Target Proteins Research Articles

The Power of Molecular Dynamics Simulations and Their Applications to Discover Cysteine Protease Inhibitors.

A large family of enzymes with the function of hydrolyzing peptide bonds, called peptidases or cysteine proteases (CPs), are divided into three categories according to the peptide chain involved. CPs catalyze the hydrolysis of amide, ester, thiol ester, and thioester peptide bonds. They can be divided into several groups, such as papain-like (CA), viral chymotrypsin-like CPs (CB), papain-like endopeptidases of RNA viruses (CC), legumain-type caspases (CD), and showing active residues of His, Glu/Asp, Gln, Cys (CE). The catalytic mechanism of CPs is the essential cysteine residue present in the active site. These mechanisms are often studied through computational methods that provide new information about the catalytic mechanism and identify inhibitors. The role of computational methods during drug design and development stages is increasing. Methods in Computer-Aided Drug Design (CADD) accelerate the discovery process, increase the chances of selecting more promising molecules for experimental studies, and can identify critical mechanisms involved in the pathophysiology and molecular pathways of action. Molecular dynamics (MD) simulations are essential in any drug discovery program due to their high capacity for simulating a physiological environment capable of unveiling significant inhibition mechanisms of new compounds against target proteins, especially CPs. Here, a brief approach will be shown on MD simulations and how the studies were applied to identify inhibitors or critical information against cysteine protease from several microorganisms, such as Trypanosoma cruzi (cruzain), Trypanosoma brucei (rhodesain), Plasmodium spp. (falcipain), and SARS-CoV-2 (Mpro). We hope the readers will gain new insights and use our study as a guide for potential compound identifications using MD simulations.

A computational discovery of hexokinase 2 inhibitors from Newbouldia laevis for Hepatocellular carcinoma (HCC) treatment

Newbouldia laevis, also known as the African Border Tree or Fever Tree, is a deciduous tree native to West Africa. The plant is valued for its medicinal properties and is used in traditional medicine for its antimicrobial and anti-inflammatory effects. N. laevis, is a storage tank of phytochemicals with huge health benefits and performances globally for the treatment and management of numerous disease conditions. Limited research exists on the usage of N. laevis for hepatocellular carcinoma (HCC) treatment. This study aims to explore the inhibitory activities of phytochemicals from N. laevis against the hexokinase 2 protein, a target in hepatocarcinoma (HCC) treatment. This study presents a unique in silico approach that includes ligand binding site prediction, molecular docking, molecular dynamics simulation, and Molecular Mechanics Poisson–Boltzmann Surface Area (MM/PBSA) methods. A total of 35 phytochemicals with available 3D structures were identified through literature mining. The literature review highlighted the significance of hexokinase 2 protein as a target inhibitor in the treatment of hepatocellular carcinoma (HCC). Molecular docking experiment with the all the identified phytochemicals and hexokinase 2 revealed that all the identified phytochemicals had potential inhibitory activities against the target protein. Moreover, chrysarobin, apigenin and ursolic acid were the best inhibitors with lowest binding energy of −8.9 kcal/mol, −8.7 kcal/mol, and −8.5 kcal/mol, respectively. The docking experiment was validated by comparing the binding affinities with known reference drug Cabozantinib-S-malate (−8.3 kcal/mol). Further, molecular dynamics studies of complexes with the best docking scores and reference drug complexes were described in detail here. The results of 100 ns modeling (RMSD, RMSF, Rg and SASA) show extraordinary stability during the establishment of complexes with apigenin and ursolic acid, as well as favorable binding energy, which was determined theoretically by means of the MM/PBSA method, thereby increase the probability of their acting as promising and likely hexokinase 2 inhibitors. Therefore, the study predicted that apigenin and ursolic acid could be used as a potential inhibitor/antagonist for the hexokinase 2 enzyme.

Discovery and validation of a novel inhibitor of HYPE-mediated AMPylation.

