Antidepressant drugs act by directly binding to TRKB neurotrophin receptors.

It is unclear how binding of antidepressant drugs to their targets gives rise to the clinical antidepressant effect. We discovered that the transmembrane domain of tyrosine kinase receptor 2 (TRKB), the brain-derived neurotrophic factor (BDNF) receptor that promotes neuronal plasticity and antidepressant responses, has a cholesterol-sensing function that mediates synaptic effects of cholesterol. We then found that both typical and fast-acting antidepressants directly bind to TRKB, thereby facilitating synaptic localization of TRKB and its activation by BDNF. Extensive computational approaches including atomistic molecular dynamics simulations revealed a binding site at the transmembrane region of TRKB dimers. Mutation of the TRKB antidepressant-binding motif impaired cellular, behavioral, and plasticity-promoting responses to antidepressants in vitro and in vivo. We suggest that binding to TRKB and allosteric facilitation of BDNF signaling is the common mechanism for antidepressant action, which may explain why typical antidepressants act slowly and how molecular effects of antidepressants are translated into clinical mood recovery.

Phanto-IDP: compact model for precise intrinsically disordered protein backbone generation and enhanced sampling.

The biological function of proteins is determined not only by their static structures but also by the dynamic properties of their conformational ensembles. Numerous high-accuracy static structure prediction tools have been recently developed based on deep learning; however, there remains a lack of efficient and accurate methods for exploring protein dynamic conformations. Traditionally, studies concerning protein dynamics have relied on molecular dynamics (MD) simulations, which incur significant computational costs for all-atom precision and struggle to adequately sample conformational spaces with high energy barriers. To overcome these limitations, various enhanced sampling techniques have been developed to accelerate sampling in MD. Traditional enhanced sampling approaches like replica exchange molecular dynamics (REMD) and frontier expansion sampling (FEXS) often follow the MD simulation approach and still cost a lot of computational resources and time. Variational autoencoders (VAEs), as a classic deep generative model, are not restricted by potential energy landscapes and can explore conformational spaces more efficiently than traditional methods. However, VAEs often face challenges in generating reasonable conformations for complex proteins, especially intrinsically disordered proteins (IDPs), which limits their application as an enhanced sampling method. In this study, we presented a novel deep learning model (named Phanto-IDP) that utilizes a graph-based encoder to extract protein features and a transformer-based decoder combined with variational sampling to generate highly accurate protein backbones. Ten IDPs and four structured proteins were used to evaluate the sampling ability of Phanto-IDP. The results demonstrate that Phanto-IDP has high fidelity and diversity in the generated conformation ensembles, making it a suitable tool for enhancing the efficiency of MD simulation, generating broader protein conformational space and a continuous protein transition path.

Tunable Surface Wettability via Terahertz Electrowave Controlled Vicinal Subnanoscale Water Layer.

Achieving timely, reversible, and long-range remote tunability over surface wettability is highly demanded across diverse fields, including nanofluidic systems, drug delivery, and heterogeneous catalysis. Herein, using molecular dynamic simulations, we show, for the first time, a theoretical design of electrowetting to achieve remotely controllable surface wettability via using a terahertz wave. The key idea driving the design is the unique terahertz collective vibration identified in the vicinal subnanoscale water layer, which is absent in bulk water, enabling efficient energy transfer from the terahertz wave to the rotational motion of the vicinal subnanoscale water layer. Consequently, a frequency-specific alternating terahertz electric field near the critical strength can significantly affect the local hydrogen-bonding network of the contact water layer on the solid surface, thereby achieving tunable surface wettability.

Orientation-Guided Immobilization of Probe DNA on swCNT-FET for Enhancing Sensitivity of EcoRV Detection.

We present a novel approach that integrates electrical measurements with molecular dynamics (MD) simulations to assess the activity of type-II restriction endonucleases, specifically EcoRV. Our approach employs a single-walled carbon nanotube field-effect transistor (swCNT-FET) functionalized with the EcoRV substrate DNA, enabling the detection of enzymatic cleavage events. Notably, we leveraged the methylene blue (MB) tag as an "orientation guide" to immobilize the EcoRV substrate DNA in a specific direction, thereby enhancing the proximity of the DNA cleavage reaction to the swCNT surface and consequently improving the sensitivity in EcoRV detection. We conducted computational modeling to compare the conformations and electrostatic potential (ESP) of MB-tagged DNA with its MB-free counterpart, providing strong support for our electrical measurements. Both conformational and ESP simulations exhibited robust agreement with our experimental data. The inhibitory efficacy of the EcoRV inhibitor aurintricarboxylic acid (ATA) was also evaluated, and the selectivity of the sensing device was examined.

