Query Ligand Research Articles

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

Carvacrol (CV) is an organic compound found in the essential oils of many aromatic herbs. It is nearly unfeasible to analyze all the current human proteins for a query ligand using in vitro and in vivo methods. This study aimed to clarify whether CV possesses an anti-diabetic feature via Docking-based inverse docking and molecular dynamic (MD) simulation and in vitro characterization against a set of novel human protein targets. Herein, the best poses of CV docking simulations according to binding energy ranged from −7.9 to −3.5 (kcal/mol). After pathway analysis of the protein list through GeneMANIA and WebGestalt, eight interacting proteins (DPP4, FBP1, GCK, HSD11β1, INSR, PYGL, PPARA, and PPARG) with CV were determined, and these proteins exhibited stable structures during the MD process with CV. In vitro application, statistically significant results were achieved only in combined doses with CV or metformin. Considering all these findings, PPARG and INSR, among these target proteins of CV, are FDA-approved targets for treating diabetes. Therefore, CV may be on its way to becoming a promising therapeutic compound for treating Diabetes Mellitus (DM). Our outcomes expose formerly unexplored potential target human proteins, whose association with diabetic disorders might guide new potential treatments for DM.

Pharmacological inhibition of PDGF-C/neuropilin-1 interaction: A novel strategy to reduce melanoma metastatic potential

Activation of neuropilin-1 (NRP-1) by platelet derived growth factor (PDGF)-C sustains melanoma invasiveness. Therefore, in the search of novel agents capable of reducing melanoma spreading, PDGF-C/NRP-1 interaction was investigated as a potential druggable target. Since the PDGF-C region involved in NRP-1 binding is not yet known, based on the sequence and structural homology between PDGF-C and vascular endothelial growth factor-A (VEGF-A), we hypothesized that the NRP-1 b1 domain region involved in the interaction with VEGF-A might also be required for PDGF-C binding. Hence, this region was selected from the protein crystal structure and used as target in the molecular docking procedure. In the following virtual screening, compounds from a DrugBank database were used as query ligands to identify agents potentially capable of disrupting NRP-1/PDGF-C interaction. Among the top 45 candidates with the highest affinity, five drugs were selected based on the safety profile, lack of hormonal effects, and current availability in the market: the antipsychotic pimozide, antidiabetic gliclazide, antiallergic cromolyn sodium, anticancer tyrosine kinase inhibitor entrectinib, and antihistamine azelastine. Analysis of drug influence on PDGF-C in vitro binding to NRP-1 and PDGF-C induced migration of human melanoma cells expressing NRP-1, indicated gliclazide and entrectinib as the most specific agents that were active at clinically achievable and non-toxic concentrations. Both drugs also reverted PDGF-C ability to stimulate extracellular matrix invasion by melanoma cells resistant to BRAF inhibitors. The inhibitory effect on tumor cell motility involved a decrease of p130Cas phosphorylation, a signal transduction pathway activated by PDGF-C-mediated stimulation of NRP-1.

Potential of natural antioxidant compound in Cymbopogon nardus as anti-cancer drug via HSP-70 inhibitor

Citronella grass (Cymbopogon nardus) is a plant containing many metabolite compounds which prevent and treat various diseases, one of which is cancer. Antioxidant compounds found in citronella have been shown to improve the immune system by increasing cytokines. The activity of changing homeostasis generates free radicals. Free radicals causing protein damage so that Heat Shock Protein-70 (HSP70) is overexpressed. HSP70 has a role as a chaperon. Mutations in the anti-apoptotic protein HSP70 are one of the causes of cancer. This current research aims to determine the potential of compounds present in the citronella plant stem as anti-cancer through inhibition of HSP-70. The method was a bioinformatics approach, namely the in-silico method which provided a simulation of binding protein ligands to HSP-70 as inhibitor mechanism. The results of this study indicated that there was a potential for citronella compounds, namely spathulenol binding to HSP-70. Spathulenol compounds interact with Hsp70 via the positions Thr204, Gly12, Gly203, Thr14, Lys71, Asp10, Val369, Asp199, Val337, Gly338, Asp366, Gly339, Pro365, Glys201, & Glys202 with Van der Waals bonds and hydrogen bonds on Thr13. In the complex, there was one unfavorable bond formed on the O atom of the query ligand. From the results above, it can be concluded that the Spathulenol compound is predicted to act as an inhibitor of Hsp70 protein activity because it inhibits the binding site of the native ligand on Hsp70. The stability of the binding interaction produced by Spathulenol allows a response to Hsp70 inhibitor activity. By inhibiting the activity of Hsp70 inhibitors, it is possible to inhibit the formation and proliferation of cancer cells

