Protein-ligand Interactions Research Articles

Expanding the tool box for native structural biology: 19F dynamic nuclear polarization with fast magic angle spinning.

Obtaining atomic-level information on components in the cell is a major focus in structural biology. Elucidating specific structural and dynamic features of proteins and their interactions in the cellular context is crucial for understanding cellular processes. We introduce 19F dynamic nuclear polarization (DNP) combined with fast magic-angle-spinning (MAS) nuclear magnetic resonance (NMR) spectroscopy as a powerful technique to study proteins in mammalian cells. We demonstrate our approach on the severe acute respiratory syndrome coronavirus 2 5F-Trp-NNTD protein, electroporated into human cells. DNP signal enhancements of 30- to 40-fold were observed, translating into over 1000-fold experimental time savings. High signal-to-noise ratio spectra were acquired on nanomole quantities of a protein in cells in minutes. 2D 19F-19F dipolar correlation spectra with remarkable sensitivity and resolution were obtained, exhibiting 19F-19F cross peaks associated with fluorine atoms as far as ~10 angstroms apart. This work paves the way for 19F DNP-enhanced MAS NMR applications in cells for probing protein structure, dynamics, and ligand interactions.

Fluorescence labeling-based differential scanning fluorimetry, an effective method for protein thermal stability and protein-compound binding analysis

Differential scanning fluorimetry (DSF) is widely used to assess protein thermal stability and protein-ligand interaction. However, its utility is often limited by the presence of detergents, which can affect hydrophobic binding. To tackle this issue, we developed an effective fluorescence-labeled DSF (FL-DSF) technique that tracks protein denaturation by monitoring the labeling fluorescence decrease, thus overcoming challenges typically encountered with traditional DSF methods. In this research, FL-DSF was first validated using Peroxisome Proliferators-Activated Receptor γ (PPARγ), Retinoid X Receptor α (RXRα), and Lysozyme, confirming its accuracy in determining melting curves. Expectedly, FL-DSF also exhibited strong compatibility with detergents in our investigations. Besides this, a new calculation method was proposed to characterize the protein denaturation process and evaluate protein-ligand binding. This mathematical model goes beyond traditional approaches, which simply treated the melting temperature (TM) shift as a concentration-dependent variable. Instead, it comprehensively incorporates the influence of irreversible denaturation-induced native protein loss on the equilibrium of protein-ligand binding. This methodology was successfully applied into the evaluation of binding affinity for 2 classical binding systems of PPARγ-Rosiglitazone and RXRα-CD3254. It was also utilized for the following binding screening studies, leading to the discovery of promising ligands for PPARγ, RXRα, and Lysozyme.

Computational Insights into Kaempferol and Luteolin: Potential Mitigators of the Omicron B.1.1.529 SARS-CoV-2 Variant

Abstract The coronavirus disease 2019 (COVID-19) has been circulating in the local and global population. Among the SARS-CoV-2 variants, the Omicron strain tends to be detected in the recent genomic surveillance studies. Despite being a manageable disease, recovered patients may experience post-COVID-19 conditions such as cough (50.6%), fatigue (45.8%), and memory loss (37.4%). While developing an updated vaccine for COVID-19 is time-consuming, the in-silico discovery of phytoconstituents that bind to the SARS-CoV-2 receptor binding domain (RBD), may provide insights on the potential of edible plants as complementary treatments. This study aimed to investigate and compare the ligand-protein interactions of phytoconstituents and COVID-19 treatment drug, remdesivir, to the SARS-CoV-2 spike protein. The molecular docking results indicated that kaempferol and luteolin could potentially mitigate COVID-19, as they exhibited higher RBD-binding affinities than remdesivir.

Exploring the cyclization of thiosemicarbazone to 1,3,4-thiadiazole: Synthesis, characterization and in-silico study

Breast cancer remains a significant health concern worldwide, necessitating the development of novel therapeutic strategies. In this study, we focus on the design, synthesis, and evaluation of isophorone-1,3,4-thiadiazoles as potential inhibitors targeting AKT1 and STAT3, crucial players in breast cancer progression. The synthesized compounds were rigorously characterized using High-Resolution Mass Spectrometry (HRMS) and Nuclear Magnetic Resonance (NMR) spectroscopy, encompassing both 1H and 13C spectra. Subsequently, Network pharmacology analysis facilitated for the identification of key genes and pathways associated with breast cancer, thereby identifying AKT1 and STAT3 as promising therapeutic targets for the designed compounds. Furthermore, the top two targets, AKT1 and STAT3, were chosen for molecular docking analyses to forecast the binding affinity of the ligands with the proteins, revealing favorable interactions between compounds 8a-c and these targets. Molecular dynamics was performed for 100 ns simulation to predict the stability of the compounds. Acceptable RMSD and RMSF values for amino acid residues indicated structural integrity and a stable protein-ligand interaction. Additionally, all designed compounds displayed favorable physicochemical properties and promising ADMET profiles. These 8a and 8c considered as promising leads for targeting AKT1 and STAT3.