Identification of Helicobacter pylori-carcinogenic TNF-alpha-inducing protein inhibitors via daidzein derivatives through computational approaches.

Gastric cancer is considered a class 1 carcinogen that is closely linked to infection with Helicobacter pylori (H. pylori), which affects over 1 million people each year. However, the major challenge to fight against H. pylori and its associated gastric cancer due to drug resistance. This research gap had led our research team to investigate a potential drug candidate targeting the Helicobacter pylori-carcinogenic TNF-alpha-inducing protein. In this study, a total of 45 daidzein derivatives were investigated and the best 10 molecules were comprehensively investigated using in silico approaches for drug development, namely pass prediction, quantum calculations, molecular docking, molecular dynamics simulations, Lipinski rule evaluation, and prediction of pharmacokinetics. The molecular docking study was performed to evaluate the binding affinity between the target protein and the ligands. In addition, the stability of ligand-protein complexes was investigated by molecular dynamics simulations. Various parameters were analysed, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM). The results has confirmed that the ligand-protein complex CID: 129661094 (07) and 129664277 (08) formed stable interactions with the target protein. It was also found that CID: 129661094 (07) has greater hydrogen bond occupancy and stability, while the ligand-protein complex CID 129664277 (08) has greater conformational flexibility. Principal component analysis revealed that the ligand-protein complex CID: 129661094 (07) is more compact and stable. Hydrogen bond analysis revealed favourable interactions with the reported amino acid residues. Overall, this study suggests that daidzein derivatives in particular show promise as potential inhibitors of H. pylori.

From Sheep Track to Motorway: Supramolecular-Mediated 2D Nanofluidic Channels for Ultrafast Water Transport.

Atomic thick 2D materials hold great potential as building blocks to construct highly permeable membranes, yet the permeability of laminar 2D material membranes is still limited by their irregularity sheep track-like interlayer channels. Herein, a supramolecular-mediated strategy to induce the regular assembly of high-throughput 2D nanofluidic channels based on host-guest interactions is proposed. Inspired by the characteristics of motorways, supramolecular-mediated ultrathin 2D membranes with broad and continuous regular water transport channels are successfully constructed using graphene oxide (GO) as an example. The prepared membrane achieves an ultrahigh water permeability (369.94 LMH bar-1) more than six times higher than that of the original membranes while maintaining dye rejection above 98.5%, which outperforms the reported 2D membranes. Characterization and simulation results show that the introduction of hyaluronate-grafted ß-cyclodextrin not only expands the interlayer channels of GO membranes but also enables the membranes to operate stably under harsh conditions with the help of host-guest interactions. This universal supramolecular assembly strategy provides new opportunities for the preparation of 2D membranes with high separation performance and reliable and stable nanofluidic channels.

A novel multi-epitope peptide vaccine candidate targeting hepatitis E virus: An in silico approach.

Hepatitis E virus (HEV) is a foodborne virus transmitted through the faecal-oral route that causes viral hepatitis in humans worldwide. Ever since its discovery as a zoonotic agent, HEV was isolated from several species with an expanding range of hosts. HEV possesses several features of other RNA viruses but also has certain HEV-specific traits that make its viral-host interactions inimitable. HEV leads to severe morbidity and mortality in immunocompromised people and pregnant women across the world. The situation in underdeveloped countries is even more alarming. Even after creating a menace across the world, we still lack an effective vaccine against HEV. Till date, there is only one licensed vaccine for HEV available only in China. The development of an anti-HEV vaccine that can reduce HEV-induced morbidity and mortality is required. Live attenuated and killed vaccines against HEV are not accessible due to the lack of a tolerant cell culture system, slow viral replication kinetics and varying growth conditions. Thus, the main focus for anti-HEV vaccine development is now on the molecular approaches. In the current study, we have designed a multi-epitope vaccine against HEV through a reverse vaccinology approach. For the first time, we have used viral ORF3, capsid protein and polyprotein altogether for epitope prediction. These are crucial for viral replication and persistence and are major vaccine targets against HEV. The proposed in silico vaccine construct comprises of highly immunogenic and antigenic T-cell and B-cell epitopes of HEV proteins. The construct is capable of inducing an effective and long-lasting host immune response as evident from the simulation results. In addition, the construct is stable, non-allergic and antigenic for the host. Altogether, our findings suggest that the in silico vaccine construct may be useful as a vaccine candidate for preventing HEV infections.

