Ranking Of Binding Affinities Research Articles

Abstract 6757: The anti-tumor efficacy of Th1-specific TROP2 vaccine (AST-07X) in a triple-negative breast cancer murine model

Abstract Background: The trophoblast cell-surface antigen 2 (TROP2) is associated with enhanced tumor proliferation and poor prognosis in breast cancer. TROP2 overexpresses on various epithelial cancer cell surfaces, and is commonly found in prostate, colon, pancreas, lung, and breast cancer. Therefore, anticancer strategy targeting TROP2 may be a potential treatment option for patients with metastatic triple-negative breast cancer (TNBC). Sacituzumab govitecan, a Trop-2-directed antibody drug conjugate (ADC), has shown significant benefits associated with progression-free and overall survival compared to standard chemotherapy for treating metastatic TNBC; however, its toxic effects were more frequent than that of chemotherapy (NCT02574455). In this study, we discovered TROP2 target peptides with T-helper 1 (Th1) polarization and anticancer efficacy. We then evaluated the anti-tumor efficacy of these epitopes and possibility of co-administration with ADC in a TNBC murine model. Materials and Methods: Th1 peptides were identified using the Th-Vac® platform and the predictive binding affinity ranking that indicates potential immunogenicity points of interest for HLA class II. Human peripheral blood mononuclear cell (PBMC) Th1-specific polarization-induced activity and in vivo immunogenicity (Module 2b) were evaluated using flow cytometry and ELISpot analysis. The anti-tumor efficacy of the selected Th1 peptide was evaluated against MDA-MB-23 inoculated athymic Balb/c mice. Each peptide was administered intradermally once weekly for a total of four times at a concentration of 100 μg/head. Sacituzumab govitecan (0.81 mg/kg) was administered intravenously once weekly, for a total of two times. Results: Ten peptides with high affinity for HLA class II were selected, and three epitopes that induce differentiation of T cells secreting TROP2-specific IFN-γ were subsequently selected. These epitopes (p1, p3 and p6) showed tumor growth inhibition efficacy of 52.4%, 38.5%, and 57.2%, respectively, compared to the control (vehicle) group in the TNBC tumor-bearing mice. The cocktail vaccine (AST-07X), which combined these epitopes, was the most dominant at 65.4%. The anti-tumor efficacy of the combination group of the cocktail vaccine and ADC was 77.4% compared with the control group, showing a potent synergistic effect compared to the 46.1% anti-tumor efficacy of the ADC-mono group. Conclusion: AST-07X that induce TROP2-specific Th1 anticancer immune responses, showed sufficient anti-tumor efficacy; these effects were evident under immunodeficient conditions, such as those of TNBC tumor-bearing athymic mice. Synergy effects were also induced via combined ADC administration. We are currently conducting further research on humanized mice to address the limitations of the immunodeficient murine model. Citation Format: Jinho Kang, Hyo-Hyun Park, Jin Kyeong Choi, Seong-Yong Jang, Min-Ah Kim, Myeong-Kyu Park, Ashley Youngmin Kim, Eunkyo Joung, Hun Jung. The anti-tumor efficacy of Th1-specific TROP2 vaccine (AST-07X) in a triple-negative breast cancer murine model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6757.

Insights into the interaction dynamics between volatile anesthetics and tubulin through computational molecular modelling

General anesthetics, able to reversibly suppress all conscious brain activity, have baffled medical science for decades, and little is known about their exact molecular mechanism of action. Given the recent scientific interest in the exploration of microtubules as putative functional targets of anesthetics, and the involvement thereof in neurodegenerative disorders, the present work focuses on the investigation of the interaction between human tubulin and four volatile anesthetics: ethylene, desflurane, halothane and methoxyflurane. Interaction sites on different tubulin isotypes are predicted through docking, along with an estimate of the binding affinity ranking. The analysis is expanded by Molecular Dynamics simulations, where the dimers are allowed to freely interact with anesthetics in the surrounding medium. This allowed for the determination of interaction hotspots on tubulin dimers, which could be linked to different functional consequences on the microtubule architecture, and confirmed the weak, Van der Waals-type interaction, occurring within hydrophobic pockets on the dimer. Both docking and MD simulations highlighted significantly weaker interactions of ethylene, consistent with its far lower potency as a general anesthetic. Overall, simulations suggest a transient interaction between anesthetics and microtubules in general anesthesia, and contact probability analysis shows interaction strengths consistent with the potencies of the four compounds. Communicated by Ramaswamy H. Sarma