ES-Screen: A Novel Electrostatics-Driven Method for Drug Discovery Virtual Screening

Electrostatic interactions drive biomolecular interactions and associations. Computational modeling of electrostatics in biomolecular systems, such as protein-ligand, protein-protein, and protein-DNA, has provided atomistic insights into the binding process. In drug discovery, finding biologically plausible ligand-protein target interactions is challenging as current virtual screening and adjuvant techniques such as docking methods do not provide optimal treatment of electrostatic interactions. This study describes a novel electrostatics-driven virtual screening method called 'ES-Screen' that performs well across diverse protein target systems. ES-Screen provides a unique treatment of electrostatic interaction energies independent of total electrostatic free energy, typically employed by current software. Importantly, ES-Screen uses initial ligand pose input obtained from a receptor-based pharmacophore, thus independent of molecular docking. ES-Screen integrates individual polar and nonpolar replacement energies, which are the energy costs of replacing the cognate ligand for a target with a query ligand from the screening. This uniquely optimizes thermodynamic stability in electrostatic and nonpolar interactions relative to an experimentally determined stable binding state. ES-Screen also integrates chemometrics through shape and other physicochemical properties to prioritize query ligands with the greatest physicochemical similarities to the cognate ligand. The applicability of ES-Screen is demonstrated with in vitro experiments by identifying novel targets for many drugs. The present version includes a combination of many other descriptor components that, in a future version, will be purely based on electrostatics. Therefore, ES-Screen is a first-in-class unique electrostatics-driven virtual screening method with a unique implementation of replacement electrostatic interaction energies with broad applicability in drug discovery.

Challenging Reverse Screening: A Benchmark Study for Comprehensive Evaluation.

As an efficient way of computational target prediction, reverse docking can find not only potential targets but also binding modes for a query ligand. Though the number of available docking tools keeps expanding, there is still not a comprehensive evaluation study which can uncover the advantages and limitations of these strategies in the research field of computational target-fishing. In this study, we propose a brand-new evaluation dataset tailor-made for reverse docking, which is composed of a true positive set (the core set) and two negative sets (the similar decoy set and the dissimilar decoy set). The proposed evaluation dataset can assess the prediction performance of docking tools as various values affected by varying degrees of inter-target ranking bias. The performance of four classical docking programs (AutoDock, AutoDock Vina, Glide and GOLD) was evaluated utilizing our dataset, and a biased prediction performance was observed regarding binding site properties. The results demonstrated that Glide (SP) and Glide(XP) had the best capacity to find true targets whether there was inter-target ranking bias or not.

Incorporating structural similarity into a scoring function to enhance the prediction of binding affinities

