Exploring the dynamics of the ABCB1 membrane transporter P-glycoprotein in the presence of ATP and active/non-active compounds through molecular dynamics simulations.

P-glycoprotein (Pgp) is a member of the ATP-binding cassette family of transporters that confers multidrug resistance to cancer cells and is actively involved in the pharmacokinetics and toxicokinetics of a big variety of drugs. Extensive studies have provided insights into the binding of many compounds, but the precise mechanism of translocation across the membrane remains unknown; in this context, the major challenge has been to understand the basis for its polyspecificity. In this study, molecular dynamics (MD) simulations of human P-gp (hP-gp) in an explicit membrane-and-water environment were performed to investigate the dynamic behavior of the transporter in the presence of different compounds (active and inactive) in the binding pocket and ATP molecules within the nucleotide binding domains (NBDs). The complexes studied involve four compounds: cyclosporin A (CSA), amiodarone (AMI), pamidronate (APD), and valproic acid (VPA). While CSA and AMI are known to interact with P-gp, APD and VPA do not. The results highlighted how the presence of ATP notably contributed to increased flexibility of key residues in NBD1 of active systems, indicating potential conformational changes activating the translocation mechanism. MD simulations reveal how these domains adapt and respond to the presence of different substrates, as well as the influence of ATP binding on their flexibility. Furthermore, distinctive behavior was observed in the presence of active and inactive compounds, particularly in the arrangement of ATP between NBDs, supporting the proposed nucleotide sandwich dimer mechanism for ATP binding. This study provides comprehensive insights into P-gp behavior with various ligands and ATP, offering implications for drug development, toxicity assessment and demonstrating the validity of the results derived from the MD simulations.

Recent Advances on P-Glycoprotein (ABCB1) Transporter Modelling with In Silico Methods.

ABC transporters play a critical role in both drug bioavailability and toxicity, and with the discovery of the P-glycoprotein (P-gp), this became even more evident, as it plays an important role in preventing intracellular accumulation of toxic compounds. Over the past 30 years, intensive studies have been conducted to find new therapeutic molecules to reverse the phenomenon of multidrug resistance (MDR) ), that research has found is often associated with overexpression of P-gp, the most extensively studied drug efflux transporter; in MDR, therapeutic drugs are prevented from reaching their targets due to active efflux from the cell. The development of P-gp inhibitors is recognized as a good way to reverse this type of MDR, which has been the subject of extensive studies over the past few decades. Despite the progress made, no effective P-gp inhibitors to reverse multidrug resistance are yet on the market, mainly because of their toxic effects. Computational studies can accelerate this process, and in silico models such as QSAR models that predict the activity of compounds associated with P-gp (or analogous transporters) are of great value in the early stages of drug development, along with molecular modelling methods, which provide a way to explain how these molecules interact with the ABC transporter. This review highlights recent advances in computational P-gp research, spanning the last five years to 2022. Particular attention is given to the use of machine-learning approaches, drug-transporter interactions, and recent discoveries of potential P-gp inhibitors that could act as modulators of multidrug resistance.

Exploration of structural requirements for azole chemicals towards human aromatase CYP19A1 activity: Classification modeling, structure-activity relationships and read-across study.

Human aromatase, also called CYP19A1, plays a major role in the conversion of androgens into estrogens. Inhibition of aromatase is an important target for estrogen receptor (ER)-responsive breast cancer therapy. Use of azole compounds as aromatase inhibitors is widespread despite their low selectivity. A toxicological evaluation of commonly used azole-based drugs and agrochemicals with respect to CYP19A1 is currently requested by the European Union- Registration, Evaluation, Authorization and Restriction of Chemicals (EU-REACH) regulations due to their potential as endocrine disruptors. In this connection, identification of structural alerts (SAs) is an effective strategy for the toxicological assessment and safe drug design. The present study describes the identification of SAs of azole-based chemicals as guiding experts to predict the aromatase activity. Total 21 SAs associated with aromatase activity were extracted from dataset of 326 azole-based drugs/chemicals obtained from Tox21 library. A cross-validated classification model having high accuracy (error rate 5%) was proposed which can precisely classify azole chemicals into active/inactive toward aromatase. In addition, mechanistic details and toxicological properties (agonism/antagonism) of azoles with respect to aromatase were explored by comparing active and inactive chemicals using structure-activity relationships (SAR). Lastly, few structural alerts were applied to form chemical categories for read-across applications.