In silico methods predict new blood-brain barrier permeable structure for the inhibition of kynurenine 3-monooxygenase

Kynurenine 3-monooxygenase (KMO) regulates the levels of bioactive substances in the kynurenine pathway of tryptophan catabolism and its activity is tied to so many diseases that finding an appropriate inhibitor for KMO has become an urgent task. This especially proved to be difficult for the central nervous system related diseases due to the requirement that the supposed inhibitor should be both blood brain barrier permeable and should not cause hydrogen peroxide as a harmful side product. In this in silico study, we present our step-wise approach, whose starting point is based on the important experimental observations. To tackle the problem, a library of 7561938 structures was obtained from Zinc15 database utilizing the tranche browser. From this library, a subset of 501777 structures was determined with the considerations of their functional groups that constrain their applicability. Then, the binding affinity ranking of this set of structures was determined via virtual screening. Starting from the structures whose affinities are the highest among this subset, the ADMET properties were checked through in silico methods and the binding properties of the selected inhibitor candidates were further investigated via molecular dynamics simulations and MM/GBSA calculations. According to the computational results of this study, ZINC_71915355 has passed all the evaluations and is a potentially BBB permeable structure that can inhibit KMO. Additionally, ZINC_19827377 was identified as a new potential KMO inhibitor which may be more suitable for peripheral administration. From the in silico study presented herein, ZINC_71915355 and ZINC_19827377 structures, which showed high binding affinity without harmful H2O2 production, along with the tailored properties can now serve as powerful candidates for KMO inhibition and these hits are worth of further experimental validation.

Modeling the Sterol-Binding Domain of Aster-A Provides Insight into Its Multiligand Specificity.

Intracellular transport of cholesterol and related sterols relies to a large degree on nonvesicular mechanisms, which are only partly understood at the molecular level. Aster proteins belonging to the Lam family of sterol transfer proteins have recently been identified as important catalysts of nonvesicular sterol exchange between the plasma membrane (PM) and endoplasmic reticulum (ER). Here, we used a range of computational tools to study the molecular mechanisms underlying sterol binding as well as multisterol ligand specificity of Aster-A. Our study focused primarily on gaining atomistic insight into the bound ligand-protein complex and was, on this basis, performed in the absence of any membrane. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations provide a rationale for the experimentally found ranking of binding affinities of various sterols to Aster-A. In particular, the polarity of the sterols and the length of their alkyl chain could be identified as being critical determinants of ligand affinity. A Gibbs free energy decomposition identified a charged residue, Glu444, at the base of the binding pose as an important control point for sterol binding. Removing its net charge via protonation was found to cause significant changes to the environment surrounding this residue. In addition, the protonation of Glu444 was found to be paralleled by a large redistribution of molecular flexibility in the Aster domain. This finding was supplemented by multiple branched adaptive steered molecular dynamics (MB-ASMD) simulations by which we defined a possible molecular path for sterol release and demonstrated the importance of Glu444 in this process.

Unveiling the molecular mechanism of SARS-CoV-2 main protease inhibition from 137 crystal structures using algebraic topology and deep learning.

Currently, there is neither effective antiviral drugs nor vaccine for coronavirus disease 2019 (COVID-19) caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to its high conservativeness and low similarity with human genes, SARS-CoV-2 main protease (Mpro) is one of the most favorable drug targets. However, the current understanding of the molecular mechanism of Mpro inhibition is limited by the lack of reliable binding affinity ranking and prediction of existing structures of Mpro–inhibitor complexes. This work integrates mathematics (i.e., algebraic topology) and deep learning (MathDL) to provide a reliable ranking of the binding affinities of 137 SARS-CoV-2 Mpro inhibitor structures. We reveal that Gly143 residue in Mpro is the most attractive site to form hydrogen bonds, followed by Glu166, Cys145, and His163. We also identify 71 targeted covalent bonding inhibitors. MathDL was validated on the PDBbind v2016 core set benchmark and a carefully curated SARS-CoV-2 inhibitor dataset to ensure the reliability of the present binding affinity prediction. The present binding affinity ranking, interaction analysis, and fragment decomposition offer a foundation for future drug discovery efforts.