Adenosyl monophosphate (AMP)ylation (the covalent transfer of an AMP from Adenosine Triphosphate (ATP) onto a target protein) is catalyzed by the human enzyme Huntingtin Yeast Interacting Partner E (HYPE)/FicD to regulate its substrate, the heat shock chaperone binding immunoglobulin protein (BiP). HYPE-mediated AMPylation of BiP is critical for maintaining proteostasis in the endoplasmic reticulum and mounting a unfolded protein response in times of proteostatic imbalance. Thus, manipulating HYPE's enzymatic activity is a key therapeutic strategy towardthe treatment of various protein misfolding diseases, including neuropathy and early-onset diabetes associated with two recently identified clinical mutations of HYPE. Herein, we present an optimized, fluorescence polarization-based, high-throughput screening (HTS) assay to discover activators and inhibitors of HYPE-mediated AMPylation. After challenging our HTS assay with over 30,000 compounds, we discovered a novel AMPylase inhibitor, I2.10. We also determined a low micromolar IC50 for I2.10 and employed biorthogonal counter-screens to validate its efficacy against HYPE's AMPylation of BiP. Further, we report low cytotoxicity of I2.10 on human cell lines. We thus established an optimized, high-quality HTS assay amenable to tracking HYPE's enzymatic activity at scale, and provided the first novel small-molecule inhibitor capable of perturbing HYPE-directed AMPylation of BiP in vitro. Our HTS assay and I2.10 compound serve as a platform for further development of HYPE-specific small-molecule therapeutics.

DFT, Molecular Docking, Bioactivity and ADME Analyses of Vic-dioxim Ligand Containing Hydrazone Group and its Zn(II) Complex.

Cancer is one of the diseases affecting a large population worldwide and resulting in death. Finding new anti-cancer drugs that are target-focused and have low toxicity is of great importance. This study aimed to investigate the effects of vic-dioxime derivatives carrying hydrazone group and its Zn(II) complex on cancer using molecular docking, bioactivity and quantum chemical calculations. Molecular docking studies were performed on epidermal growth factor receptor and vascular endothelial growth factor receptor 2 target proteins. Furthermore, molecular geometry was performed, and the frontier molecular orbitals, Mulliken charges and molecular electron density distribution were evaluated using density functional theory. Also, the bioactivity parameters of the compounds were evaluated, and ADME analysis was performed using web-based tools. Higher binding affinity was observed for Zn(II) complex with target proteins vascular endothelial growth factor receptor 2 and against epidermal growth factor receptor when compared with LH2. Only the Zn(II) complex against the epidermal growth factor receptor had ligand efficiency and fit quality in the valid range. Furthermore, LH2 has the most potent electrophilic ability (acceptor) among other compounds. Moreover, both LH2 and Zn(II) complexes strongly satisfy Lipinski's rule of five. In conclusion, these novel compounds, especially Zn(II) complex, can be new candidates for anticancer drug development studies which are target-focused and have low toxicity.

Computational Study of Lactucine and its Derivatives to Investigate its Anti-cancerous Properties Targeting Apoptosis-inducing Protein