In this study, we developed a novel algorithm to improve the screening performance of an arbitrary docking scoring function by recalibrating the docking score of a query compound based on its structure similarity with a set of training compounds, while the extra computational cost is neglectable. Two popular docking methods, Glide and AutoDock Vina were adopted as the original scoring functions to be processed with our new algorithm and similar improvement performance was achieved. Predicted binding affinities were compared against experimental data from ChEMBL and DUD-E databases. 11 representative drug receptors from diverse drug target categories were applied to evaluate the hybrid scoring function. The effects of four different fingerprints (FP2, FP3, FP4, and MACCS) and the four different compound similarity effect (CSE) functions were explored. Encouragingly, the screening performance was significantly improved for all 11 drug targets especially when CSE = S4 (S is the Tanimoto structural similarity) and FP2 fingerprint were applied. The average predictive index (PI) values increased from 0.34 to 0.66 and 0.39 to 0.71 for the Glide and AutoDock vina scoring functions, respectively. To evaluate the performance of the calibration algorithm in drug lead identification, we also imposed an upper limit on the structural similarity to mimic the real scenario of screening diverse libraries for which query ligands are general-purpose screening compounds and they are not necessarily structurally similar to reference ligands. Encouragingly, we found our hybrid scoring function still outperformed the original docking scoring function. The hybrid scoring function was further evaluated using external datasets for two systems and we found the PI values increased from 0.24 to 0.46 and 0.14 to 0.42 for A2AR and CFX systems, respectively. In a conclusion, our calibration algorithm can significantly improve the virtual screening performance in both drug lead optimization and identification phases with neglectable computational cost.

DStruBTarget: Integrating Binding Affinity with Structure Similarity for Ligand-Binding Protein Prediction.

Motivation: Identification of ligand-binding proteins is an important issue for drug development. Most of the current computational approach is developed only utilizing ligand structure similarity. However, the ligand structure similarity has failed to reflect the binding quality between the ligand and the target protein, which limited the performance of current methods. Results: The present study integrated two-dimensional (2D) and three-dimensional (3D) ligand structure similarity between query ligand and template with known ligand-protein binding affinity (BA) to identify proteins binding with the query ligand. This method is named as DStruBTarget. The performance of DStruBTarget was evaluated by 10-fold cross-validation in a dataset containing 9197 ligands and 1111 ligand-binding proteins (DBD dataset). This dataset was constructed by excluding the ligands with similar structures and the proteins with high sequence identity. The DStruBTarget achieved a hit rate of 77% in top 1 prediction, which is 4.80 and 3.00% better than the methods only using 2D structure similarity, and the method integrating 2D and 3D structure similarity (2D + 3D), respectively. An independent test of DStruBTarget was performed in a publicly available dataset constructed by SwissTargetPrediction. In this dataset, the top 1 hit rate of DStruBTarget reached 44.02%, which was better than the SwissTargetPrediction, and also outstands other methods, such as 2D, 3D, 2D + 3D, 2D integrating binding affinity (2D + BA), and 3D integrating binding affinity (3D + BA). DStruBTarget was compared to another newly published method HitPickV2 and achieved 52.17% hit rate of the top 1 prediction, which was significantly better than the result of HitpickV2 (30.43%). Finally, DStruBTarget was integrated with protein BLAST to predict the ligand-binding proteins not limited in a certain database. DStruBTarget with BLAST was tested in the DBD dataset. Its top 1 hit rate was 51.15%, which is lower than DStruBTarget without BLAST. Further comparison was on the ligands that bind to multiple numbers of proteins, which illustrated that DStruBTarget with BLAST performed better than without BLAST when the number of binding proteins of the query ligands is larger than six. Meanwhile, the prediction power of the DStruBTarget with BLAST in top 1 prediction was found to be positively correlated with the number of proteins binding with the query ligands, while the top 1 prediction power of DStruBTarget without BLAST was negatively correlated with the number of binding proteins for query ligands. Thus, DStruBTarget with BLAST is a potentially useful approach for predicting novel proteins for ligands that bind to multiple proteins.

Scaffold hopping computational approach for searching novel β-lactamase inhibitors

We present a novel computational ligand-based virtual screening approach with scaffold hopping capabilities for the identification of novel inhibitors of β-lactamases which confer bacterial resistance to β-lactam antibiotics. The structures of known β-lactamase inhibitors were used as query ligands, and a virtual in silico screening a database of 8 million drug-like compounds was performed in order to select the ligands with similar shape and charge distribution. A set of numerical descriptors was used such as chirality, eigen spectrum of matrices of interatomic distances and connectivity together with higher order moment invariants that showed their efficiency in the field of pattern recognition but have not yet been employed in drug discovery. The developed scaffold-hopping approach was applied for the discovery of analogues of four allosteric inhibitors of serine β-lactamases. After a virtual in silico screening, the effect of two selected ligands on the activity of TEM type β-lactamase was studied experimentally. New non-β-lactam inhibitors were found that showed more effective inhibition of β-lactamases compared to query ligands.