D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors.

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4 (GC4), which focused on predicting the binding poses and affinity ranking for compounds targeting the [Formula: see text]-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2Å RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and DOCK 6 and found that using a reference ligand to guide the docking process is a better strategy for pose prediction and helped HYBRID to perform better here. We also conducted end-point free energy estimates on molecules dynamics based ensembles of protein-ligand complexesusing molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R GC4 are: (i) the generation of the macrocyclic conformers is a key step for successful pose prediction, (ii) the protonation states of the BACE-1 binding site should be treated carefully, (iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and (iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.

Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S.

Cathepsin S (CatS), a member of cysteine cathepsin proteases, has been well studied due to its significant role in many pathological processes, including arthritis, cancer and cardiovascular diseases. CatS inhibitors have been included in D3R-GC3 for both docking pose prediction and affinity ranking, and in D3R-GC4 for binding affinity ranking. The difficulties posed by CatS inhibitors in D3R mainly come from three aspects: large size, high flexibility and similar chemical structures. We have participated in GC4; our best submitted model, which employs a similarity-based alignment docking and Vina scoring protocol, yielded Kendall's τ of 0.23 for 459 binders in GC4. In our further explorations with machine learning, by curating a CatS specific training set, adopting a similarity-based constrained docking method as well as an arm-based fragmentation strategy which can describe large inhibitors in a locality-sensitive fashion, our best structure-based ranking protocol can achieve Kendall's τ of 0.52 for all binders in GC4. In this exploration process, we have demonstrated the importance of training data, docking approaches and fragmentation strategies in inhibitor-ranking protocol development with machine learning.

Cellulases adsorb reversibly on biomass lignin.

Adsorption of cellulases onto lignin is considered a major factor in retarding enzymatic cellulose degradation of lignocellulosic biomass. However, the adsorption mechanisms and kinetics are not well understood for individual types of cellulases. This study examines the binding affinity, kinetics of adsorption, and competition of four monocomponent cellulases of Trichoderma reesei during adsorption onto lignin. TrCel7A, TrCel6A, TrCel7B, and TrCel5A were radiolabeled for adsorption experiments on lignin‐rich residues (LRRs) isolated from hydrothermally pretreated spruce (L‐HPS) and wheat straw (L‐HPWS), respectively. On the basis of adsorption isotherms fitted to the Langmuir model, the ranking of binding affinities was TrCel5A > TrCel6A > TrCel7B > TrCel7A on both types of LRRs. The enzymes had a higher affinity to the L‐HPS than to the L‐HPWS. Adsorption experiments with dilution after 1 and 24 hr and kinetic modeling were performed to quantify any irreversible binding over time. Models with reversible binding parameters fitted well and can explain the results obtained. The adsorption constants obtained from the reversible models agreed with the fitted Langmuir isotherms and suggested that reversible adsorption–desorption existed at equilibrium. Competitive binding experiments showed that individual types of cellulases competed for binding sites on the lignin and the adsorption data fitted the Langmuir adsorption model. Overall, the data strongly indicate that the adsorption of cellulases onto lignin is reversible and the findings have implications for the development of more efficient cellulose degrading enzymes.

Calculate protein-ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3.