Background: Lactucine is related to the sesquiterpene lactone group of naturally occurring compounds and has a variety of pharmacological effects including anticancer properties found in Chicory, Wormwood, Laurus nobilis, Pyrethrum, Chamomile, etc. Lactucine has an anticancer effect which may induce apoptosis in cancerous cells and protect other cells from getting infected. Objective: In this study, Lactucine and its derivatives were screened, and performed their in silico docking study with the proteins involved in the apoptosis-inducing effect in human leukemia cancer. Methods: The three-dimensional structure of lactucine and its derivatives were retrieved in the SDF format. Active sites of protein structures were determined by Sitemap. LigPrep module was used for geometrical refining of chemical structures of lactucine and its derivatives. The protein preparation wizard of Maestro (Schrodinger) was used for protein preparation. From the receptor-complex structure, the cocrystallized ligands were removed from the active site position of the receptor chain. All ligands were docked using default Glide settings for a grid centered on the ligand and structure. Flexible docking was performed using the extra precision (XP) feature of Glide module. The best docking poses for the lactucine and their derivatives were selected based on their docking score. The ADMET properties of lactucine 15- oxalate have been predicted by admetSAR software. Results: Proteins and ligands three-dimensional structures were retrieved from PDB and Pubchem databases, respectively. All lactucine derivatives suitably docked on the apoptosis-inducing proteins with ample Glide scores Lactucin 15-oxalate interacts with proteins which are responsible for apoptosis with a maximum of six H-bonds. Other types of interactions are also involved, like Pi-cation, Pi-Pi stacking, salt bridges, and halogen bonds. Protein CDK-4 has shown the highest number of H-bond (LYS142 salt bridges), ALA16, VAL14, ASP99, LYS35, TYR17, and ASN145) with the Lactucin 15-oxalate. ADMET properties of lactucin 15-oxalate met with the criteria of being eligible to be a novel drug for the treatment of human leukemia cancer. The Dock score of both the Dasatinib drug and the lactucine-15-oxalate with the apoptosis-inducing protein stipulates that the selected ligand has equitable interaction with the target proteins. Conclusion: In this study, lactucine derivatives were docked with apoptosis-inducing proteins for the prediction of its anticancer effect. Lactucin15-oxalate has shown the highest binding affinity for the CDK-4 target and can be used as a lead compound for cancer treatment. Glide and Dock score for docking of lactucin 15-oxalate with CDK-4, well as the number of hydrogen bonding, is in agreement to use this ligand for study. These in silico results are valuable to proceed with the in vitro and in vivo studies related to the anti-cancer role of lactucin 15-oxalate.

Integrated computational modeling and in-silico validation of flavonoids-Alliuocide G and Alliuocide A as therapeutic agents for their multi-target potential: Combination of molecular docking, MM-GBSA, ADMET and DFT analysis

Alliuocide class of compounds belong to flavonoids and were isolated from Allium cepa. Alliuocide are the key phytoconstituents of Allium cepa having biological activities like anticancer, antidiabetic, anti-allergic, metabolic, and inflammatory disorders, antibacterial as well as effective antioxidants against chronic diseases. However, after their isolation report, there was no further work done and a gap of about 10 years. Herein, the present study was carried out to cover the gap and to identify new lead candidates in drug discovery pipeline. Alliuocide A and G were screened against various antioxidant, antidiabetic, anticancer and antibacterial target proteins. Target based screening by molecular docking against different target receptor co-crystal structure revealed Alliuocide A as potential hit, these results were further demonstrated by MM-GBSA studies. Further optimization of both compounds was done by in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) studies. Both compounds followed Lipinski's rule of five therefore safe for further in vivo studies. Finally spectral studies along with quantum descriptors were carried out by employing DFT method at 6–311 G (d,p) basis set. These quantum chemical parameters proved to be important mechanism for correlating the inhibition efficiency with molecular quatum parameters. All these studies help in designing of derivatives for better pharmacological potential.

Alternative splicing-related long noncoding RNA ANRIL facilitates hepatocellular carcinoma by targeting the miR-199a-5p/SRSF1 axis and impacting Anillin.

Hepatocellular carcinoma (HCC) is characterized by aberrant alternative splicing (AS), which plays an important part in the pathological process of this disease. However, available reports about genes and mechanisms involved in AS process are limited. Our previous research has identified ANRIL as a long noncoding RNA related to the AS process of HCC. Here, we investigated the exact effect and the mechanism of ANRIL on HCC progress.The ANRIL expression profile was validated using the real-time quantitative polymerase chain reactionassay. The western blot analysis and IHC assay were conducted on candidate targets, including SRSF1 and Anillin. The clinicopathological features of 97 patients were collected and analyzed. Loss-of and gain-of-function experiments were conducted. The dual-luciferase reporter assay was applied to verify the interaction between ANRIL, miR-199a-5p, and SRSF1.Anomalous upregulation of ANRIL in HCC was observed, correlating with worse clinicopathological features of HCC. HCC cell proliferation, mobility, tumorigenesis, and metastasis were impaired by depleting ANRIL. We found that ANRIL acts as a sponger of miRNA-199a-5p, resulting in an elevated level of its target protein SRSF1. The phenotypes induced by ANRIL/miR-199a-5p/SRSF1 alteration are associated with Anillin, a validated HCC promoter.ANRIL is an AS-related lncRNA promoting HCC progress by modulating the miR-199a-5p/SRSF1 axis. The downstream effector of this axis in the development of HCC is Anillin.