Research into how carvacrol and metformin affect several human proteins in a hyperglycemic condition: A comparative study in silico and in vitro.

Carvacrol (CV) is an organic compound found in the essential oils of many aromatic herbs. It is nearly unfeasible to analyze all the current human proteins for a query ligand using in vitro and in vivo methods. This study aimed to clarify whether CV possesses an anti-diabetic feature via Docking-based inverse docking and molecular dynamic (MD) simulation and in vitro characterization against a set of novel human protein targets. Herein, the best poses of CV docking simulations according to binding energy ranged from -7.9 to -3.5 (kcal/mol). After pathway analysis of the protein list through GeneMANIA and WebGestalt, eight interacting proteins (DPP4, FBP1, GCK, HSD11ß1, INSR, PYGL, PPARA, and PPARG) with CV were determined, and these proteins exhibited stable structures during the MD process with CV. In vitro application, statistically significant results were achieved only in combined doses with CV or metformin. Considering all these findings, PPARG and INSR, among these target proteins of CV, are FDA-approved targets for treating diabetes. Therefore, CV may be on its way to becoming a promising therapeutic compound for treating Diabetes Mellitus (DM). Our outcomes expose formerly unexplored potential target human proteins, whose association with diabetic disorders might guide new potential treatments for DM.

Design of multi-epitope vaccine against porcine rotavirus using computational biology and molecular dynamics simulation approaches.

Porcine Rotavirus (PoRV) is a significant pathogen affecting swine-rearing regions globally, presenting a substantial threat to the economic development of the livestock sector. At present, no specific pharmaceuticals are available for this disease, and treatment options remain exceedingly limited. This study seeks to design a multi-epitope peptide vaccine for PoRV employing bioinformatics approaches to robustly activate T-cell and B-cell immune responses. Two antigenic proteins, VP7 and VP8*, were selected from PoRV, and potential immunogenic T-cell and B-cell epitopes were predicted using immunoinformatic tools. These epitopes were further screened according to non-toxicity, antigenicity, non-allergenicity, and immunogenicity criteria. The selected epitopes were linked with linkers to form a novel multi-epitope vaccine construct, with the PADRE sequence (AKFVAAWTLKAAA) and RS09 peptide attached at the N-terminus of the designed peptide chain to enhance the vaccine's antigenicity. Protein-protein docking of the vaccine constructs with toll-like receptors (TLR3 and TLR4) was conducted using computational methods, with the lowest energy docking results selected as the optimal predictive model. Subsequently, molecular dynamics (MD) simulation methods were employed to assess the stability of the protein vaccine constructs and TLR3 and TLR4 receptors. The results indicated that the vaccine-TLR3 and vaccine-TLR4 docking models remained stable throughout the simulation period. Additionally, the C-IMMSIM tool was utilized to determine the immunogenic triggering capability of the vaccine protein, demonstrating that the constructed vaccine protein could induce both cell-mediated and humoral immune responses, thereby playing a role in eliciting host immune responses. In conclusion, this study successfully constructed a multi-epitope vaccine against PoRV and validated the stability and efficacy of the vaccine through computational analysis. However, as the study is purely computational, experimental evaluation is required to validate the safety and immunogenicity of the newly constructed vaccine protein.

Impact of subtype C-specific amino acid variants on HIV-1 Tat-TAR interaction: insights from molecular modelling and dynamics.