We participatedin the Cathepsin S (CatS) sub-challenge of the Drug Design Data Resource (D3R) Grand Challenge 3 (GC3) in 2017 to blindly predict the binding poses of 24 CatS-bound ligands, the binding affinity ranking of 136 ligands, and the binding free energies of a subset of 33 ligands in Stage 1A and Stage 2. Our submitted predictions ranked relatively well compared to the submissions from other participants. Here we present our methodologies used in the challenge. For the binding pose prediction, we employed the Glide module in the Schrodinger Suite 2017 and AutoDock Vina. For the binding affinity/free energy prediction, we carried out molecular dynamics simulations of the complexes in explicit water solvent with counter ions, and then estimated the binding free energies with our newly developed model of extended linear interaction energy (ELIE), which is inspired by two other popular end-point approaches: the linear interaction energy(LIE) method, and the molecular mechanics with Poisson-Boltzmann surface area solvation method(MM/PBSA). Our studies suggest that ELIE is a good trade-off between efficiency and accuracy, and it is appropriate for filling the gap between the high-throughput docking and scoring methods and the rigorous but much more computationally demanding methods like free energy perturbation(FEP) or thermodynamics integration(TI) in computer-aided drug design (CADD)projects.

Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.

Advanced mathematics, such as multiscale weighted colored subgraph and element specific persistent homology, and machine learning including deep neural networks were integrated to construct mathematical deep learning models for pose and binding affinity prediction and ranking in the last two D3R Grand Challenges in computer-aided drug design and discovery. D3R Grand Challenge 2 focused on the pose prediction, binding affinity ranking and free energy prediction for Farnesoid X receptor ligands. Our models obtained the top place in absolute free energy prediction for free energy set 1 in stage 2. The latest competition, D3R Grand Challenge 3 (GC3), is considered as the most difficult challenge so far. It has five subchallenges involving Cathepsin S and five other kinase targets, namely VEGFR2, JAK2, p38-α, TIE2, and ABL1. There is a total of 26 official competitive tasks for GC3. Our predictions were ranked 1st in 10 out of these 26 tasks.

Role of Resultant Dipole Moment in Mechanical Dissociation of Biological Complexes.

Protein-peptide interactions play essential roles in many cellular processes and their structural characterization is the major focus of current experimental and theoretical research. Two decades ago, it was proposed to employ the steered molecular dynamics (SMD) to assess the strength of protein-peptide interactions. The idea behind using SMD simulations is that the mechanical stability can be used as a promising and an efficient alternative to computationally highly demanding estimation of binding affinity. However, mechanical stability defined as a peak in force-extension profile depends on the choice of the pulling direction. Here we propose an uncommon choice of the pulling direction along resultant dipole moment (RDM) vector, which has not been explored in SMD simulations so far. Using explicit solvent all-atom MD simulations, we apply SMD technique to probe mechanical resistance of ligand-receptor system pulled along two different vectors. A novel pulling direction—when ligand unbinds along the RDM vector—results in stronger forces compared to commonly used ligand unbinding along center of masses vector. Our observation that RDM is one of the factors influencing the mechanical stability of protein-peptide complex can be used to improve the ranking of binding affinities by using mechanical stability as an effective scoring function.

Combination of human acetylcholinesterase and serum albumin sensing surfaces as highly informative analytical tool for inhibitor screening

In the continuous research for potential drug lead candidates, the availability of highly informative screening methodologies may constitute a decisive element in the selection of best-in-class compounds. In the present study, a surface plasmon resonance (SPR)-based assay was developed and employed to investigate interactions between human recombinant AChE (hAChE) and four known ligands: galantamine, tacrine, donepezil and edrophonium. To this aim, a sensor chip was functionalized with hAChE using mild immobilization conditions to best preserve enzyme integrity. Binding affinities and, for the first time, kinetic rate constants for all drug–hAChE complexes formation/disruption were determined. Inhibitors were classified in two groups: slow-reversible and fast-reversible binders according to respective target residence time. Combining data obtained on drug–target residence time with data obtained on serum albumin binding levels, a good correlation with potency, plasma protein binding in vivo, and administration regimen was found. The outcomes of this work demonstrated that the developed SPR-based assay is suitable for the screening, the binding affinity ranking and the kinetic evaluation of hAChE inhibitors. The method proposed ensures a simpler and cost-effective assay to quantify kinetic rate constants for inhibitor–hAChE interaction as compared with other proposed and published methods. Eventually, the determination of residence time in combination with preliminary ADME studies might constitute a better tool to predict in vivo behaviour, a key information for the research of new potential drug candidates.