Multi-Pharmacophore Modeling of Caspase-3 Inhibitors using Crystal, Dock and Flexible Conformation Schemes.

Numerous caspase-3 drug discovery projects were found to have relied on single receptor as the template to recognize most promising small molecule candidates using docking approach. Alternatively, some researchers were contingent upon ligand-based alignment to build up an empirical relationship between ligand functional groups and caspase-3 inhibitory activity quantitatively. To connect both caspase-3 receptor details and its inhibitors chemical functionalities, this study was undertaken to develop receptor- and ligand-pharmacophore models based on different conformational schemes. A multi-pharmacophore modeling strategy is carried out based on three conformational schemes of pharmacophore hypothesis generation to screen caspase-3 inhibitors from database. The schemes include (i) flexible (conformations unrestricted or flexible during pharmacophore mapping), (ii) dock (conformations obtained using FlexX docking method) and (iii) crystal (extracted from multiple caspase-3-ligand complexes from PDB repository) conformations of query ligands. The pharmacophore models developed using these conformational schemes were then used to identify probable caspase-3 inhibitors from ZINC database. We noticed better sensitivity with good specificity measures returned by candidate pharmacophore hypotheses across each conformation type and recognized crucial pharmacophore features that enable caspase-3 binding. Pharmacophore modeling based on flexible conformational scheme indicated that the crystal structure 3KJF (AAAADH) is the best receptor structure to perform receptor-based pharmacophore screening of caspase-3 inhibitors. When multiple crystal structures were included, the hypothesis (HAAA) is more generalized. Superimposition of multiple co-crystal ligands from various caspase-3 PDB entries in crystallographic binding mode revealed similar hypothesis (HAAA). Further, FlexX-guided dock conformations of validation dataset showed that the crystal structure 1RE1 is the best-suited for dock-based pharmacophore models. Database screening using these pharmacophore hypotheses identified N'-[6-(benzimidazol-1-yl)-5-nitro-pyrimidin-4-yl]-4 methylbenzenesulfonohydrazide and 2-nitro-N'-[5-nitro-6-[N'-(p-tolylsulfonyl)hydrazino]pyrimidin-4- yl]benzohydrazide as the probable caspase-3 inhibitors. N'-[6-(benzimidazol-1-yl)-5-nitro-pyrimidin-4-yl]-4 methylbenzenesulfonohydrazide and 2-nitro-N'-[5-nitro-6-[N'-(p-tolylsulfonyl)hydrazino]pyrimidin-4-yl]benzohydrazide may be tested for caspase-3 inhibition. We believe that potential caspase-3 inhibitors can be recognized efficiently by adapting multi-pharmacophore models in database screening.

Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server

BackgroundThe identification of target molecules is important for understanding the mechanism of “target deconvolution” in phenotypic screening and “polypharmacology” of drugs. Because conventional methods of identifying targets require time and cost, in-silico target identification has been considered an alternative solution. One of the well-known in-silico methods of identifying targets involves structure activity relationships (SARs). SARs have advantages such as low computational cost and high feasibility; however, the data dependency in the SAR approach causes imbalance of active data and ambiguity of inactive data throughout targets.ResultsWe developed a ligand-based virtual screening model comprising 1121 target SAR models built using a random forest algorithm. The performance of each target model was tested by employing the ROC curve and the mean score using an internal five-fold cross validation. Moreover, recall rates for top-k targets were calculated to assess the performance of target ranking. A benchmark model using an optimized sampling method and parameters was examined via external validation set. The result shows recall rates of 67.6% and 73.9% for top-11 (1% of the total targets) and top-33, respectively. We provide a website for users to search the top-k targets for query ligands available publicly at http://rfqsar.kaist.ac.kr.ConclusionsThe target models that we built can be used for both predicting the activity of ligands toward each target and ranking candidate targets for a query ligand using a unified scoring scheme. The scores are additionally fitted to the probability so that users can estimate how likely a ligand–target interaction is active. The user interface of our web site is user friendly and intuitive, offering useful information and cross references.

Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

The growing number of protein-ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein-ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein-ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein-ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein-ligand complex structures available to improve predictions on binding.

Ligand-based virtual screening under partial shape constraints.

Ligand-based virtual screening has proven to be a viable technology during the search for new lead structures in drug discovery. Despite the rapidly increasing number of published methods, meaningful shape matching as well as ligand and target flexibility still remain open challenges. In this work, we analyze the influence of knowledge-based sterical constraints on the performance of the recently published ligand-based virtual screening method mRAISE. We introduce the concept of partial shape matching enabling a more differentiated view on chemical structure. The new method is integrated into the LBVS tool mRAISE providing multiple options for such constraints. The applied constraints can either be derived automatically from a protein-ligand complex structure or by manual selection of ligand atoms. In this way, the descriptor directly encodes the fit of a ligand into the binding site. Furthermore, the conservation of close contacts between the binding site surface and the query ligand can be enforced. We validated our new method on the DUD and DUD-E datasets. Although the statistical performance remains on the same level, detailed analysis reveal that for certain and especially very flexible targets a significant improvement can be achieved. This is further highlighted looking at the quality of calculated molecular alignments using the recently introduced mRAISE dataset. The new partial shape constraints improved the overall quality of molecular alignments especially for difficult targets with highly flexible or different sized molecules. The software tool mRAISE is freely available on Linux operating systems for evaluation purposes and academic use (see http://www.zbh.uni-hamburg.de/raise ).

DUSR (Distributed Ultrafast Shape Recognition): a Hadoop Based Tool to Identify Similar Shaped Ligand Molecules

Abstract: Background: Identifying potential drug candidates through Ligand-based virtual screening is often associated with processing of huge amount of data and hence is a computational intensive task. Ultrafast Shape Recognition (USR) algorithm has been reported as a faster alternative for molecular shape comparison which maps the chemical structure of query ligand into its shape moment vector to find novel chemical scaffolds in chemical compound libraries. The USR algorithm however was devoid of the ability to discriminate ligand molecules according to their pharmacokinetic features. Methods: To overcome this discrepancy, a modification in the existing USR algorithm called DUSR (Distributed Ultrafast Shape Recognition) was carried out where chemical compounds were screened on the basis of their drug-likeliness properties prior to the molecular shape comparison followed by shape complementarity momentum measure. The DUSR due to its Hadoop implementation acts as a faster approach than the existing standalone tools, utilizing the MapReduce algorithm supporting the high throughput screening of million conformers in a much reduced time span. We further demonstrated the utility of DUSR on dataset of 2 million ligand molecules by running shape comparison based searching job on standalone and multisystem Hadoop platforms. Results: The result suggested that DUSR completed its job in 1h 15 m 41s, 0 h 23 m 41s and 0h13m 22s sec for 2038924 molecules on Hadoop standalone mode, 3-nodes cluster & 5-nodes cluster of distributed commodity hardware respectively. Key words: DUSR (Distributed Ultrafast Shape Recognition), High throughput Screening, Hadoop, MapReduce, Virtual Screening.

Boltzmann Sampling by Degenerate Optical Parametric Oscillator Network for Structure-Based Virtual Screening

A structure-based lead optimization procedure is an essential step to finding appropriate ligand molecules binding to a target protein structure in order to identify drug candidates. This procedure takes a known structure of a protein-ligand complex as input, and structurally similar compounds with the query ligand are designed in consideration with all possible combinations of atomic species. This task is, however, computationally hard since such combinatorial optimization problems belong to the non-deterministic nonpolynomial-time hard (NP-hard) class. In this paper, we propose the structure-based lead generation and optimization procedures by a degenerate optical parametric oscillator (DOPO) network. Results of numerical simulation demonstrate that the DOPO network efficiently identifies a set of appropriate ligand molecules according to the Boltzmann sampling law.

Prospective evaluation of shape similarity based pose prediction method in D3R Grand Challenge 2015.