High-throughput system for the thermostability analysis of proteins.

Thermal stability of proteins is a primary metric for evaluating their physical properties. Although researchers attempted to predict it using machine learning frameworks, their performance has been dependent on the quality and quantity of published data. This is due to the technical limitation that thermodynamic characterization of protein denaturation by fluorescence or calorimetry in a high-throughput manner has been challenging. Obtaining a melting curve that derives solely from the target protein requires laborious purification, making it far from practical to prepare a hundred or more samples in a single workflow. Here, we aimed to overcome this throughput limitation by leveraging the high protein secretion efficacy of Brevibacillus and consecutive treatment with plate-scale purification methodologies. By handling the entire process of expression, purification, and analysis on a per-plate basis, we enabled the direct observation of protein denaturation in 384 samples within 4 days. To demonstrate a practical application of the system, we conducted a comprehensive analysis of 186 single mutants of a single-chain variable fragment of nivolumab, harvesting the melting temperature (Tm) ranging from -9.3 up to +10.8°C compared to the wild-type sequence. Our findings will allow for data-driven stabilization in protein design and streamlining the rational approaches.

In silico Analysis of Natural Inhibitors against HPV E6 Protein.

Drug re-purposing is one of the cost-effective methods to establish novel therapeutics against many diseases. Established natural products are collected from databases and used to potentially screen them against HPV E6 protein, a critical viral protein. This study aims to design potential small molecule inhibitors against HPV E6 protein using structure-based approaches. Ten natural anti-cancerous compounds (Apigenin, Baicalein, Baicalin, Ponicidin, Oridonin, Lovastatin, Triterpenoid, Narirutin, Rosmarinic Acid, and Xanthone) were selected by review of the literature. These compounds were screened using Lipinski Rule of Five. Out of ten compounds, seven were found to satisfy Rule of five. Docking of these seven compounds was carried out using AutoDock software and corresponding Molecular Dynamics Simulations were performed by GROMACS. Among the seven compounds docked with the E6 target protein, six compounds showed lesser binding energy than the reference compound, Luteolin. The three-dimensional structures of E6 protein and the corresponding ligand complexes were visualised and analysed using PyMOL whereas the two-dimensional images of protein-ligand interactions were obtained by LigPlot⁺ software to study the specific interactions. ADME analysis using SwissADME software revealed that all the compounds except Rosmarinic acid have good gastrointestinal absorption and solubility characteristics while Xanthone and Lovastatin showed blood brain barrier penetration properties. Considering the binding energy and ADME analysis, Apigenin and Ponicidin are found to be most suitable for de novo</i> designing of potential inhibitors against the HPV16 E6 protein. Further, synthesis and characterization of these potential HPV16 E6 inhibitors will be carried out and their functional evaluation using cell culture-based assays will be undertaken.

Harnessing Computational Modeling for Efficient Drug Design Strategies

Abstract: Computational modeling has become a crucial tool in drug design, offering efficiency and cost-effectiveness. This paper discusses the various computational modeling techniques used in drug design and their role in enabling efficient drug discovery strategies. Molecular docking predicts the binding affinity of a small molecule to a target protein, allowing the researchers to identify potential lead compounds and optimize their interactions. Molecular dynamics simulations provide insights into protein-ligand complexes, enabling the exploration of conformational changes, binding free energies, and fundamental protein-ligand interactions. Integrating computational modeling with machine learning algorithms, such as QSAR modeling and virtual screening, enables the prediction of compound properties and prioritizes potential drug candidates. High-performance computing resources and advanced algorithms are essential for accelerating drug design workflows, with parallel computing, cloud computing, and GPU acceleration reducing computational time. The paper also addresses the challenges and limitations of computational modeling in drug design, such as the accuracy of scoring functions, protein flexibility representation, and validation of predictive models. It emphasizes the need for experimental validation and iterative refinement of computational predictions to ensure the reliability and efficacy of designed drugs.