Molecular dynamic modeling of CYP51B in complex with azole inhibitors

Cytochrome P450 14α-sterol demethylase (CYP51), the key enzyme in sterol biosynthesis pathway, is an important target protein of cholesterol-lowering agents, antifungal drugs, and herbicides. CYP51B enzyme is one of the CYP51 family members. In the present study, we have chosen four representative inhibitors of CYP51B: diniconazole (Din), fluconazole (Flu), tebuconazole (Teb), and voriconazole (Vor), and launched to investigate the binding features of CYP51B-inhibitor and gating characteristics via molecular docking and molecular dynamics (MD) simulations. The results show that the ranking of binding affinities among these inhibitors is Din > Teb > Vor > Flu. Din shows the strongest binding affinity, whereas Flu shows the weakest binding affinity. More importantly, based on the calculated binding free energy results, we hypothesize that the nonpolar interactions are the most important contributors, and three key residues (Thr77, Ala258, and Lys454) play crucial role in protein-inhibitor binding. Besides, residue Phe180 may play a molecular switch role in the process of the CYP51B-Teb and CYP51B-Vor binding. Additionally, Tunnel analysis results show that the major tunnel of Din, Flu, and Teb is located between helix K, FG loop, and β4 hairpin (Tunnel II).The top ranked possible tunnel (Tunnel II) corresponds to Vor exits through helix K, F and helix J. This study further revealed the CYP51B relevant structural characteristics at the atomic level as well as provided a basis for rational design of new and more efficacious antifungal agents.

BayesPI-BAR: a new biophysical model for characterization of regulatory sequence variations.

Sequence variations in regulatory DNA regions are known to cause functionally important consequences for gene expression. DNA sequence variations may have an essential role in determining phenotypes and may be linked to disease; however, their identification through analysis of massive genome-wide sequencing data is a great challenge. In this work, a new computational pipeline, a Bayesian method for protein–DNA interaction with binding affinity ranking (BayesPI-BAR), is proposed for quantifying the effect of sequence variations on protein binding. BayesPI-BAR uses biophysical modeling of protein–DNA interactions to predict single nucleotide polymorphisms (SNPs) that cause significant changes in the binding affinity of a regulatory region for transcription factors (TFs). The method includes two new parameters (TF chemical potentials or protein concentrations and direct TF binding targets) that are neglected by previous methods. The new method is verified on 67 known human regulatory SNPs, of which 47 (70%) have predicted true TFs ranked in the top 10. Importantly, the performance of BayesPI-BAR, which uses principal component analysis to integrate multiple predictions from various TF chemical potentials, is found to be better than that of existing programs, such as sTRAP and is-rSNP, when evaluated on the same SNPs. BayesPI-BAR is a publicly available tool and is able to carry out parallelized computation, which helps to investigate a large number of TFs or SNPs and to detect disease-associated regulatory sequence variations in the sea of genome-wide noncoding regions.

Molecular Dynamics of Interactions between Rigid and Flexible Antifolates and Dihydrofolate Reductase from Pyrimethamine‐Sensitive and Pyrimethamine‐Resistant Plasmodium falciparum

Currently, the usefulness of antimalarials such as pyrimethamine (PYR) is drastically reduced due to the emergence of resistant Plasmodium falciparum (Pf) caused by its dihydrofolate reductase (PfDHFR) mutations, especially the quadruple N51I/C59R/S108N/I164L mutations. The resistance was due to the steric conflict of PYR with S108N. WR99210 (WR), a dihydrotriazine antifolate with a flexible side chain that can avoid such conflict, can overcome this resistance through tight binding with the mutant. To understand factors contributing to different binding affinities of PYR/WR to the wild type (WT) and quadruple mutant (QM), we performed simulations on WR-WT, WR-QM, PYR-WT, and PYR-QM complexes and found that Ile14 and Asp54 were crucial for PYR/WR binding to PfDHFR due to strong hydrogen bonds. The quadruple mutations cause PYR to form, on average, fewer hydrogen bonds with Ile14 and Leu164, and to be displaced from its optimal orientation for Asp54 interaction. The predicted binding affinity ranking (WR-QM ≈ WR-WT ≈ PYR-WT >> PYR-QM) reasonably agrees with the inhibition constant (K(i)) ranking. Our results reveal important residues for tight binding of PYR/WR to WT/QM, which may be used to evaluate the inhibition effectiveness of antimalarials and to provide fundamental information for designing new drugs effective against drug-resistant P. falciparum.