Design, Synthesis, Characterization, Molecular Docking Studies, In vitro and In vivo Cervical Cancer Activity of Novel N-Substituted Pyrazole Derivatives

The aim of this study is to design, synthesize, characterize few novel N-substituted pyrazole derivatives and evaluated for their anticancer capabilities; these compounds were selected based on their ability to inhibit the HPV E6 protein. The IR, 1H & 13C NMR, mass spectral and elemental analysis were used to determine the structural details of the newly synthesized substances. The molecular docking was performed using Auto Dock Vina, and the protein-ligand interaction was analyzed using Discovery Studio. The sulforhodamine B (SRB) assay was used to determine the in vitro anticancer activity against the HeLa cell line. Body weight analysis, mean survival time and % increase in life span approaches were used to assess in vivo anticancer effectiveness in Swiss albino mice bearing Dalton’s Lymphoma Ascites (DAL) tumor model. Synthesized compounds have excellent docking energies, according to docking experiments. Compounds I, II and III were selected for anticancer activity in vivo DAL-bearing mice based on the findings of their in vitro and SRB assays exhibiting the antiproliferative activity. Pyrazole derivatives, compounds I and III, demonstrated significant potential as anticancer agents.

Molecular Docking Insights into Gatifloxacin Derivatives as Prospective Antidepressant Agents

ABSTRACT: The current focus in drug discovery aims to identify promising therapeutic candidates for further research. Depression, a significant public health issue, is closely linked to low serotonin levels. Researchers are investigating novel antidepressant agents through chemical modifications and protein analysis. The various derivatives are assessed for their antidepressant activity through in silico molecular docking simulations by using AutoDock and ADME analysis. Autodock includes formation of PDBQT files, docking of converted ligands and protein forms, formation of 9 different positions of docked molecules results into binding score, 2D image and 3D images of same one. Docking studies reveal molecular interactions between biological macromolecules (8FSB) and ligands, with strong protein-ligand interactions potentially inhibiting the reuptake of serotonin at the post-synaptic nerve, thereby increasing serotonin availability and exerting antidepressant effects. Gatifloxacin derivatives demonstrated higher binding affinities with 8FSB, Gati I (-11.4 kcal/mol), Gati II (-11.1 kcal/mol), Gati III (-8.2 kcal/mol), Gati IV (-7.9 kcal/mol), Gati V (-9.5 kcal/mol), and Gati VI (-9.7 kcal/mol), all exceeding gatifloxacin (-6.9 kcal/mol). These findings suggest that gatifloxacin derivatives may serve as potent antidepressants of drug discovery lies in the synergistic use of in vitro and in vivo studies, leveraging technological advancements to develop safer and more effective therapies. The findings related to gatifloxacin derivatives highlight the potential of these approaches in identifying novel antidepressants, paving the way for further research and clinical trials.

Ligand-human serum albumin analysis: the near-UV CD and UV-Vis spectroscopic studies.

Spectroscopic methods offer many new opportunities to study protein-ligand interactions. The aim of this study was to evaluate the possibility of using near-UV CD as well as UV-Vis spectroscopic techniques to study the interaction between human serum albumin (HSA) and markers of Sudlow's site I (warfarin, phenylbutazone) and II (ketoprofen, ibuprofen), as well as prednisolone and indapamide. In order to perform the planned measurements, near-UV CD spectropolarimetry and UV-Vis spectrophotometry have been used. It has been demonstrated that both techniques allow for rapid evaluation of non-covalent interactions between HSA and ligand, as well as identification of the HSA aromatic amino acid residues involved in this process. The near-UV CD spectroscopic data were more valuable than the analysis based on the second derivative of differential UV-Vis absorption spectra, especially for ligands with a non-specified binding site and low affinity towards HSA, such as prednisolone. The combination of both techniques makes it possible for comprehensive analysis of the interaction between HSA and ligands.