Structure-Function Relationships in the Human P-Glycoprotein (ABCB1): Insights from Molecular Dynamics Simulations.

P-Glycoprotein (P-gp) is a transmembrane protein belonging to the ATP binding cassette superfamily of transporters, and it is a xenobiotic efflux pump that limits intracellular drug accumulation by pumping compounds out of cells. P-gp contributes to a reduction in toxicity, and has broad substrate specificity. It is involved in the failure of many cancer and antiviral chemotherapies due to the phenomenon of multidrug resistance (MDR), in which the membrane transporter removes chemotherapeutic drugs from target cells. Understanding the details of the ligand-P-gp interaction is therefore critical for the development of drugs that can overcome the MDR phenomenon, for the early identification of P-gp substrates that will help us to obtain a more effective prediction of toxicity, and for the subsequent outdesign of substrate properties if needed. In this work, a series of molecular dynamics (MD) simulations of human P-gp (hP-gp) in an explicit membrane-and-water environment were performed to investigate the effects of binding different compounds on the conformational dynamics of P-gp. The results revealed significant differences in the behaviour of P-gp in the presence of active and non-active compounds within the binding pocket, as different patterns of movement were identified that could be correlated with conformational changes leading to the activation of the translocation mechanism. The predicted ligand-P-gp interactions are in good agreement with the available experimental data, as well as the estimation of the binding-free energies of the studied complexes, demonstrating the validity of the results derived from the MD simulations.

Homology Modeling of the Human P-glycoprotein (ABCB1) and Insights into Ligand Binding through Molecular Docking Studies.

The ABCB1 transporter also known as P-glycoprotein (P-gp) is a transmembrane protein belonging to the ATP binding cassette super-family of transporters; it is a xenobiotic efflux pump that limits intracellular drug accumulation by pumping the compounds out of cells. P-gp contributes to a decrease of toxicity and possesses broad substrate specificity. It is involved in the failure of numerous anticancer and antiviral chemotherapies due to the multidrug resistance (MDR) phenomenon, where it removes the chemotherapeutics out of the targeted cells. Understanding the details of the ligand-P-gp interaction is therefore crucial for the development of drugs that might overcome the MRD phenomenon and for obtaining a more effective prediction of the toxicity of certain compounds. In this work, an in silico modeling was performed using homology modeling and molecular docking methods with the aim of better understanding the ligand-P-gp interactions. Based on different mouse P-gp structural templates from the PDB repository, a 3D model of the human P-gp (hP-gp) was constructed by means of protein homology modeling. The homology model was then used to perform molecular docking calculations on a set of thirteen compounds, including some well-known compounds that interact with P-gp as substrates, inhibitors, or both. The sum of ranking differences (SRD) was employed for the comparison of the different scoring functions used in the docking calculations. A consensus-ranking scheme was employed for the selection of the top-ranked pose for each docked ligand. The docking results showed that a high number of π interactions, mainly π-sigma, π-alkyl, and π-π type of interactions, together with the simultaneous presence of hydrogen bond interactions contribute to the stability of the ligand-protein complex in the binding site. It was also observed that some interacting residues in hP-gp are the same when compared to those observed in a co-crystallized ligand (PBDE-100) with mouse P-gp (PDB ID: 4XWK). Our in silico approach is consistent with available experimental results regarding P-gp efflux transport assay; therefore it could be useful in the prediction of the role of new compounds in systemic toxicity.

Structural Analysis and Dynamic Processes of the Transmembrane Segment Inside Different Micellar Environments-Implications for the TM4 Fragment of the Bilitranslocase Protein.

