was the most stable one. Furthermore, nicardipine was found to have the highest affinity toward the enzyme.

Rumex vesicarius L. boosts the effectiveness of sorafenib in triple-negative breast cancer by downregulating BCl2, mTOR, and JNK, and upregulating p21 expression

BACKGROUND/AIM

Triple-negative breast cancer (TNBC) is characterized by poor prognosis, rapid progression, serious clinical behavior, an elevated risk of metastasis, and resistance to standard treatments. Traditional medicine practitioners value Rumex vesicarius L. (RMV) for a variety of reasons, including the plant's antioxidant capabilities. Our study's goals were to ascertain the efficacy of RMV alone and in combination with sorafenib (SOR) against the aggressive TNBC cell line (MDA-MB-231) and use in vitro and in silico analysis to deduce the fundamental mechanism of action.

METHODS

In the current study, molecular operating environment (MOE, 2019.0102) software was used for performing molecular docking. The MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay was used to determine the cytotoxicity of RMV, SOR or RMV/SOR combination against the TNBC cell line MDA-MB-231 cells. The effects of RMV, SOR, and RMV and SOR combining on mRNAs expressions of the target genes including mTOR, p21, JNK, and BCl2 were evaluated. In TNBC cells, the relative expressions of mRNAs of the genes were examined by using real-time quantitative polymerase chain reaction (RT-qPCR).

RESULTS

In our experiments, we discovered that both RMV extracts alone and in combination with SOR considerably reduced cancer cell proliferation (IC50 = 0.83 and 0.19 μM, respectively). Additionally, the expression of the tumor suppressor gene p21 was elevated whereas the expression of the invasion and anti-apoptosis genes BCl2, mTOR, and JNK were significantly decreased after treatment with RMV and SOR. Based on in silico analysis, it was found that RMV extract contains bioactive chemicals with a high affinity for inhibiting JNK and VEGFR-2.

CONCLUSION

In conclusion, in vitro and in silico investigations show that the RMV extract improves the anticancer efficiency of SOR through molecular processes involving the downregulation of mTOR, BCl2, and JNK1 and overexpression of p21 tumor suppressor gene. Finally, we suggest conducting additional in vivo investigations on RMV and its bioactive components to verify their potential in cancer therapy.

An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.

In Silico Bioinformatics Analysis on the Role of Long Non-Coding RNAs as Drivers and Gatekeepers of Androgen-Independent Prostate Cancer Using LNCaP and PC-3 Cells

Prostate cancer (PCa) is the leading cancer in men globally. The association between PCa and long non-coding RNAs (lncRNAs) has been reported. Aberrantly expressed lncRNAs have been documented in each of the cancer "hallmarks". Androgen signaling plays an important role in PCa progression. This study aimed to profile the aberrantly expressed lncRNAs in androgen-dependent (LNCaP) PCa compared to androgen-independent (PC-3) PCa cells. This was achieved by using a 384-well plate of PCa lncRNA gene panel. Differential expression of ±2 up or downregulation was determined using the CFX Maestro software v2.1. LncSEA and DIANA-miRPath were used to identify the enriched pathways. Telomerase RNA component (TERC) lncRNA was illustrated to participate in various tumourigenic classes by in silico bioinformatics analysis and was thus selected for validation using RT-qPCR. Further bioinformatics analysis revealed the involvement of differentially expressed lncRNAs in oncogenic pathways. Some lncRNAs undergo hypermethylation, others are encapsulated by exosomes, while others interact with several microRNAs (miRNAs), favouring tumourigenic pathways. Notably, TERC lncRNA was shown to interact with tumour-suppressor miRNAs hsa-miR-4429 and hsa-miR-320b. This interaction in turn activates TGF-β-signaling and ECM-receptor interaction pathways, favouring the progression of PCa. Understanding lncRNAs as competitive endogenous RNA molecules and their interactions with miRNAs may aid in the identification of novel prognostic PCa biomarkers and therapeutic targets.