Evaluation of AlphaFold3 for the fatty acids docking to human fatty acid-binding proteins

Human fatty acid-binding proteins (FABPs) are involved in many aspects of lipid metabolism, such as the uptake, transport, and storage of lipophilic molecules, as well as cellular functions. Understanding how FABPs recognize fatty acids (FAs) is crucial for identifying FABP function and applications, such as in inhibitor design or biomarker development. The recently developed AlphaFold3 (AF3) demonstrates significantly higher accuracy than other prediction tools, particularly in predicting protein–ligand interactions with state-of-the-art docking tools. Studies on whether AF3 can be used to identify the FAs of FABP are lacking. To assess the accuracy of FA docking to FABPs using AF3, models of FA docked into FABP generated using AF3 were compared with experimentally determined FA-bound FABP structures. FA ligands in AF3 structures docked reliably into the FA-binding pocket of FABPs; however, the detailed binding configuration of most FA ligands docked into FABPs and the interaction between FA and FABP determined using AF3 and experimentally differed. These results will aid in understanding FA docking to FABPs and other FA-binding proteins using AF3.

Dynamics of the Pre-Powerstroke Myosin Lever Arm and the Effects of Omecamtiv Mecarbil.

The binding of small molecules to sarcomeric myosin can elicit powerful effects on the chemomechanical cycle, making them effective therapeutics in the clinic and research tools at the benchtop. However, these myotropes can have complex effects that act on different phases of the crossbridge cycle and which depend on structural, dynamic, and environmental variables. While small molecule binding sites have been identified crystallographically and their effects on contraction studied extensively, small molecule-induced dynamic changes that link structure-function are less studied. Here, we use molecular dynamics simulations to explore how omecamtiv mecarbil (OM), a cardiac myosin-specific myotrope, alters the coordinated dynamics of the lever arm and the motor domain in the pre-powerstroke state. We show that the lever arm adopts a range of orientations and find that different lever arm orientations are accompanied by changes in the hydrogen bonding patterns near the converter. We find that the binding of OM to myosin reduces the conformational heterogeneity of the lever arm orientation and also adjusts the average lever arm orientation. Finally, we map out the distinct conformations and ligand-protein interactions adopted by OM. These results uncover some structural factors that govern the motor domain-tail orientations and the mechanisms by which OM primes the pre-powerstroke myosin heads.

Generating globin-like reactivities in [human serum albumin-FeII(heme)] complex through N-donor ligand addition

Human serum albumin (HSA) has a strong binding affinity for heme b, forming a complex in a 1:1 ratio with the co-factor ([HSA-FeIIIheme]). This system displays spectroscopic and functional properties comparable to globins when chemical derivatives mimicking them are incorporated into the protein matrix. The aim of this study is to generate globin-like systems using [HSA-FeIIIheme] as a protein template and binding N-donor ligands (imidazole, Im; and 1-methylimidazole, 1-MeIm) to construct artificial [HSA-Fe(heme)-(N-donor)] complexes. Their electronic structure and binding thermodynamics are investigated using UV–vis and (synchronous) fluorescence spectroscopies, while ligand-protein interactions are visualized using docking simulations. The imidazole derivatives have a strong affinity for [HSA-FeIIIheme] (K ~ 104–106), where the spontaneous binding of Im and 1-MeIm are dominated by entropic and enthalpic effects, respectively. The reduced form of the [HSA-Fe(heme)-(N-donor)] complexes demonstrate nitrite reductase (NiR) activity similar to that observed in globins, but with significant differences in their rates. [HSA-FeIIheme-(1-MeIm)] reduces nitrite ~4× faster than the Im analogue, and ~ 30× faster than myoglobin (Mb). The enhanced NiR activity of [HSA-FeIIheme-(1-MeIm)] is a cumulative effect of several factors including a slightly expanded and more optimal heme binding pocket, nearby residues as possible proton sources, and a H-bonding interaction between 1-MeIm and residues Arg160 and Lys181 that may have a long-distance influence on the heme π electron density.