In-silico approach to characterize the structure and function of a hypothetical protein of Monkeypox virus exploring Chordopox-A20R domain-containing protein activity.

Background: Monkeypox has emerged as a noteworthy worldwide issue due to its daily escalating case count. This illness presents diverse symptoms,including skin manifestations, which have the potential to spread through contact. The transmission of this infectious agent is intricate and readily transfers between individuals.Methods: The hypothetical protein MPXV-SI-2022V502225_00135 strain of monkeypox underwent structural and functional analysis using NCBI-CD Search, Pfam, and InterProScan. Quality assessment utilized PROCHECK, QMEAN, Verify3D, and ERRAT, followed by protein-ligand docking, visualization, and a 100-nanosecond simulation on Schrodinger Maestro.Results: Different physicochemical properties were estimated, indicating a stable molecular weight (49147.14) and theoretical pI (5.62) with functional annotation tools predicting the target protein to contain the domain of Chordopox_A20R domain. In secondary structure analysis, the helix coil was found to be predominant. The three-dimensional (3D) structure of the protein was obtained using a template protein (PDB ID:6zyc.1), which became more stable after YASARA energy minimization and was validated by quality assessment tools like PROCHECK, QMEAN, Verify3D, and ERRAT. Protein-ligand docking was conducted using PyRx 9.0 software to examine the binding and interactions between a ligand and a hypothetical protein, focusing on various amino acids. The model structure, active site, and binding site were visualized using the CASTp server, FTsite, and PyMOL. A 100 nanosecond simulation was performed with ligand CID_16124688 to evaluate the efficiency of this protein.Conclusion: The analysis revealed significant binding interactions and enhanced stability, aiding in drug or vaccine design for effective antiviral treatment and patient management.

In-vitro Antioxidant, In-vitro and In-silico Ovarian Anticancer Activity (Ovarian Cancer Cells-PA1) and Phytochemical Analysis of Cissus quadrangularis L. Ethanolic Extract.

Cissus quadrangularis is a valuable natural source of traditional medicines. An in vitro investigation was performed to determine whether the ethanolic extract from the whole portions of C. quadrangularis had anticancer and free radical scavenging activities against ovarian cancer cells-PA1. C. quadrangularis is a herb collected from rural areas in Andhra Pradesh, India. C. quadrangularis was air-dried and crushed, and the powder and ethanol (0.5 kg) were used in a Soxhlet device for continuous extraction. Phytochemical analysis of the extracts was performed using a standard procedure. The antioxidant activity of the ethanolic extract of C. quadrangularis was evaluated using DPPH. An in vitro anticancer study used an ethanolic extract against the PA1 cell line. Apoptosis of ovarian cancer cells was studied using DAPI and carboxy-H2DCFDA staining. From LC-MS analysis, quercetin-3-O-alpha-Lrhamnopyranoside and erucic acid were docked with the threonine tyrosine kinase (TTK) enzyme using auto docking. The ethanolic extract of C. quadrangularis demonstrated significant dose-dependent antioxidant activity compared to ascorbic acid. The ethanolic extract of C. quadrangularis was found to have high anticancer activity against ovarian cancer cell lines (PA1), with an IC50 value of 482.057 ± 113.857 μg/ml. DAPI and carboxy-H2DCFDA staining confirmed that C. quadrangularis ethanolic extract induced apoptosis in ovarian cancer cells (p < .001). Molecular docking studies helped identify the binding affinities between the protein and ligand complexes, such as Quercetin-3-O-alpha-Lrhamnopyranoside binding sites of target proteins 5N7V (MET602, GLN672) and erucic acid 5N7V (GLY354). Quercetin-3-O-alpha-L-rhamnopyranoside was reported to bind with 5N7V by hydrogen bonding at MET602 and GLN672 amino acids with 2.02, 2.99 Å bonding length distance and binding affinity of -7.9 kcal/mol. Erucic acid was reported to bind with 5N7V by hydrogen bonding at GLY354 amino acid with 3.18, 2.93 Å bonding length (Å) distance and binding affinity of -4.3 kcal/mol. The current analysis showed that the ethanolic extracts of C. quadrangularis L. exhibited antioxidant and anticancer properties against ovarian PA1 cells. The experimental results confirmed that C. quadrangularis L. is a promising, safe chemotherapeutic plant for ovarian cancer PA1 cells. The docking results demonstrated that Quercetin-3-O-alpha-L-rhamnopyranoside strongly binds threonine tyrosine kinase at the MET602 and GLN672 positions. This study showed that the C. quadrangularis ethanolic extract has Quercetin-3-O-alpha-L-rhamnopyranoside, which can be used as an anticancer agent.