BACKGROUND: HIV-1 produces Tat, a crucial protein for transcription, viral replication, and CNS neurotoxicity. Tat interacts with TAR, enhancing HIV reverse transcription. Subtype C Tat variants (C31S, R57S, Q63E) are associated with reduced transactivation and neurovirulence compared to subtype B. However, their precise impact on Tat-TAR binding is unclear. This study investigates how these substitutions affect Tat-TAR interaction. METHODS: We utilized molecular modelling techniques, including MODELLER, to produce precise three-dimensional structures of HIV-1 Tat protein variants. We utilized Tat subtype B as the reference or wild type, and generated Tat variants to mirror those amino acid variants found in Tat subtype C. Subtype C-specific amino acid substitutions were selected based on their role in the neuropathogenesis of HIV-1. Subsequently, we conducted molecular docking of each Tat protein variant to TAR using HDOCK, followed by molecular dynamic simulations. RESULTS: Molecular docking results indicated that Tat subtype B (TatWt) showed the highest affinity for the TAR element (-262.07), followed by TatC31S (-261.61), TatQ63E (-256.43), TatC31S/R57S/Q63E (-238.92), and TatR57S (-222.24). However, binding free energy analysis showed higher affinities for single variants TatQ63E (-349.2 ± 10.4 kcal/mol) and TatR57S (-290.0 ± 9.6 kcal/mol) compared to TatWt (-247.9 ± 27.7 kcal/mol), while TatC31S and TatC31S/R57SQ/63E showed lower values. Interactions over the protein trajectory were also higher for TatQ63E and TatR57S compared to TatWt, TatC31S, and TatC31S/R57SQ/63E, suggesting that modifying amino acids within the Arginine/Glutamine-rich region notably affects TAR interaction. Single amino acid mutations TatR57S and TatQ63E had a significant impact, while TatC31S had minimal effect. Introducing single amino acid variants from TatWt to a more representative Tat subtype C (TatC31S/R57SQ/63E) resulted in lower predicted binding affinity, consistent with previous findings. CONCLUSIONS: These identified amino acid positions likely contribute significantly to Tat-TAR interaction and the differential pathogenesis and neuropathogenesis observed between subtype B and subtype C. Additional experimental investigations should prioritize exploring the influence of these amino acid signatures on TAR binding to gain a comprehensive understanding of their impact on viral transactivation, potentially identifying them as therapeutic targets.

Novel hydrazones derived from anthranilic acid as potent cholinesterases and α-glycosidase inhibitors: Synthesis, characterization, and biological effects.

N-substitued anthranilic acid derivatives are commonly found in the structure of many biologically active molecules. In this study, new members of hydrazones derived from anthranilic acid (1-15) were synthesized and investigated their effect on some metabolic enzymes such as acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and α-glycosidase (α-Gly). Results indicated that all the molecules exhibited potent inhibitory effects against all targets as compared to the standard inhibitors, revealed by IC50 values. Ki values of compounds for AChE, BChE, and α-Gly enzymes were obtained in the ranges 66.36 ± 8.30-153.82 ± 13.41, 52.68 ± 6.38-113.86, and 2.13 ± 0.25-2.84 nM, respectively. The molecular docking study was performed for the most active compounds to the determination of ligand-enzyme interactions. Binding affinities of the most active compound were found at the range of -9.70 to -9.00 kcal/mol for AChE, -11.60 to -10.60 kcal/mol for BChE, and -10.30 to -9.30 kcal/mol for α-Gly. Molecular docking simulations showed that the novel compounds had preferential interaction with AChE, BChE, and α-Gly. Drug-likeness properties and ADMET (absorption, distribution, metabolism, excretion, and toxicity) analyzes of all synthesized compounds (1-15) were estimated and their toxic properties were evaluated as well as their therapeutic properties. Moreover, molecular dynamics simulations were carried out to understand the accuracy of the most potent derivatives of docking studies.