Identification of antagonists to the vasotocin receptor sub-type 4 (VT4R) involved in stress by molecular modelling and verification using anterior pituitary cells

The vasotocin receptor family is homologous to the mammalian vasopressin G-protein coupled receptor (GPCR) family. The vasotocin receptor 2 (VT2R) and 4 (VT4R) have recently been shown to play important role(s) in the neuroendocrine regulation of stress in birds. A homology-based structural model of VT4R of the domestic chicken, Gallus gallus, was built using the sophisticated SYBYL-X suite. The structure of VT4R built with and without extra- and intracellular unstructured loops showed a seven-helix transmembrane domain, which is a characteristic feature of GPCRs. Several agonists and antagonists were screened by molecular docking to map their potential binding sites on the structure of VT4R. Interestingly, the presence of the N-terminal, intracellular and extracellular loops and C-terminal amino acid sequences emerging from the transmembrane domains during molecular docking appeared to influence the binding interface of the peptide agonists and peptide/non-peptide antagonists on the VT4R. The presence of unstructured loops, however, did not affect the relative binding affinity ranking of the peptide antagonists to VT4R. In general, the natural ligand, arginine vasotocin and the peptide/non-peptide antagonists were observed to be more deeply buried in the receptor. Results of in vitro inhibition experiments, using cultured anterior pituitary cells, showed excellent agreement with the binding affinity of the antagonists predicted by molecular docking. The results of this study provide valuable clues for the rational design of novel pharmaceutical compounds capable of blocking or attenuating the stress response.

Protein–Ligand Binding Free Energies from Exhaustive Docking

We explore the use of exhaustive docking as an alternative to Monte Carlo and molecular dynamics sampling for the direct integration of the partition function for protein-ligand binding. We enumerate feasible poses for the ligand and calculate the Boltzmann factor contribution of each pose to the partition function. From the partition function, the free energy, enthalpy, and entropy can be derived. All our calculations are done with a continuum solvation model that includes solving the Poisson equation. In contrast to Monte Carlo and molecular dynamics simulations, exhaustive docking avoids (within the limitations of a discrete sampling) the question of "Have we run long enough?" due to its deterministic complete enumeration of states. We tested the method on the T4 lysozyme L99A mutant, which has a nonpolar cavity that can accommodate a number of small molecules. We tested two electrostatic models. Model 1 used a solute dielectric of 2.25 for the complex apoprotein and free ligand and 78.5 for the solvent. Model 2 used a solute dielectric of 2.25 for the complex and apoprotein but 1.0 for the free ligand. For our test set of eight molecules, we obtain a reasonable correlation with a Pearson r(2) = 0.66 using model 1. The trend in binding affinity ranking is generally preserved with a Kendall τ = 0.64 and Spearman ρ = 0.83. With model 2, the correlation is improved with a Pearson r(2) = 0.83, Kendall τ = 0.93, and Spearman ρ = 0.98. This suggests that the energy function and sampling method adequately captured most of the thermodynamics of binding of the nonpolar ligands to T4 lysozyme L99A.

Rapid and accurate ranking of binding affinities of epidermal growth factor receptor sequences with selected lung cancer drugs

The epidermal growth factor receptor (EGFR) is a major target for drugs in treating lung carcinoma. Mutations in the tyrosine kinase domain of EGFR commonly arise in human cancers, which can cause drug sensitivity or resistance by influencing the relative strengths of drug and ATP-binding. In this study, we investigate the binding affinities of two tyrosine kinase inhibitors--AEE788 and Gefitinib--to EGFR using molecular dynamics simulation. The interactions between these inhibitors and the EGFR kinase domain are analysed using multiple short (ensemble) simulations and the molecular mechanics/Poisson-Boltzmann solvent area (MM/PBSA) method. Here, we show that ensemble simulations correctly rank the binding affinities for these systems: we report the successful ranking of each drug binding to a variety of EGFR sequences and of the two drugs binding to a given sequence, using petascale computing resources, within a few days.