PyPLIF HIPPOS-aided Construction and Retrospective Validation of Structure-Based Virtual Screening Protocol Targeting VEGFR2

Recently, the discovery of small molecules as inhibitors for vascular endothelial growth factor receptor 2 (VEGFR2) is of timely interest, especially in the area of ocular neovascular diseases. On the other hand, PyPLIF HIPPOS in combination with machine learning techniques has been reported to increase the prediction quality of structure-based virtual screening (SBVS) protocols. The original version of PyPLIF has served in the development of an SBVS protocol that successfully identified novel chalcone derivatives and short peptides as potent inhibitors for acetylcholinesterase. In this short communication, construction and retrospective validation of an SBVS protocol employing PyPLIF HIPPOS targeting VEGFR2 are presented to make it publicly available. The retrospective validation employed 409 active compounds and 24,950 decoys from the enhanced version of the directory of useful decoys. All compounds were docked independently 3 times using AutoDock Vina followed by the identification of the protein-ligand interaction fingerprints (PLIF) employing PyPLIF HIPPOS. The derived ensemble PLIF descriptors were then used in the decision tree construction using a machine-learning technique called recursive partitioning and regression trees. The best decision was then incorporated in the SBVS protocol. The F-measure and enrichment factor values of the SBVS protocol were 0.387 and 76.879, respectively. Hence, the SBVS protocol is readily available to screen small molecules or short peptides.

Pioneering bioinformatics with agent-based modelling: an innovative protocol to accurately forecast skin or respiratory allergic reactions to chemical sensitizers.

The assessment of theallergenic potential of chemicals, crucial for ensuring public health safety, faces challenges in accuracy and raises ethical concerns due to reliance on animal testing. This paper presents a novel bioinformatic protocol designed to address the critical challenge of predicting immune responses to chemical sensitizers without the use of animal testing. The core innovation lies in the integration of advanced bioinformatics tools, including the Universal Immune System Simulator (UISS), which models detailed immune system dynamics. By leveraging data from structural predictions and docking simulations, our approach provides a more accurate and ethical method for chemical safety evaluations, especially in distinguishing between skin and respiratory sensitizers. Our approach integrates a comprehensive eight-step process, beginning with the meticulous collection of chemical and protein data from databases like PubChem and the Protein Data Bank. Following data acquisition, structural predictions are performed using cutting-edge tools such as AlphaFold to model proteins whose structures have not been previously elucidated. This structural information is then utilized in subsequent docking simulations, leveraging both ligand-protein and protein-protein interactions to predict how chemical compounds may trigger immune responses. The core novelty of our method lies in the application of UISS-an advanced agent-based modelling system that simulates detailed immune system dynamics. By inputting the results from earlier stages, including docking scores and potential epitope identifications, UISS meticulously forecasts the type and severity of immune responses, distinguishing between Th1-mediated skin and Th2-mediated respiratory allergic reactions. This ability to predict distinct immune pathways is a crucial advance over current methods, which often cannot differentiate between the sensitization mechanisms. To validate the accuracy and robustness of our approach, we applied the protocol to well-known sensitizers: 2,4-dinitrochlorobenzene for skin allergies and trimellitic anhydride for respiratory allergies. The results clearly demonstrate the protocol's ability to differentiate between these distinct immune responses, underscoring its potential for replacing traditional animal-based testing methods. The results not only support the potential of our method to replace animal testing in chemical safety assessments but also highlight its role in enhancing the understanding of chemical-induced immune reactions. Through this innovative integration of computational biology and immunological modelling, our protocol offers a transformative approach to toxicological evaluations, increasing the reliability of safety assessments.

Evaluating the potential of non-immunosuppressive cyclosporin analogs for targeting Toxoplasma gondii cyclophilin: Insights from structural studies.

Toxoplasmosis persists as a prevalent disease, facing challenges from parasite resistance and treatment side effects. Consequently, identifying new drugs by exploring novel protein targets is essential for effective intervention. Cyclosporin A (CsA) possesses antiparasitic activity against Toxoplasma gondii, with cyclophilins identified as possible targets. However, CsA immunosuppressive nature hinders its use as an antitoxoplasmosis agent. Here, we evaluate the potential of three CsA derivatives devoid of immunosuppressive activity, namely, NIM811, Alisporivir, and dihydrocyclosporin A to target a previously characterized cyclophilin from Toxoplasma gondii (TgCyp23). We determined the X-ray crystal structures of TgCyp23 in complex with the three analogs and elucidated their binding and inhibitory properties. The high resolution of the structures revealed the precise positioning of ligands within the TgCyp23 binding site and the details of protein-ligand interactions. A comparison with the established ternary structure involving calcineurin indicates that substitutions at position 4 in CsA derivatives prevent calcineurin binding. This finding provides a molecular explanation for why CsA analogs can target Toxoplasma cyclophilins without compromising the human immune response.