Alpha-glucosidase inhibitory and hypoglycemic effects of imidazole-bearing thioquinoline derivatives with different substituents: In silico, in vitro, and in vivo evaluations.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by high blood sugar levels. It was shown that modulating the activity of α-glucosidase, an enzyme involved in carbohydrate digestion and absorption, can improve blood sugar control and overall metabolic health in individuals with T2DM. As a result, in the current study, a series of imidazole bearing different substituted thioquinolines were designed and synthesized as α-glucosidase inhibitors. All derivatives exhibited significantly better potency (IC50 = 12.1 ± 0.2 to 102.1 ± 4.9 µM) compared to the standard drug acarbose (IC50 = 750.0 ± 5.0 µM). 8g as the most potent analog, indicating a competitive inhibition with Ki = 9.66 µM. Also, the most potent derivative was subjected to molecular docking and molecular dynamic simulation against α-glucosidase to determine its mode of action in the enzyme and study the complex's behavior over time. In vivo studies showed that 8g did not cause acute toxicity at 2000 mg/kg doses. Additionally, in a diabetic rat model, treatment with 8g significantly reduced fasting blood glucose levels and decreased blood glucose levels following sucrose loading compared to acarbose, a standard drug used for blood sugar control. The findings suggest that the synthesized compound 8g holds promise as an α-glucosidase inhibitor for improving blood sugar control and metabolic health.

Discovery of putative inhibitors of human Pkd1 enzyme: Molecular docking, dynamics and simulation, QSAR, and MM/GBSA.

Polycystic kidney disease is the most prevalent hereditary kidney disease globally and is mainly linked to the overexpression of a gene called PKD1. To date, there is no effective treatment available for polycystic kidney disease, and the practicing treatments only provide symptomatic relief. Discovery of the compounds targeting the PKD1 gene by inhibiting its expression under the disease condition could be crucial for effective drug development. In this study, a molecular docking and molecular dynamic simulation, QSAR, and MM/GBSA-based approaches were used to determine the putative inhibitors of the Pkd1 enzyme from a library of 1379 compounds. Initially, fourteen compounds were selected based on their binding affinities with the Pkd1 enzyme using MOE and AutoDock tools. The selected drugs were further investigated to explore their properties as drug candidates and the stability of their complex formation with the Pkd1 enzyme. Based on the physicochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, and toxicity profiling, two compounds including olsalazine and diosmetin were selected for the downstream analysis as they demonstrated the best drug-likeness properties and highest binding affinity with Pkd1 in the docking experiment. Molecular dynamic simulation using Gromacs further confirmed the stability of olsalazine and diosmetin complexes with Pkd1 and establishing interaction through strong bonding with specific residues of protein. High biological activity and binding free energies of two complexes calculated using 3D QSAR and Schrodinger module, respectively further validated our results. Therefore, the molecular docking and dynamics simulation-based in-silico approach used in this study revealed olsalazine and diosmetin as potential drug candidates to combat polycystic kidney disease by targeting Pkd1 enzyme.

CB-0821, a novel CC chemokine receptor 5 (CCR5) inhibitor with improved binding efficacy proposed as anti-HIV candidate: Computational and in vitro approach.

The CC chemokine receptor 5 (CCR5) serves a pivotal role in human immunodeficiency virus 1 (HIV-1) infection by acting as a co-receptor and facilitating the binding of the viral envelope glycoprotein (env). Maraviroc (MVC), a Food and Drug Administration-approved monocarboxylic acid amide, is one of the CCR5 inhibitors employed in HIV treatment. Despite the existence of approved drugs, the emergence of drug resistance underscores the necessity for novel compounds to combat resistance and enhance therapeutic efficacy. In this study, CB-0821, identified from the ChemBridge library, emerged as a promising CCR5 inhibitor. Molecular dynamics simulations indicate comparable dynamic properties for CB-0821 and MVC. In silico comparisons with other CCR5 inhibitors emphasize CB-0821's superior binding affinity, positioning it as a potential lead compound. Evaluations of the dissociation constant (Ki) and absorption, distribution, metabolism, and excretion predictions suggest CB-0821 as a well-tolerated drug. Furthermore, the dose-dependent inhibition of CCR5 by CB-0821 in Peripheral blood mononuclear cells (PBMCs) (ranging from 10 to 200 nM) demonstrates efficacy, coupled with nontoxicity to Vero cells at concentrations up to 500 nM. These results underscore the potential of CB-0821 in HIV antiviral therapy, calling for additional preclinical validations before advancing to clinical considerations.

FDI-6 inhibits VEGF-B expression in metastatic breast cancer: a combined in vitro and in silico study.