M6A modification of lncRNA PHKA1-AS1 enhances Actinin Alpha 4 stability and promotes non-small cell lung cancer metastasis.

Cancer is a disease with molecular heterogeneity that is closely related to gene mutations and epigenetic changes. The principal histological subtype of lung cancer is non-small cell lung cancer (NSCLC). Long noncoding RNA (lncRNA) is a kind of RNA that is without protein coding function, playing a critical role in the progression of cancer. In this research, the regulatory mechanisms of lncRNA phosphorylase kinase regulatory subunit alpha 1 antisense RNA 1 (PHKA1-AS1) in the progression of NSCLC were explored. The increased level of N6-methyladenosine (m6A) modification in NSCLC caused the high expression of PHKA1-AS1. Subsequently, high-expressed PHKA1-AS1 significantly facilitated the proliferation and metastasis of NSCLC cells, and these effects could be reversed upon the inhibition of PHKA1-AS1 expression, both in vivo and in vitro. Additionally, the target protein of PHKA1-AS1 was actinin alpha 4 (ACTN4), which is known as an oncogene. Herein, PHKA1-AS1 could enhance the protein stability of ACTN4 by inhibiting its ubiquitination degradation process, thus exerting the function of ACTN4 in promoting the progress of NSCLC. In conclusion, this research provided a theoretical basis for further exploring the potential mechanism of NSCLC metastasis and searching novel biomarkers related to the pathogenesis and progression of NSCLC.

Consensus holistic virtual screening for drug discovery: a novel machine learning model approach

In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula “w_new”, consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodologies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation. In conclusion, this novel workflow consistently delivered superior results, emphasizing the significance of a holistic approach in drug discovery, where both quantitative metrics and active enrichment play pivotal roles in identifying the best virtual screening methodology.Scientific contributionWe presented a novel consensus scoring workflow in virtual screening, merging diverse methods for enhanced compound selection. We also introduced ‘w_new’, a groundbreaking metric that intricately refines machine learning model rankings by weighing various model-specific parameters, revolutionizing their efficacy in drug discovery in addition to other domains.Graphical

Novel Strategy for Screening Target Proteins by the Common Drugs─Sofosbuvir-Specific Profiling of HCV Patient Serum.