Screening of DNMT3A inhibitors from phytochemicals using molecular docking and molecular dynamics simulation for their anti-cancer potential

ABSTRACT There is a consensus that epigenomic changes play a significant role in carcinogenesis. The effect of DNA methylation and its increased modulations on gene expression during carcinogenesis is significant for diagnostic and therapeutic purposes. Potential phytochemicals as anticancer approach modulators of epigenetic pathways are the focus of this investigation. Molecular docking studies were conducted to foretell how phytochemicals will interact with DNMT3A. As part of our effort to identify novel anti-cancer treatments, we examined a wide variety of phytochemicals from many chemical classes, including cannabinoids, carotenoids, chalcones, fatty acids, lignans, polysaccharides, saponins, steroids, and tannins. The docking scores for Dihydromyricetin are -10.207 kcal/mol, -9.313 kcal/mol for S-Adenosyl-L-methionine (SAM), and -8.757 kcal/mol for Zeylenol were determined to be superior than to phytochemical inhibitors against DNMT3A in the virtual screening method. Docking scores, hydrogen bond interactions, ADME characteristics, and DFT. Molecular Dynamics Simulation and free energy calculations demonstrated that these compounds bind stably to DNMT3A, potentially inhibiting its activity. Network Pharmacology suggested Dihydromyricetin's specific anticancer potential against cervical cancer. These findings provide insights into protein-Ligand interactions with Dnmt3A and highlights the need for In-vitro validation.

A novel brominated chalcone derivative as a promising multi-target inhibitor against multidrug-resistant Listeria monocytogenes

Foodborne pathogens continue to challenge public health due to their ability to cause severe illness and their increasing resistance to current antimicrobial treatments. Listeria monocytogenes is a resilient foodborne pathogen that poses significant risks to vulnerable populations, leading to severe infections and high hospitalization rates. The emergence of antimicrobial-resistant (AMR) strains of L. monocytogenes underscores the need for novel therapeutic strategies. In this study, we investigated the antimicrobial efficacy of the (2E)-3-(3,5-dibromo-2-hydroxylphenyl)-1-(5-methylfuran-2-yl) prop-2-en-1-one (DK06) against multidrug-resistant L. monocytogenes. DK06 exhibited a significant dose-dependent inhibition of L. monocytogenes growth, achieving a maximum inhibition of 92.9 % at 320 μM. Molecular docking and dynamics simulations revealed high binding affinities for key virulence proteins PlcB and ArgA, with stable protein-ligand interactions. DK06 also disrupted biofilm formation at sub-MIC levels, reducing extracellular polymeric substances (EPS) and biofilm mass, as observed by scanning electron microscopy (SEM) analysis. Furthermore, DK06 downregulated the expression of virulence genes (plcB, argA, and hly) and decreased hemolytic activity. In vivo zebrafish studies confirmed the safety of DK06 up to 80 μM, demonstrating its efficacy in reducing mortality and oxidative stress associated with L. monocytogenes infection. DK06 also attenuated inflammation by downregulating key inflammatory markers (tnfa, il1b, il6, and nfkb). These findings indicate that DK06 is a promising multi-target inhibitor with potential application in treating infections and combating antimicrobial resistance.

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs.

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models' reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein-ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery.

Oxadiazolines as Photoreleasable Labels for Drug Target Identification.

Photoaffinity labeling is a widely used technique for studying ligand-protein and protein-protein interactions. Traditional photoaffinity labels utilize nonspecific C-H bond insertion reactions mediated by a highly reactive intermediate. Despite being the most widely used photoaffinity labels, diazirines exhibit limited compatibility with downstream organic reactions and suffer from storage stability concerns. This study introduces oxadiazolines as innovative and complementary photoactivatable labels for addition to the toolbox and demonstrates their application in vitro and through in cellulo labeling experiments. Oxadiazolines can be easily synthesized from ketone moieties and cleaved with 302-330 nm light to cleanly liberate a diazo reactive moiety that can covalently modify nucleophilic amino acid residues. Notably, oxadiazolines are compatible with various organic reaction conditions and functional groups, allowing for the exploration of a large chemical space. Several known inhibitors featuring the oxadiazoline functionality were prepared without affecting their binding affinity. Furthermore, we confirmed the ability of oxadiazolines to form covalent bonds with proteins upon UV-irradiation, both in vitro and in cellulo, yielding comparable results to those of the matched diazirine compounds.

Solvation free energy in governing equations for DNA hybridization, protein-ligand binding, and protein folding.