The transmembrane (TM) proteins are gateways for molecular transport across the cell membrane that are often selected as potential targets for drug design. The bilitranslocase (BTL) protein facilitates the uptake of various anions, such as bilirubin, from the blood into the liver cells. As previously established, there are four hydrophobic transmembrane segments (TM1-TM4), which constitute the structure of the transmembrane channel of the BTL protein. In our previous studies, the 3D high-resolution structure of the TM2 and TM3 transmembrane fragments of the BTL in sodium dodecyl sulfate (SDS) micellar media were solved using Nuclear Magnetic Resonance (NMR) spectroscopy and molecular dynamics simulations (MD). The high-resolution 3D structure of the fourth transmembrane region (TM4) of the BTL was evaluated using NMR spectroscopy in two different micellar media, anionic SDS and zwitterionic DPC (dodecylphosphocholine). The presented experimental data revealed the existence of an α -helical conformation in the central part of the TM4 in both micellar media. In the case of SDS surfactant, the α -helical conformation is observed for the Pro258-Asn269 region. The use of the zwitterionic DPC micelle leads to the formation of an amphipathic α -helix, which is characterized by the extension of the central α -helix in the TM4 fragment to Phe257-Thr271. The complex character of the dynamic processes in the TM4 peptide within both surfactants was analyzed based on the relaxation data acquired on 15 N and 31 P isotopes. Contrary to previously published and present observations in the SDS micelle, the zwitterionic DPC environment leads to intensive low-frequency molecular dynamic processes in the TM4 fragment.

Development of N-(Functionalized benzoyl)-homocycloleucyl-glycinonitriles as Potent Cathepsin K Inhibitors.

Cathepsin K is a major drug target for osteoporosis and related-bone disorders. Using a combination of virtual combinatorial chemistry, QSAR modeling, and molecular docking studies, a series of cathepsin K inhibitors based on N-(functionalized benzoyl)-homocycloleucyl-glycinonitrile scaffold was developed. In order to avoid previous problems of cathepsin K inhibitors associated with lysosomotropism of compounds with basic character that resulted in off-target effects, a weakly- to nonbasic moiety was incorporated into the P3 position. Compounds 5, 6, and 9 were highly selective for cathepsin K when compared with cathepsins L and S, with the Ki values in the 10-30 nM range. The kinetic studies revealed that the new compounds exhibited reversible tight binding to cathepsin K, while the X-ray structural studies showed covalent and noncovalent binding between the nitrile group and the catalytic cysteine (Cys25) site.

Structural elucidation of transmembrane transporter protein bilitranslocase: conformational analysis of the second transmembrane region TM2 by molecular dynamics and NMR spectroscopy.

Membrane proteins represent about a third of the gene products in most organisms, as revealed by the genome sequencing projects. They account for up to two thirds of known drugable targets, which emphasizes their critical pharmaceutical importance. Here we present a study on bilitranslocase (BTL) (TCDB 2.A.65), a membrane protein primarily involved in the transport of bilirubin from blood to liver cells. Bilitranslocase has also been identified as a potential membrane transporter for cellular uptake of several drugs and due to its implication in drug uptake, it is extremely important to advance the knowledge about its 3D structure. However, at present, only a limited knowledge is available beyond the primary structure of BTL. It has been recently confirmed experimentally that one of the four computationally predicted transmembrane segments of bilitranslocase, TM3, has a helical structure with hydrophilic amino acid residues oriented towards one side, which is typical for transmembrane domains of membrane proteins. In this study we confirmed by the use of multidimensional NMR spectroscopy that the second transmembrane segment, TM2, also appears in a form of α-helix. The stability of this polypeptide chain was verified by molecular dynamics (MD) simulation in dipalmitoyl phosphatidyl choline (DPPC) and in sodium dodecyl sulfate (SDS) micelles. The two α-helices, TM2 corroborated in this study, and TM3 confirmed in our previous investigation, provide reasonable building blocks of a potential transmembrane channel for transport of bilirubin and small hydrophilic molecules, including pharmaceutically active compounds.

In silico assessment of adverse effects of a large set of 6-fluoroquinolones obtained from a study of tuberculosis chemotherapy.