It is the scientific basis of precision medicine to study all of the targets of drugs based on the interaction between drugs and proteins. It is worth paying attention to unknown proteins that interact with drugs to find new targets for the design of new drugs. Herein, we developed a protein profiling strategy based on drug-protein interactions and drug-modified magnetic nanoparticles and took hepatitis C virus (HCV) and its corresponding drug sofosbuvir (SOF) as an example. A SOF-modified magnetic separation medium (Fe3O4@POSS@SOF) was prepared, and a gradient elution strategy was employed and optimized to profile specific proteins interacted with SOF. A series of proteomic analyses were performed to profile proteins based on SOF-protein interactions (SPIs) in the serum of HCV patients to evaluate the specificity of the profiling strategy. As a result, five proteins were profiled with strong SPIs and exhibited high relevance with liver tissue, which were potentially new drug targets. Among them, HSP60 was used to confirm the highly specific interactions between the SOF and its binding proteins by Western blotting analysis. Besides, 124 and 29 differential proteins were profiled by SOF material from three HCV patient serum and pooled 20 HCV patient serum, respectively, by comparing with healthy human serum. In comparison with those profiled by the polyhedral oligomeric silsesquioxane (POSS) material, differential proteins profiled by the SOF material were highly associated with liver diseases through GO analysis and pathway analysis. Furthermore, four common differential proteins profiled by SOF material but not by POSS material were found to be identical and expressed consistently in both pooled serum samples and independent serum samples, which might potentially be biomarkers of HCV infection. Taken together, our study proposes a highly specific protein profiling strategy to display distinctive proteomic profiles, providing a novel idea for drug design and development.

Fluorescence Thermal Shift Assay based High Through-put Screening in Small Molecule Drug Discovery: A Brief Overview

Background: The Early drug discovery process was majorly phenotypic, lacking target specificity, and mechanism of action causing short and/or long-term toxicological impacts on the patients leading to several drug withdrawals from the market. Biochemical procedures and methods used in drug discovery and development make it lengthy in terms of time requiring approx. 15 years with the investment of billions of dollars for a new drug molecule. Technological advancement leading to the identification of protein structures through crystallography paved a new path toward targeted drug discovery. Objective: This paper provides a brief overview of the use of the fluorescence thermal shift assay (FTSA) as a high throughput (HTS) screening technique in small molecule drug discovery. Methods: The article discusses the principles of thermal denaturation techniques and how they are applied in drug discovery. It highlights the advantages of FTSA over other biophysical methods, including its label-free, cost-effective, and high HTS nature. The review also presents a comparative analysis of FTSA with techniques such as isothermal titration calorimetry (ITC), mass spectrometry (MS), nuclear magnetic resonance (NMR), surface plasmon resonance (SPR), X-ray crystallography(X-RD), circular dichroism (CD), and differential scanning calorimetry (DSC). Results: The FTSA technique is described as a powerful tool that addresses the limitations of larger protein requirements, specific assay development, and low HTS associated with other thermal denaturation methods. Unlike biochemical and cellular assays that measure secondary effects, FTSA directly estimates the binding of small molecules to target proteins. Conclusion: The review concludes that FTSA is the only biophysical method that satisfies the three key prerequisites of being label-free, cost-effective, and suitable for high HTS screening in small molecule drug discovery.

Preparation, Characterization, In silico and In vitro Antimicrobial Studies of Phenothiazine-3-sulphonamide Derivatives

The antibacterial activities of phenothiazine and sulphonamide derivatives have attracted so much interest. In this study, the synthesis and characterization of phenothiazine-3-sulphonamide derivatives and evaluation of their antimicrobial activity against the following pathogenic microorganisms is reported. Two Gram-positive bacteria; (Staphylococcus aureus (ATCC: 6538) and Streptococcus pyogenes (ATCC: 27853)), two Gram-negative bacteria; (Escherichia coli(ATCC: 3008), and Salmonella typhi (ATCC: 25175)) as well as one fungus (Aspergillus fumigatus (ATCC: 10231)) were used while ciprofloxacin, gentamycin and ketoconazole served as standard drugs. The synthesis of the derivatives was achieved through a base catalyzed the reaction of 4-chloroaniline with 1-naphthylamine to form 1-(naphthalen-1-yl)benzene-1,4-diamine. This intermediate was then treated with sulphur and iodine to yield the phenothiazine derivative while subsequent treatment of the phenothiazine compound with sulphonyl chlorides gave the final products. The synthesized compounds were characterized via proton nuclear magnetic resonance (1H-NMR), carbon-13 nuclear magnetic resonance (13C-NMR) and Fourier-transform infrared (FTIR) spectroscopic techniques. The minimum inhibitory concentration (MIC) of each compound was then determined using the agar well diffusion method. To predict the binding energies and patterns of the synthesized compounds with target proteins of the above-mentioned microorganisms, molecular docking simulations were run using Autodock Vina software (version 4.2). The spectra data of the compounds for FTIR, 1H-NMR and 13C-NMR spectral data were consistent with the assigned structures of the synthesized compounds. The binding energies (kcal/mol) for in silico antimicrobial studies were in the range -5.1 to -7.6 kcal/mol. The MIC values were in the range 3.5 to 1.0 mg/L. The results of the in vitro test revealed that the synthesized compounds exhibit promising antimicrobial activity and showed excellent bactericidal and fungicidal activities. The results obtained showed that the synthesized compounds possess drug-like properties and are good starting materials for drug production.