Angiogenesis is the process by which new blood vessels are formed to meet the oxygen and nutrient needs of tissues. This process is vitally important in many physiological and pathological conditions such as tumor growth, metastasis, and chronic inflammation. Although the relationship of FDI-6 compound with FOXM1 protein is well known in the literature, its relationship with angiogenesis is not adequately elucidated. This study investigates the relationship of FDI-6 with angiogenesis and vascular endothelial growth factor B (VEGF-B) protein expression alterations. Furthermore, the study aims to elucidate the in silico interaction of FDI-6 with the VEGFR1 protein, a key player in initiating the angiogenic process, which is activated through its binding with VEGF-B. Our results demonstrate a significant effect of FDI-6 on cell viability. Specifically, we determined that the IC50 value of FDI-6 in HUVEC cells after 24 h of treatment is 24.2 µM, and in MDA-MB-231 cells after 24 h of application, it is 10.8 µM. These findings suggest that the cytotoxic effect of FDI-6 varies depending on the cell type. In wound healing experiments, FDI-6 significantly suppressed wound closure in MDA-MB-231 cells but did not show a similar effect in HUVEC cells. This finding suggests FDI-6 may have potential cell-type-specific effects. Molecular docking studies reveal that FDI-6 exhibits a stronger interaction with the VEGFR1 protein compared to its inhibitor, a novel interaction not previously reported in the literature. Molecular dynamic simulation results demonstrate a stable interaction between FDI-6 and VEGFR1. This interaction suggests that FDI-6 might modulate mechanisms associated with angiogenesis. Our Western blot analysis results show regulatory effects of FDI-6 on the expression of the VEGF-B protein. We encourage exploration of FDI-6 as a potential therapeutic agent in pathological processes related to angiogenesis. In conclusion, this study provides a detailed examination of the relationship between FDI-6 and both the molecular interactions and protein expressions of VEGF-B. Our findings support FDI-6 as a potential therapeutic agent in pathological processes associated with angiogenesis.

Comparative assessment of different anti-CD147/Basigin 2 antibodies as a potential therapeutic anticancer target by molecular modeling and dynamic simulation.

Cluster of differentiation 147 (CD147) is an attractive target for anticancer therapy since it is pivotal in developing and progressing several of malignant tumors in the context of its high expression levels. Although anti-CD147 antibodies by different laboratories are designed for the Ig-like domains of CD147, there is a demand to provide priority among these anti-CD147 antibodies for developing of therapeutic anti-CD147 antibody before experimental validations. This study uses molecular docking and dynamic simulation techniques to compare the binding modes and affinities of nine antibody models against the Ig-like domains of CD147. After obtaining the model antibodies by homology modeling via Robetta, we predicted the CDRs of nine antibodies and the epitopes of CD147 to reach more accurate results for antigen affinity in molecular docking. Next, from HADDOCK 2.4., we meticulously handpicked the most superior model clusters (Z-Score: - 2.5 to - 1.2) and identified that meplazumab had higher affinities according to the success rate as the percentage of a scoring scale. We achieved stable simulations of CD147-antibody interaction. Our outcomes hold hypothetical importance for further experimental cancer research on the design and development of the relevant model antibodies.

Computational study on the binding mechanism of allosteric drug TNO155 inhibiting SHP2E76A.

E76A mutations of SHP2 have been reported to associate with genetic developmental diseases and cancers, and TNO155 is one of the effective inhibitors targeted to the allosteric site 1, which has already entered the clinical stage. However, the detailed binding mechanism between them still needs further clarification at micro-atomic level. In this study, the binding mechanism of TNO155 inhibiting SHP2E76A and the superiorities of TNO155 at binding affinity and dynamic interactive behavior with SHP2E76A were probed utilizing a series of computational drug design technologies. The results show that SHP2E76A forms tighter interaction with TNO155 compared to SHP099. SHP2E76A-TNO155 exhibits the largest electrostatic interaction among all complex systems, which can be manifested by the strong hydrogen bond interactions formed by two electrically charged residues, Arg111 and Glu250. Notably, in SHP2E76A-TNO155 system, Asp489 makes an additional substantial beneficial contribution. The E76A mutation brings stronger residue positive correlation and a larger conformation fluctuation between N-CH2 and PTP domains, resulting in tighter binding between TNO155 and SHP2E76A. This study offers valuable insights for the further design and development of novel SHP2E76A allosteric inhibitors.