Among the different chemotherapeutic classes available today, the 6-fluoroquinolone (6-FQ) antibacterials are still one of the most effective cures in fighting tuberculosis (TB). Nowadays, the development of novel 6-FQs for treatment of TB mainly depends on understanding how the structural modifications of the main quinolone scaffold at specific positions affect the anti-mycobacterial activity. Alongside the structure-activity relationship (SAR) studies of the 6-FQ antibacterials, which can be considered as a golden rule in the development of novel active antitubercular 6-FQs, the structure side effects relationship (SSER) of these drugs must be also taken into account. In the present study we focus on a proficient implementation of the existing knowledge-based expert systems for design of novel 6-FQ antibacterials with possible enhanced biological activity against Mycobaterium tuberculosis as well as lower toxicity. Following the SAR in silico studies of the quinolone antibacterials against M. tuberculosis performed in our laboratory, a large set of 6-FQs was selected. Several new 6-FQ derivatives were proposed as drug candidates for further research and development. The 6- FQs identified as potentially effective against M. tuberculosis were subjected to an additional SSER study for prediction of their toxicological profile. The assessment of structurally-driven adverse effects which might hamper the potential of new drug candidates is mandatory for an effective drug design. We applied publicly available knowledge-based (expert) systems and Quantitative Structure-Activity Relationship (QSAR) models in order to prepare a priority list of active compounds. A preferred order of drug candidates was obtained, so that the less harmful candidates were identified for further testing. TOXTREE expert system as well as some QSAR models developed in the framework of EC funded project CAESAR were used to assess toxicity. CAESAR models were developed according to the OECD principles for the validation of QSAR and they turn to be appropriate tools for in silico tests regarding five different toxicity endpoints. Those endpoints with high relevance for REACH are: bioconcentration factor, skin sensitization, carcinogenicity, mutagenicity, and developmental toxicity. We used the above-mentioned freely available models to select a set of less harmful active 6-FQs as candidates for clinical studies.

Structural analysis of a peptide fragment of transmembrane transporter protein bilitranslocase.

Using a combination of genomic and post-genomic approaches is rapidly altering the number of identified human influx carriers. A transmembrane protein bilitranslocase (TCDB 2.A.65) has long attracted attention because of its function as an organic anion carrier. It has also been identified as a potential membrane transporter for cellular uptake of several drugs and due to its implication in drug uptake, it is extremely important to advance the knowledge about its structure. However, at present, only the primary structure of bilitranslocase is known. In our work, transmembrane subunits of bilitranslocase were predicted by a previously developed chemometrics model and the stability of these polypeptide chains were studied by molecular dynamics (MD) simulation. Furthermore, sodium dodecyl sulfate (SDS) micelles were used as a model of cell membrane and herein we present a high-resolution 3D structure of an 18 amino acid residues long peptide corresponding to the third transmembrane part of bilitranslocase obtained by use of multidimensional NMR spectroscopy. It has been experimentally confirmed that one of the transmembrane segments of bilitranslocase has alpha helical structure with hydrophilic amino acid residues oriented towards one side, thus capable of forming a channel in the membrane.

Integration of QSAR and SAR methods for the mechanistic interpretation of predictive models for carcinogenicity.

The knowledge-based Toxtree expert system (SAR approach) was integrated with the statistically based counter propagation artificial neural network (CP ANN) model (QSAR approach) to contribute to a better mechanistic understanding of a carcinogenicity model for non-congeneric chemicals using Dragon descriptors and carcinogenic potency for rats as a response. The transparency of the CP ANN algorithm was demonstrated using intrinsic mapping technique specifically Kohonen maps. Chemical structures were represented by Dragon descriptors that express the structural and electronic features of molecules such as their shape and electronic surrounding related to reactivity of molecules. It was illustrated how the descriptors are correlated with particular structural alerts (SAs) for carcinogenicity with recognized mechanistic link to carcinogenic activity. Moreover, the Kohonen mapping technique enables one to examine the separation of carcinogens and non-carcinogens (for rats) within a family of chemicals with a particular SA for carcinogenicity. The mechanistic interpretation of models is important for the evaluation of safety of chemicals.

Evaluating the applicability domain in the case of classification predictive models for carcinogenicity based on the counter propagation artificial neural network.

The applicability domain (AD) of models developed for regulatory use has attached great attention recently. The AD of quantitative structure-activity relationship (QSAR) models is the response and chemical structure space in which the model makes predictions with a given reliability. The evaluation of AD of regressions QSAR models for congeneric sets of chemicals can be find in many papers and books while the issue about metrics for the evaluation of an AD for the non-linear models (like neural networks) for the diverse set of chemicals represents the new field of investigations in QSAR studies. The scientific society is standing before the challenge to find out reliable way for the evaluation of an AD of non linear models. The new metrics for the evaluation of the AD of the counter propagation artificial neural network (CP ANN) models are discussed in the article: the Euclidean distances between an object (molecule) and the corresponding excited neuron of the neural network and between an object (molecule) and the representative object (vector of average values of descriptors). The investigation of the training and test sets chemicals coverage in the descriptors space was made with the respect to false predicted chemicals. The leverage approach was used to compare non linear (CP ANN) models with linear ones.