Dynamics of the Apo µ-Opioid Receptor in Complex with Gi Protein

Opioid receptors, particularly the µ-opioid receptor (μOR), play a pivotal role in mediating the analgesic and addictive effects of opioid drugs. G protein signaling is an important pathway of μOR function, usually associated with painkilling effects. However, the molecular mechanisms underlying the interaction between the μOR and G protein remain poorly understood. In this study, we employed classical all-atom molecular dynamics simulations to investigate the structural changes occurring with the μOR-G protein complex under two different conditions: with the G protein in the apo form (open) and with the GDP bound G protein (closed, holo form). The receptor was in the apo form and active conformation in both cases, and the simulation time comprised 1µs for each system. In order to assess the effect of the G protein coupling on the receptor activation state, three parameters were monitored: the correlation of the distance between TM3 and TM6 and the RMSD of the NPxxYA motif; the universal activation index (A100); and the χ2 dihedral distribution of residue W2936.48. When complexed with the open G protein, receptor conformations with intermediate activation state prevailed throughout the molecular dynamics, whereas in the condition with the closed G protein, mostly inactive conformations of the receptor were observed. The major effect of the G protein in the receptor conformation comes from a steric hindrance involving an intracellular loop of the receptor and a β-sheet region of the G protein. This suggests that G-protein precoupling is essential for receptor activation, but this fact is not sufficient for complete receptor activation.

Chalcones reloaded: an integration of network pharmacology and molecular docking for type 2 diabetes therapy

Chalcones have various biological effects, from immune boosting to anti-cancer and anti-diabetic. Structurally modified chalcones (SMC) are clinically relevant for diabetes and cardiometabolic complications. From the original research articles, a structurally proven and biologically outstanding 14 structurally modified chalcones were screened and inducted in this study. This study evaluated the effects of SMC towards diabetes via network pharmacology analysis. The network data shows compounds S2, S3, S5, S9 &S12 suit the diabetes target. Especially Compounds S5 and S9 have a higher binding affinity towards the targets of TNF, PI3K, MAPK1 and AKT1 active sites. Compound S9 [(E)-3-(4-(1H-imidazol-1-yl)phenyl)-1-(4-(2,4-difluorobenz-yloxy)phenyl)prop-2-en-1-one] have identified with stronger binding affinities towards the active sites of MAPK3 (PDB:4QTB) -10.5(Kcal/mol). To provide a more effective mechanism for demonstrating protein-ligand interaction, one of the molecular docking complex (ERK2 kinase-S5) was subjected to a molecular dynamic at 300K for 100 ns. In term of structural stability, structure compactness, residual flexibility and hydrogen bond interaction of the complex was evaluated Integrating network pharmacology, in silico virtual screening, and molecular docking analysis shows that structurally modified compounds are effective and may help identify lead compounds towards glycemic control.Communicated by Ramaswamy H. Sarma.

MD-Ligand-Receptor: A High-Performance Computing Tool for Characterizing Ligand-Receptor Binding Interactions in Molecular Dynamics Trajectories

Molecular dynamics simulation is a widely employed computational technique for studying the dynamic behavior of molecular systems over time. By simulating macromolecular biological systems consisting of a drug, a receptor and a solvated environment with thousands of water molecules, MD allows for realistic ligand-receptor binding interactions (lrbi) to be studied. In this study, we present MD-ligand-receptor (MDLR), a state-of-the-art software designed to explore the intricate interactions between ligands and receptors over time using molecular dynamics trajectories. Unlike traditional static analysis tools, MDLR goes beyond simply taking a snapshot of ligand-receptor binding interactions (lrbi), uncovering long-lasting molecular interactions and predicting the time-dependent inhibitory activity of specific drugs. With MDLR, researchers can gain insights into the dynamic behavior of complex ligand-receptor systems. Our pipeline is optimized for high-performance computing, capable of efficiently processing vast molecular dynamics trajectories on multicore Linux servers or even multinode HPC clusters. In the latter case, MDLR allows the user to analyze large trajectories in a very short time. To facilitate the exploration and visualization of lrbi, we provide an intuitive Python notebook (Jupyter), which allows users to examine and interpret the results through various graphical representations.

Hydration and Structural Adaptations of the Human CYP1A1, CYP1A2, and CYP1B1 Active Sites by Molecular Dynamics Simulations

Cytochromes CYP1A1, CYP1A2, and CYP1B1, the members of the cytochrome P450 family 1, catalyze the metabolism of endogenous compounds, drugs, and non-drug xenobiotics which include substances involved in the process of carcinogenesis, cancer chemoprevention, and therapy. In the present study, the interactions of three selected polymethoxy-trans-stilbenes, analogs of a bioactive polyphenol trans-resveratrol (3,5,4'-trihydroxy-trans-stilbene) with the binding sites of CYP1 isozymes were investigated with molecular dynamics (MD) simulations. The most pronounced structural changes in the CYP1 binding sites were observed in two substrate recognition sites (SRS): SRS2 (helix F) and SRS3 (helix G). MD simulations show that the number and position of water molecules occurring in CYP1 APO and in the structures complexed with ligands are diverse. The presence of water in binding sites results in the formation of water-protein, water-ligand, and bridging ligand-water-protein hydrogen bonds. Analysis of the solvent and substrate channels opening during the MD simulation showed significant differences between cytochromes in relation to the solvent channel and the substrate channels 2c, 2ac, and 2f. The results of this investigation lead to a deeper understanding of the molecular processes that occur in the CYP1 binding sites and may be useful for further molecular studies of CYP1 functions.