Assessment of a robust model protocol with accelerated throughput for a human recombinant full length estrogen receptor-α binding assay: Protocol optimization and intralaboratory assay performance as initial steps towards validation

Despite about two decades of research in the field of endocrine active compounds, still no validated human recombinant (hr) estrogen receptor-α (ERα) binding assay is available, although hr-ERα is available from several sources. In a joint effort, US EPA and Bayer Schering Pharma with funding from the EU-sponsored 6th framework project, ReProTect, developed a model protocol for such a binding assay. Important features of this assay are the use of a full length hr-ERα and performance in a 96-well plate format. A full length hr-ERα was chosen, as it was considered to provide the most accurate and human-relevant results, whereas truncated receptors could perform differently. Besides three reference compounds [17β-estradiol, norethynodrel, dibutylphthalate] nine test compounds with different affinities for the ERα [diethylstilbestrol (DES), ethynylestradiol, meso-hexestrol, equol, genistein, o, p′-DDT, nonylphenol, n-butylparaben, and corticosterone] were used to explore the performance of the assay. Three independent experiments per compound were performed on different days, and dilutions of test compounds from deep-frozen stocks, solutions of radiolabeled ligand and receptor preparation were freshly prepared for each experiment. The ERα binding properties of reference and test compounds were well detected. As expected dibutylphthalate and corticosterone were non-binders in this assay. In terms of the relative ranking of binding affinities, there was good agreement with published data obtained from experiments using a human recombinant ERα ligand binding domain. Irrespective of the chemical nature of the compound, individual IC 50-values for a given compound varied by not more than a factor of 2.5. Our data demonstrate that the assay was robust and reliably ranked compounds with strong, weak, and no affinity for the ERα with high accuracy. It avoids the manipulation and use of animals, i.e., the preparation of uterine cytosol as receptor source from ovariectomized rats, as a recombinant protein is used and thus contributes to the 3R concept (reduce, replace, and refine). Furthermore, in contrast to other assays, this assay could be adjusted to an intermediate/high throughput format. On the whole, this assay is a promising candidate for further validation.

Assessment of a recombinant androgen receptor binding assay: Initial steps towards validation

Despite more than a decade of research in the field of endocrine active compounds with affinity for the androgen receptor (AR), still no validated recombinant AR binding assay is available, although recombinant AR can be obtained from several sources. With funding from the European Union (EU)-sponsored 6th framework project, ReProTect, we developed a model protocol for such an assay based on a simple AR binding assay recently developed at our institution. Important features of the protocol were the use of a rat recombinant fusion protein to thioredoxin containing both the hinge region and ligand binding domain (LBD) of the rat AR (which is identical to the human AR-LBD) and performance in a 96-well plate format. Besides two reference compounds [dihydrotestosterone (DHT), androstenedione] ten test compounds with different affinities for the AR [levonorgestrel, progesterone, prochloraz, 17α-methyltestosterone, flutamide, norethynodrel, o,p′-DDT, dibutylphthalate, vinclozolin, linuron] were used to explore the performance of the assay. At least three independent experiments per compound were performed. The AR binding properties of reference and test compounds were well detected, in terms of the relative ranking of binding affinities, there was good agreement with published data obtained from experiments using recombinant AR preparations. Irrespective of the chemical nature of the compound, individual IC 50-values for a given compound varied by not more than a factor of 2.6. Our data demonstrate that the assay reliably ranked compounds with strong, weak, and no/marginal affinity for the AR with high accuracy. It avoids the manipulation and use of animals, as a recombinant protein is used and thus contributes to the 3R concept. On the whole, this assay is a promising candidate for further validation.