Some findings relevant to the mechanistic interpretation in the case of predictive models for carcinogenicity based on the counter propagation artificial neural network.

The goal of the study was to contribute to a better mechanistic understanding of so-called "general" QSAR models for non-congeneric chemicals based on the counter propagation artificial neural network (CP ANN). Possible mechanisms of action was proofed using the Toxtree expert system based on structural alerts (SAs) for carcinogenicity. We have illustrated how statistically selected MDL descriptors, which refer to topological characteristics as well as to polarizability and charge distribution related to reactivity, are correlated with particular chemical classes (containing carcinogenic SA) with the recognized mechanistic link to the carcinogenic activity and consequently with the carcinogenic potency. Mechanistic insight in CP ANN models was demonstrated using an inherent mapping technique (i.e. Kohonen maps).

New public QSAR model for carcinogenicity.

BACKGROUND: One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration. RESULTS: Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B. CONCLUSION: Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.

QSAR models for reproductive toxicity and endocrine disruption activity.

Reproductive toxicity is an important regulatory endpoint, which is required in registration procedures of chemicals used for different purposes (for example pesticides). The in vivo tests are expensive, time consuming and require large numbers of animals, which must be sacrificed. Therefore an effort is ongoing to develop alternative In vitro and in silico methods to evaluate reproductive toxicity. In this review we describe some modeling approaches. In the first example we describe the CAESAR model for prediction of reproductive toxicity; the second example shows a classification model for endocrine disruption potential based on counter propagation artificial neural networks; the third example shows a modeling of relative binding affinity to rat estrogen receptor, and the fourth one shows a receptor dependent modeling experiment.

Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses.

The new European chemicals regulation Registration, Evaluation, Authorization and Restriction of Chemicals entered into force in June 2007 and accelerated the development of quantitative structure-activity relationship (QSAR) models for a variety of endpoints, including carcinogenicity. Here, we would like to present quantitative (continuous) and qualitative (categorical) models for non-congeneric chemicals for prediction of carcinogenic potency. A dataset of 805 substances was obtained after a preliminary screening of findings of rodent carcinogenicity for 1,481 chemicals accessible via Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network originated from the Lois Gold Carcinogenic Potency Database (CPDB). Twenty seven two-dimensional MDL descriptors were selected using Kohonen mapping and principal component analysis. The counter propagation artificial neural network (CP ANN) technique was applied. Quantitative models were developed exploring the relationship between the experimental and predicted carcinogenic potency expressed as a tumorgenic dose TD(50) for rats. The obtained models showed low prediction power with correlation coefficient less than 0.5 for the test set. In the next step, qualitative models were developed. We found that the qualitative models exhibit good accuracy for the training set (92%). The model demonstrated good predicted performance for the test set. It was obtained accuracy (68%), sensitivity (73%), and specificity (63%). We believe that CP ANN method is a good in silico approach for modeling and predicting rodent carcinogenicity for non-congeneric chemicals and may find application for other toxicological endpoints.

Multivariate data analysis of erythrocyte membrane phospholipid Fatty Acid profiles in the discrimination between normal blood tissue and various disease States.