Computational Strategies in Drug Discovery: Leveraging Subtractive Genomic Analysis for Target Identification.

The utilization of In-silico subtractive genomic analysis has emerged as an important and essential method in modern drug discovery and development since it significantly improves the process of identifying and validating potential targets for discovering novel therapeutic compounds to treat severe infections caused by Antimicrobial-resistant (AMR) - pathogenic species. This review provides a complete overview of the methodology, advantages, disadvantages, and prospects, associated with subtractive genomic research in the context of drug discovery and development. The initial phase of analysis encompasses the retrieval of data, which serves as a foundation for the subsequent data mining process in Phase 1. After data mining, Phase 2 utilizes subtractive channels for the target's non-homology and essentiality analysis. Phase 3 of the study aims to provide a comprehensive understanding of prospective targets by their qualitative characterization. Further, Phase 4 of the study emphasizes on conducting structure-based analyses, which involves the determination, refinement, evaluation, and validation of three-dimensional structures of the target proteins, along with their active site prediction and selection of the novel therapeutic compounds against that active site on the obtained targets through virtual screening and docking studies by utilizing various databases and servers. The therapeutic compounds obtained can be then validated by in vitro and in vivo testing, thereby establishing a connection between the computational predictions and real applications.

Structure prediction of protein-ligand complexes from sequence information with Umol

Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly from sequence information. We find that classical docking methods are still superior, but depend upon having crystal structures of the target protein. In addition to predicting flexible all-atom structures, predicted confidence metrics (plDDT) can be used to select accurate predictions as well as to distinguish between strong and weak binders. The advances presented here suggest that the goal of AI-based drug discovery is one step closer, but there is still a way to go to grasp the complexity of protein-ligand interactions fully. Umol is available at: https://github.com/patrickbryant1/Umol.

Characterization and Efficacy of C60 Nano-Photosensitive Drugs in Colorectal Cancer Treatment

BackgroundFullerenes C60 shows great potential for drug transport. C60 generates large amounts of singlet oxygen upon photoexcitation, which has a significant inhibitory effect on tumor cells, so the photosensitive properties of C60 were exploited for photodynamic therapy of tumors by laser irradiation. MethodsIn this study, C60-NH2 was functionalized by introducing amino acids on the surface of C60, coupled with 5-FU to obtain C60 amino acid-derived drugs (C60AF, C60GF, C60LF), and activated photosensitive drugs (C60AFL, C60GFL, C60LFL) were obtained by laser irradiation. The C60 nano-photosensitive drugs were characterized in various ways, and the efficacy and safety of C60 nano-photosensitive drugs were verified by cellular experiments and animal experiments. Bioinformatics methods and cellular experiments were used to confirm the photosensitive drug targets and verify the therapeutic targets with C60AF. ResultsPhotosensitised tumor-targeted drug delivery effectively crosses cell membranes, leads to more apoptotic cell death, and provides higher anti-tumor efficacy and safety in vitro and in vivo colorectal cancer pharmacodynamic assays compared to free 5-FU.C60 photosensitized drug promotes tumor killing by inhibiting the colorectal cancer FLOR1 tumor protein target, with no significant toxic effects on normal organs. ConclusionC60 photosensitized drug delivery systems are expected to improve efficacy and reduce side effects in the future treatment of colorectal cancer. Further and better development and design of drugs and vectors for colorectal cancer therapy.