AI in drug discovery and its clinical relevance

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.

Design, synthesis and anti-Tb evaluation of chalcone derivatives as novel inhibitors of InhA

A series of halogenated chalcone derivatives were designed and developed for anti-tubercular activity. Novel molecules were designed and in-silico screening were performed using admetSAR, SwissADME, and Osiris Property Explorer. From the initial filter the top 10 compounds were docked using the Autodock tool 1.5.6. and the binding energies of the docked compounds were higher than the standard drugs Isoniazid.and Ethionamide. Based on the in-silico and docking results, the top halogenated chalcones were synthesized and characterized using FT-IR, mass spectrometry, 1H, and 13C NMR spectroscopy. The chalcones were further evaluated for anti-tubercular activity using MABA against the H37Rv strain. Among the series of compounds, DK12 and DK14 showed potent in-vitro activity, with MICs of 0.8 µg/ml, in comparison with 1.6 µg/ml of the first-line drug Isoniazid. Further molecular dynamics simulations studies for 100 ns revealed that the key interaction with TYR 158 were observed in both DK12 and DK14 in the InhA active site. The compound DK12 further showed significant interactions with PHE 149 and ARG 153 residues and is a hit molecule among the series. Further DK12 and DK14 does not show any significance toxicity. The compounds DK12 needs to be optimized and further investigation to be carried out against InhA.Communicated by Ramaswamy H. Sarma.

Triiodothyronine positively regulates endoplasmic reticulum-associated degradation (ERAD) and promotes androgenic signaling in androgen-dependent prostate cancer cells

Thyroid hormones (THs) play crucial roles in numerous physiological processes of nearly all mammalian tissues, including differentiation and metabolism. Deterioration of TH signaling has been associated with several pathologies, including cancer. The effect of highly active triiodothyronine (T3) has been investigated in many in vivo and in vitro cancer models. However, the role of T3 on cancerous prostate tissue is controversial. Recent studies have focused on the characterization of the supportive roles of the endoplasmic reticulum-associated degradation (ERAD) and unfolded protein response (UPR) signaling in prostate cancer (PCa) and investigating new hormonal regulation patterns, including estrogen, progesterone and 1,25(OH)₂D₃. Additionally, androgenic signaling controlled by androgens, which are critical in PCa progression, has been shown to be regulated by other steroid hormones. While the effects of T3 on ERAD and UPR are unknown today, the impact on androgenic signaling is still not understood in PCa. Therefore, we aimed to investigate the molecular action of T3 on the ERAD mechanism and UPR signaling in PCa cells and also extensively examined the effect of T3 on androgenic signaling. Our data strongly indicated that T3 tightly regulates ERAD and UPR signaling in androgen-dependent PCa cells. We also found that T3 stimulates androgenic signaling by upregulating AR mRNA and protein levels and enhancing its nuclear translocation. Additionally, advanced computational studies supported the ligand binding effect of T3 on AR protein. Our data suggest that targeting thyroidal signaling should be considered in therapeutic approaches to be developed for prostate malignancy in addition to other steroidal regulations.

Structural insights into SARS-CoV-2 main protease conformational plasticity

The spread of different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants underscores the need for insights into the structural properties of its structural and non-structural proteins. The highly conserved homo-dimeric chymotrypsin-like protease (3CL M^PRO ), belonging to the class of cysteine hydrolases, plays an indispensable role in processing viral polyproteins that are involved in viral replication and transcription. Studies have successfully demonstrated the role of M^PRO as an attractive drug target for designing antiviral treatments because of its importance in the viral life cycle. Herein, we report the structural dynamics of six experimentally solved structures of M^PRO (i.e., 6LU7, 6M03, 6WQF, 6Y2E, 6Y84, and 7BUY including both ligand-free and ligand-bound states) at different resolutions. We have employed a structure-based balanced forcefield, CHARMM36m through state-of-the-art all-atoms molecular dynamics simulations at µ-seconds scale at room temperature (303K) and pH 7.0 to explore their structure-function relationship. The helical domain-III responsible for dimerization mostly contributes to the altered conformational states and destabilization of M^PRO . A keen observation of the high degree of flexibility in the P5 binding pocket adjoining domain II-III highlights the reason for observation of conformational heterogeneity among the structural ensembles of M^PRO . We also observe a differential dynamics of the catalytic pocket residues His41, Cys145, and Asp187, which may lead to catalytic impairment of the monomeric proteases. Among the highly populated conformational states of the six systems, 6LU7 and 7M03 forms the most stable and compact M^PRO conformation with intact catalytic site and structural integrity. Altogether, our findings from this extensive study provides a benchmark to identify physiologically relevant structures of such promising drug targets for structure-based drug design and discovery of potent drug-like compounds having clinical potential.