The investigation presented here aims to compare the fatty acid composition of red blood cells of patients with different disease states and to test the hypothesis that the changes in fatty acid profiles derived from erythrocyte phospholipids might be relevant to various diseases. The study sample consisted of 342 blood donors, among them 135 with inflammatory bowel disease, 53 with uterine leiomyoma, 14 with verified absence of uterine leiomyoma, 52 with asthma, 18 with colon adenomas, and 70 blood samples without any of mentioned diseases that was used as a control group. After the isolation of erythrocytes from blood samples, total extracted lipids were separated by solid-phase extraction (SPE) into non polar lipids and polar phospholipids. After the saponification of phospholipid fraction, the esterification process followed with boron trifluoride-methanol reagent. The fatty acid methyl ester (FAME) composition of the total red blood cell phospholipid fraction was analyzed by gas chromatography (GC) with flame ionization detector (FID). Additionally two fatty aldehyde dimethyl acetals (hexadecanal and octadecanal dimethyl acetals; 16:0 DMA and 18:0 DMA) derived from erythrocyte membrane plasmalogen phospholipids were also determined. The resulting fatty acid and plasmalogen linked fatty acid composition was evaluated by the principal component analysis (PCA). We demonstrated decreased levels of omega-3 polyunsaturated fatty acids (n-3 PUFAs) in red blood cell membrane of patients with colon adenomas. Also, a large negative correlation was observed among all samples between the quantity of saturated acids and arachidonic (20:4n6) acid as well as saturated acids and adrenic (22:4n6) acid. In PCA score plot a group of female donors is distinguished mainly by the content of linoleic (18:2n6) acid; a small subgroup shows its concentration highly above the average value. At the same time, the same subgroup has both dimethyl acetals below the average concentrations. The study demonstrates feasibility of multivariate data analysis in discrimination of patients with different diseases according to fatty acid profile and suggests considerable differences in membrane fatty acid profiles in patients with various disease states.

Automatic adjustment of the relative importance of different input variables for optimization of counter-propagation artificial neural networks.

In this work we present a quantitative structure-activity relationship study with 49 peptidic molecules, inhibitors of the HIV-1 protease. The modelling was preformed using counter-propagation artificial neural networks (CPANN), an algorithm which has been proven as a valuable tool for data analysis. The initial pre-processing of the data involved auto-scaling, which gives equal importance to all the variables considered in the model. In order to enhance the influence of some of the variables that carry valuable information for improvement of the model, we introduce a novel approach for adjustment of the relative importance of different input variables. Having involved a genetic algorithm, the relative importance was adjusted during the training of the CPANN. The proposed approach is capable of finding simpler efficient models, when compared to the approach with the original, i.e. equally important input variables. A simpler model also means more robust and less subjected to the overfitting model, therefore we consider the proposed procedure as a valuable improvement of the CPANN algorithm.

Structural features of diverse ligands influencing binding affinities to estrogen alpha and estrogen beta receptors. Part II. Molecular descriptors calculated from conformation of the ligands in the complex resulting from previous docking study.

A QSAR study is reported, in which the relationship between chemical structure of a set of compounds and the binding affinity to human estrogen receptor alpha and beta (ER-alpha and ER-beta) is modelled. Counterpropagation neural networks are used to predict experimental binding affinity of a range of substances. Several compounds as estrogenic chemicals, phytoestrogens, and natural and synthetic estrogens are treated with a structure-based approach that involves the protein structure. The conformations obtained with a docking methodology are used to calculate molecular descriptors. The models are built up with the neural network training procedure, which encodes the information present in molecular descriptors and related binding affinities of the pre-selected training set of compounds. In order to reach the best possible models, a selection of the descriptors using genetic algorithm was conducted. The selection was directed by the error in the prediction of binding affinities of compounds from the test set. The final models obtained for estrogen receptor alpha and beta were tested with an external validation set and were compared with the models obtained from a receptor-independent approach reported in the accompanying paper.

Structural features of diverse ligands influencing binding affinities to estrogen alpha and estrogen beta receptors. Part I: Molecular descriptors calculated from minimal energy conformation of isolated ligands.

We report a neural network modeling approach combined with genetic algorithm for prediction of experimental binding affinity to human Estrogen Receptor alpha and beta (ER-alpha and ER-beta) of a diverse set of chemicals. The counterpropagation artificial neural network is used as a modeling method. Structural features of ligands having the strongest influence to the binding affinities were investigated. The molecular descriptors have been selected in the variable selection procedure based on the genetic algorithm (GA). The 3D descriptors of molecular structures were calculated for the minimal energy conformation of isolated ligands. All the optimized models were tested by an internal and an external set of compounds. The models served for classification and prediction of binding affinities. The optimized models were 100% correct in the classification part, where the active molecules were separated from the inactive ones. The best predictive model of active molecules was assessed with the internal test set yielding the error in prediction RMS = 0.12, while the predictions for the external test set contain some outliers, which are ascribed to the incompatibility of individual compounds concerning the structural domain of our model. The influence of the receptor on the conformation of the ligands in the ligand-protein complex is described and discussed in the accompanying paper.