Evaluation of ligand three-dimensional (3D) shape similarity is one of the commonly used approaches to identify ligands similar to one or more known active compounds from a library of small molecules. Apart from using ligand shape similarity as a virtual screening tool, its role in pose prediction and pose scoring has also been reported. We have recently developed a method that utilizes ligand 3D shape similarity with known crystallographic ligands to predict binding poses of query ligands. Here, we report the prospective evaluation of our pose prediction method through the participation in drug design data resource (D3R) Grand Challenge 2015. Our pose prediction method was used to predict binding poses of heat shock protein 90 (HSP90) and mitogen activated protein kinase kinase kinase kinase (MAP4K4) ligands and it was able to predict the pose within 2Å root mean square deviation (RMSD) either as the top pose or among the best of five poses in a majority of cases. Specifically for HSP90 protein, a median RMSD of 0.73 and 0.68Å was obtained for the top and the best of five predictions respectively. For MAP4K4 target, although the median RMSD for our top prediction was only 2.87Å but the median RMSD of 1.67Å for the best of five predictions was well within the limit for successful prediction. Furthermore, the performance of our pose prediction method for HSP90 and MAP4K4 ligands was always among the top five groups. Particularly, for MAP4K4 protein our pose prediction method was ranked number one both in terms of mean and median RMSD when the best of five predictions were considered. Overall, our D3R Grand Challenge 2015 results demonstrated that ligand 3D shape similarity with the crystal ligand is sufficient to predict binding poses of new ligands with acceptable accuracy.

Facing the Challenges of Structure-Based Target Prediction by Inverse Virtual Screening

Computational target prediction for bioactive compounds is a promising field in assessing off-target effects. Structure-based methods not only predict off-targets, but, simultaneously, binding modes, which are essential for understanding the mode of action and rationally designing selective compounds. Here, we highlight the current open challenges of computational target prediction methods based on protein structures and show why inverse screening rather than sequential pairwise protein-ligand docking methods are needed. A new inverse screening method based on triangle descriptors is introduced: iRAISE (inverse Rapid Index-based Screening Engine). A Scoring Cascade considering the reference ligand as well as the ligand and active site coverage is applied to overcome interprotein scoring noise of common protein-ligand scoring functions. Furthermore, a statistical evaluation of a score cutoff for each individual protein pocket is used. The ranking and binding mode prediction capabilities are evaluated on different datasets and compared to inverse docking and pharmacophore-based methods. On the Astex Diverse Set, iRAISE ranks more than 35% of the targets to the first position and predicts more than 80% of the binding modes with a root-mean-square deviation (RMSD) accuracy of <2.0 Å. With a median computing time of 5 s per protein, large amounts of protein structures can be screened rapidly. On a test set with 7915 protein structures and 117 query ligands, iRAISE predicts the first true positive in a ranked list among the top eight ranks (median), i.e., among 0.28% of the targets.

Combinatorial Pharmacophore Modeling of Organic Cation Transporter 2 (OCT2) Inhibitors: Insights into Multiple Inhibitory Mechanisms

Organic cation transporter 2 (OCT2) is responsible for the entry step of many drugs in renal elimination, of which the changing activity may cause unwanted drug-drug interactions (DDIs). To develop drugs with favorable safety profile and provide instruction for rational clinical drug administration, it is of great interest to investigate the multiple mechanisms of OCT2 inhibition. In this study, we designed a combinatorial scheme to screen the optimum combination of pharmacophores from a pool of hypotheses established based on 162 OCT2 inhibitors. Among them, one single pharmacophore hypothesis represents a potential binding mode that may account for one unique inhibitory mechanism, and the obtained pharmacophore combination describes the multimechanisms of OCT2 inhibition. The final model consists of four individual pharmacophores, i.e., DHPR18, APR2, PRR5 and HHR4. Given a query ligand, it is considered as an inhibitor if it matches at least one of the hypotheses, or a noninhibitor if it fails to match any of four hypotheses. Our combinatorial pharmacophore model performs reasonably well to discriminate inhibitors and noninhibitors, yielding an overall accuracy around 0.70 for a test set containing 81 OCT2 inhibitors and 218 noninhibitors. Intriguingly, we found that the number of matched hypotheses was positively correlated with inhibition rate, which coincides with the pharmacophore modeling result of P-gp substrate binding. Further analysis suggested that the hypothesis PRR5 was responsible for competitive inhibition of OCT2, and other hypotheses were important for interaction between the inhibitor and OCT2. In light of the results, a hypothetical model for inhibiting transporting mediated by OCT2 was proposed.