Molecular docking, QSAR, pharmacophore modeling, and dynamics studies of some chromone derivatives for the discovery of anti-breast cancer agents against hormone-dependent breast cancer

In search of new anti-breast cancer agents, the present study envisaged the design and synthesis of a series of benzopyran-chalcones. All the synthesized compounds were assayed for their in-vitro anticancer activity against ER + MCF-7 and triple-negative MDA-MB-231 breast cancer cell lines using SRB assay. The synthesized compounds were found active against ER + MCF-7 cell lines. Based on the in-vitro data, in-silico analysis was performed using hormone-dependent breast cancer targets such as hER-α and aromatase because the compounds showed activity against MCF-7 cells and none was active against MDA-MB-231. The in-silico results supported the in-vitro anticancer activity suggesting the affinity of compounds toward hormone-dependant breast cancer. Compounds 4A1 to 4A3 were found to be most cytotoxic to MCF-7 cells with IC50 values of 31.87, 22.95, and 20.34 μg/ml, respectively (Doxorubicin IC50: <10 μg/ml). In addition, they showed the interactions with the amino acid residues of a binding cavity of an hER-α. Furthermore, quantitative structure-activity relationship (QSAR) studies were performed to reveal the vital structural features required for anticancer activity against breast cancer. Molecular dynamic simulation studies of hER-α and 4A3 in comparison with the raloxifene complex ensure the appropriate refinement of compounds in the dynamic system. Additionally, a generated pharmacophore model explored the essential pharmacophoric features of the synthesized scaffolds with respect to clinically used drug molecules for optimal hormone-dependant anti-breast cancer activity.Communicated by Ramaswamy H. Sarma.

Small Molecules Targeting the RNA-Binding Protein HuR Inhibit Tumor Growth in Xenografts

The RNA-binding protein Hu antigen R (HuR) is a post-transcriptional regulator critical in several types of diseases, including cancer, making it a promising therapeutic target. We have identified small-molecule inhibitors of HuR through a screening approach used in combination with fragment analysis. A total of 36 new compounds originating from fragment linking or structural optimization were studied to establish structure-activity relationships in the set. Two top inhibitors, 1c and 7c, were further validated by binding assays and cellular functional assays. Both block HuR function by directly binding to the RNA-binding pocket, inhibit cancer cell growth dependence of HuR, and suppress cancer cell invasion. Intraperitoneal administration of inhibitor 1c inhibits tumor growth as a single agent and shows a synergistic effect in combination with chemotherapy docetaxel in breast cancer xenograft models. Mechanistically, 1c interferes with the HuR-TGFB/THBS1 axis.

Trends and hotspots for European Journal of Medicinal Chemistry: A bibliometric study

European Journal of Medicinal Chemistry (EJMC) has been around for a long time and has gained broad interest from the various individuals working in the field. However, there is no bibliometric analysis on the publications of EJMC to thoroughly assess the scientific output and current status systematically. Therefore, the study was conducted to analyze the various publications of EJMC from 1987 to 2022 to improve their quality. A total of 13,386 papers were retrieved, with the number of publications increasing yearly. Based on the multiple indicators of bibliometrics, the highest impact countries, institutions, authors and representative literature were identified, and visualization networks were constructed using VOSviewer. Keyword co-occurrence analysis reveals a gradual shift from phenotypic drug discovery to target-based drug discovery in the EJMC theme change. Moreover, further discussion of the keyword clustering results is provided to support researchers in defining the scope of their research topics and planning their research directions. At this stage, there is a greater focus on developing antitumor and oxidative stress-related drugs than on the earlier anti-infective activities. In future studies, the main research directions are tumor multidrug resistance, oxidative stress, and dual inhibitors.