An in silico approach to identify novel and potential Akt1 (protein kinase B-alpha) inhibitors as anticancer drugs.

Akt1 (protein kinase B) has become a major focus of attention due to its significant functionality in a variety of cellular processes and the inhibition of Akt1 could lead to a decrease in tumour growth effectively in cancer cells. In the present work, we discovered a set of novel Akt1 inhibitors by using multiple computational techniques, i.e. pharmacophore-based virtual screening, molecular docking, binding free energy calculations, and ADME properties. A five-point pharmacophore hypothesis was implemented and validated with AADRR38. The obtained R2 and Q2 values are in the acceptable region with the values of 0.90 and 0.64, respectively. The generated pharmacophore model was employed for virtual screening to find out the potential Akt1 inhibitors. Further, the selected hits were subjected to molecular docking, binding free energy analysis, and refined using ADME properties. Also, we designed a series of 6-methoxybenzo[b]oxazole analogues by comprising the structural characteristics of the hits acquired from the database. Molecules D1-D10 were found to have strong binding interactions and higher binding free energy values. In addition, Molecular dynamic simulation was performed to understand the conformational changes of protein-ligand complex.

In silico exploration of anti-prostate cancer compounds from differential expressed genes.

Prostate cancer (PCa) is a complex and biologically diverse disease with no curative treatment options at present. This study aims to utilize computational methods to explore potential anti-PCa compounds based on differentially expressed genes (DEGs), with the goal of identifying novel therapeutic indications or repurposing existing drugs. The methods employed in this study include DEGs-to-drug prediction, pharmacokinetics prediction, target prediction, network analysis, and molecular docking. The findings revealed a total of 79 upregulated DEGs and 110 downregulated DEGs in PCa, which were used to identify drug compounds capable of reversing the dysregulated conditions (dexverapamil, emetine, parthenolide, dobutamine, terfenadine, pimozide, mefloquine, ellipticine, and trifluoperazine) at a threshold probability of 20% on several molecular targets, such as serotonin receptors 2a/2b/2c, HERG protein, adrenergic receptors alpha-1a/2a, dopamine D3 receptor, inducible nitric oxide synthase (iNOS), epidermal growth factor receptor erbB1 (EGFR), tyrosine-protein kinases, and C-C chemokine receptor type 5 (CCR5). Molecular docking analysis revealed that terfenadine binding to inducible nitric oxide synthase (-7.833 kcal.mol-1) and pimozide binding to HERG (-7.636 kcal.mol-1). Overall, binding energy ΔGbind (Total) at 0 ns was lower than that of 100 ns for both the Terfenadine-iNOS complex (-101.707 to -103.302 kcal.mol-1) and Ellipticine-TOPIIα complex (-42.229 to -58.780 kcal.mol-1). In conclusion, this study provides insight on molecular targets that could possibly contribute to the molecular mechanisms underlying PCa. Further preclinical and clinical studies are required to validate the therapeutic effectiveness of these identified drugs in PCa disease.

Development of novel ALOX15 inhibitors combining dual machine learning filtering and fragment substitution optimisation approaches, molecular docking and dynamic simulation methods.

The oxidation of unsaturated lipids, facilitated by the enzyme Arachidonic acid 15-lipoxygenase (ALOX15), is an essential element in the development of ferroptosis. This study combined a dual-score exclusion strategy with high-throughput virtual screening, naive Bayesian and recursive partitioning machine learning models, the already established ALOX15 inhibitor i472, and a docking-based fragment substitution optimisation approach to identify potential ALOX15 inhibitors, ultimately leading to the discovery of three FDA-approved drugs that demonstrate optimal inhibitory potential against ALOX15. Through fragment substitution-based optimisation, seven new inhibitor structures have been developed. To evaluate their practicality, ADMET predictions and molecular dynamics simulations were performed. In conclusion, the compounds found in this study provide a novel approach to combat conditions related to ferroptosis-related injury by inhibiting ALOX15.