This work examines the thermodynamics of model biomolecular interactions using a governing equation that accounts for the participation of bulk water in the equilibria. In the first example, the binding affinities of two DNA duplexes, one of nine and one of 10 base pairs in length, are measured and characterized by isothermal titration calorimetry (ITC) as a function of concentration. The results indicate that the change in solvation free energy that accompanies duplex formation (ΔGS) is large and unfavorable. The duplex with the larger number of G:C pairings yields the largest change in solvation free energy, ΔGS = +460 kcal·mol-1per base pair at 25 °C. A van't Hoff analysis of the data is complicated by the varying degree of intramolecular base stacking within each DNA strand as a function of temperature. A modeling study reveals how the solvation free energy alters the output of a typical ITC experiment and leads to a good, though misleading, fit to the classical equilibrium equation. The same thermodynamic framework is applied to a model protein-ligand interaction, the binding of ribonuclease A with the nucleotide inhibitor 3'-UMP, and to a conformational equilibrium, the change in tertiary structure of α-lactalbumin in molar guanidinium chloride solutions. The ribonuclease study yields a value of ΔGS = +160 kcal·mol-1, whereas the folding equilibrium yields ΔGS ≈ 0, an apparent characteristic of hydrophobic interactions. These examples provide insight on the role of solvation energy in binding equilibria and suggest a pivot in the fundamental application of thermodynamics to solution chemistry.

Determination of Protein-Ligand Binding Affinities by Thermal Shift Assay.

Quantification of protein-ligand interactions is crucial for understanding the protein's biological function and for drug discovery. In this study, we employed three distinct approaches for determination of protein-ligand binding affinities by a thermal shift assay using a single ligand concentration. We present the results of the comparison of the performance of the conventional curve fitting (CF) method and two newly introduced methods - assuming zero heat capacity change across small temperature ranges (ZHC) and utilizing the unfolding equilibrium constant (UEC); the latter has the advantage of reducing calculations by obtaining the unfolding equilibrium constant directly from the experimental data. Our results highlight superior performance of the ZHC and UEC methods over the conventional CF method in estimating the binding affinity, irrespective of the ligand concentration. In addition, we evaluated how the new methods can be applied to high-throughput screening for potential binders, when the enthalpy (ΔH L) and molar heat capacity change (ΔC PL) of ligand binding are unknown. Our results suggest that, in this scenario, using the -300 cal K-1 mol-1 assumption for ΔC pL and either -5 kcal mol-1 or the average enthalpy efficiency-based estimation for ΔH L(T) can still provide reasonable estimates of the binding affinity. Incorporating the new methods into the workflow for screening of small drug-like molecules, typically conducted using single-concentration libraries, could greatly simplify and streamline the drug discovery process.

Thymol-1,2,3-triazole derivatives: Network pharmacology, molecular simulations and synthesis targeting breast cancer

Cancer prevention has become a major focus of public health in the 21st century. Despite advancements in treatment, tackling cancer remains a significant challenge due to the different types of cancer. Breast cancer stands out as the leading cause of cancer-related deaths, highlighting its significant impact on female health worldwide. Monoterpenes, exemplified by thymol, exhibit notable potential in cancer treatment, owing to their pharmacological properties and mechanisms of action. Concurrently, 1,2,3-triazoles have garnered attention as promising entities for the development of anticancer agents, showcasing their efficacy and relevance in oncology research. This study describes the synthesis and characterization of a novel library of 1,2,3-triazole derivatives tethered to p-methoxythymol, a natural product. High-resolution mass spectrometry (HRMS) definitively confirmed the structures of the synthesized compounds, which were further elucidated by detailed analysis of their ¹H- and ¹³CNMR spectra. To predict the suitable target, network pharmacology was applied to the synthesized thymol derivatives, resulting in the identification of phosphatidylinositol-3-kinase (PIK3CA) as a key target. Furthermore, these thymol derivatives were screened for ADMET and molecular docking against the phosphatidylinositol-3-kinase (PIK3CA) target. Molecular dynamics simulations were conducted for the top two compounds over a duration of 100 ns to assess their stability. The RMSD (Root-Mean-Square Devia­tion) and RMSF (root-mean-square fluctuation) values for amino acid residues indicated structural integrity and stable protein-ligand interaction. Additionally, all designed compounds displayed favorable physicochemical properties and ADMET profiles, and two compounds, 4b and 4d, were found to be new lead molecules targeting PIK3CA