Effect of Antihypertensive Drug (Chlorothiazide) on Fibrillation of Lysozyme: A Combined Spectroscopy, Microscopy, and Computational Study

Amyloid fibrils abnormally accumulate together in the human body under certain conditions, which can result in lethal conditions. Thus, blocking this aggregation may prevent or treat this disease. Chlorothiazide (CTZ) is a diuretic and is used to treat hypertension. Several previous studies suggest that diuretics prevent amyloid-related diseases and reduce amyloid aggregation. Thus, in this study we examine the effects of CTZ on hen egg white lysozyme (HEWL) aggregation using spectroscopic, docking, and microscopic approaches. Our results showed that under protein misfolding conditions of 55 °C, pH 2.0, and 600 rpm agitation, HEWL aggregated as evidenced by the increased turbidity and Rayleigh light scattering (RLS). Furthermore, thioflavin-T, as well as trans electron microscope (TEM) analysis confirmed the formation of amyloid structures. An anti-aggregation effect of CTZ is observed on HEWL aggregations. Circular dichroism (CD), TEM, and Thioflavin-T fluorescence show that both CTZ concentrations reduce the formation of amyloid fibrils as compared to fibrillated. The turbidity, RLS, and ANS fluorescence increase with CTZ increasing. This increase is attributed to the formation of a soluble aggregation. As evidenced by CD analysis, there was no significant difference in α-helix content and β-sheet content between at 10 µM CTZ and 100 µM. A TEM analysis of HEWL coincubated with CTZ at different concentrations validated all the above-mentioned results. The TEM results show that CTZ induces morphological changes in the typical structure of amyloid fibrils. The steady-state quenching study demonstrated that CTZ and HEWL bind spontaneously via hydrophobic interactions. HEWL-CTZ also interacts dynamically with changes in the environment surrounding tryptophan. Computational results revealed the binding of CTZ to ILE⁹⁸, GLN⁵⁷, ASP⁵², TRP¹⁰⁸, TRP⁶³, TRP⁶³, ILE⁵⁸, and ALA¹⁰⁷ residues in HEWL via hydrophobic interactions and hydrogen bonds with a binding energy of -6.58 kcal mol^-1. We suggest that at 10 µM and 100 μM, CTZ binds to the aggregation-prone region (APR) of HEWL and stabilizes it, thus preventing aggregation. Based on these findings, we can conclude that CTZ has antiamyloidogenic activity and can prevent fibril aggregation.

Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences

Systematically identifying functional peptides is difficult owing to the vast combinatorial space of peptide sequences. Here we report a machine-learning pipeline that mines the hundreds of billions of sequences in the entire virtual library of peptides made of 6-9 amino acids to identify potent antimicrobial peptides. The pipeline consists of trainable machine-learning modules (for performing empirical selection, classification, ranking and regression tasks) assembled sequentially following a coarse-to-fine design principle to gradually narrow down the search space. The leading three antimicrobial hexapeptides identified by the pipeline showed strong activities against a wide range of clinical isolates of multidrug-resistant pathogens. In mice with bacterial pneumonia, aerosolized formulations of the identified peptides showed therapeutic efficacy comparable to penicillin, negligible toxicity and a low propensity to induce drug resistance. The machine-learning pipeline may accelerate the discovery of new functional peptides.

Identification of a Novel Dual Inhibitor of Acetylcholinesterase and Butyrylcholinesterase: In Vitro and In Silico Studies

The enhancement of cholinergic functions via acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibition is considered a valuable therapeutic strategy for the treatment of Alzheimer's disease. This study aimed to evaluate the in vitro effect of ZINC390718, previously filtered using computational approaches, on both cholinesterases and to characterize, using a molecular dynamics (MD) simulation, the possible binding mode of this compound inside the cholinesterase enzymes. The in vitro cytotoxicity effect was also investigated using a primary astrocyte-enriched glial cell culture. ZINC390718 presented in vitro dual inhibitory activity against AChE at a high micromolar range (IC₅₀ = 543.8 µM) and against BuChE (IC₅₀ = 241.1 µM) in a concentration-dependent manner, with greater activity against BuChE. The MD simulation revealed that ZINC390718 performed important hydrophobic and H-bond interactions with the catalytic residue sites on both targets. The residues that promoted the hydrophobic interactions and H-bonding in the AChE target were Leu67, Trp86, Phe123, Tyr124, Ser293, Phe295, and Tyr341, and on the BuChE target, they were Asp70, Tyr332, Tyr128, Ile442, Trp82, and Glu197. The cytotoxic effect of Z390718, evaluated via cell viability, showed that the molecule has low in vitro toxicity. The in vitro and in silico results indicate that ZINC390718 can be used as chemotype for the optimization and identification of new dual cholinesterase inhibitors.