Prediction of Cytochrome P450 Xenobiotic Metabolism: Tethered Docking and Reactivity Derived from Ligand Molecular Orbital Analysis

Metabolism of xenobiotic and endogenous compounds is frequently complex, not completely elucidated, and therefore often ambiguous. The prediction of sites of metabolism (SoM) can be particularly helpful as a first step toward the identification of metabolites, a process especially relevant to drug discovery. This paper describes a reactivity approach for predicting SoM whereby reactivity is derived directly from the ground state ligand molecular orbital analysis, calculated using Density Functional Theory, using a novel implementation of the average local ionization energy. Thus each potential SoM is sampled in the context of the whole ligand, in contrast to other popular approaches where activation energies are calculated for a predefined database of molecular fragments and assigned to matching moieties in a query ligand. In addition, one of the first descriptions of molecular dynamics of cytochrome P450 (CYP) isoforms 3A4, 2D6, and 2C9 in their Compound I state is reported, and, from the representative protein structures obtained, an analysis and evaluation of various docking approaches using GOLD is performed. In particular, a covalent docking approach is described coupled with the modeling of important electrostatic interactions between CYP and ligand using spherical constraints. Combining the docking and reactivity results, obtained using standard functionality from common docking and quantum chemical applications, enables a SoM to be identified in the top 2 predictions for 75%, 80%, and 78% of the data sets for 3A4, 2D6, and 2C9, respectively, results that are accessible and competitive with other recently published prediction tools.

Database of bioactive ring systems with calculated properties and its use in bioisosteric design and scaffold hopping

A system for identification of bioisosteric scaffolds is presented. The system uses a database of over 7000 scaffolds extracted from bioactive molecules. Scaffolds in the database are characterized by their size, shape, pharmacophore features and several ADME descriptors. Also properties characterizing electron-donating or -accepting power at connection vectors are considered. All these features are used as search criteria to find scaffolds with the most similar properties to the query. To guarantee fast processing the search is performed using topological descriptors only, but the system may be used to find optimal replacements of scaffolds also directly in the protein binding site. In this case a set of 3D conformations for the best 2D hits is generated and analogs optimally fitting the binding pocket are identified by overlap with the query ligand and by optimizing interactions with the protein. This tool is used at Novartis as an idea generator for identification of novel non-classical bioisosteric analogs in the drug discovery process.

Evaluation of drug–human serum albumin binding interactions with support vector machine aided online automated docking

Human serum albumin (HSA), the most abundant plasma protein is well known for its extraordinary binding capacity for both endogenous and exogenous substances, including a wide range of drugs. Interaction with the two principal binding sites of HSA in subdomain IIA (site 1) and in subdomain IIIA (site 2) controls the free, active concentration of a drug, provides a reservoir for a long duration of action and ultimately affects the ADME (absorption, distribution, metabolism, and excretion) profile. Due to the continuous demand to investigate HSA binding properties of novel drugs, drug candidates and drug-like compounds, a support vector machine (SVM) model was developed that efficiently predicts albumin binding. Our SVM model was integrated to a free, web-based prediction platform (http://albumin.althotas.com). Automated molecular docking calculations for prediction of complex geometry are also integrated into the web service. The platform enables the users (i) to predict if albumin binds the query ligand, (ii) to determine the probable ligand binding site (site 1 or site 2), (iii) to select the albumin X-ray structure which is complexed with the most similar ligand and (iv) to calculate complex geometry using molecular docking calculations. Our SVM model and the potential offered by the combined use of in silico calculation methods and experimental binding data is illustrated.