Multiple conformational states in retrospective virtual screening - homology models vs. crystal structures: beta-2 adrenergic receptor case study

Background

Distinguishing active from inactive compounds is one of the crucial problems of molecular docking, especially in the context of virtual screening experiments. The randomization of poses and the natural flexibility of the protein make this discrimination even harder. Some of the recent approaches to post-docking analysis use an ensemble of receptor models to mimic this naturally occurring conformational diversity. However, the optimal number of receptor conformations is yet to be determined.

In this study, we compare the results of a retrospective screening of beta-2 adrenergic receptor ligands performed on both the ensemble of receptor conformations extracted from ten available crystal structures and an equal number of homology models. Additional analysis was also performed for homology models with up to 20 receptor conformations considered.

Results

The docking results were encoded into the Structural Interaction Fingerprints and were automatically analyzed by support vector machine. The use of homology models in such virtual screening application was proved to be superior in comparison to crystal structures. Additionally, increasing the number of receptor conformational states led to enhanced effectiveness of active vs. inactive compounds discrimination.

Conclusions

For virtual screening purposes, the use of homology models was found to be most beneficial, even in the presence of crystallographic data regarding the conformational space of the receptor. The results also showed that increasing the number of receptors considered improves the effectiveness of identifying active compounds by machine learning methods.

Graphical abstract

Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening

Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bond-based bilinear indices, are developed and both show accuracies higher than 86% for training and test sets. The stochastic model correctly indentifies nine out of eleven compounds of a set of organic chemicals obtained from our synthetic collaborators. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Both models show a good agreement between theoretical predictions and experimental results. Three compounds showed IC50 values for epimastigote elimination (AE) lower than 50 μM, while for the benznidazole the IC50 = 54.7 μM which was used as reference compound. The value of IC50 for cytotoxicity of these compounds is at least 5 times greater than their value of IC50 for AE. Finally, we can say that, the present algorithm constitutes a step forward in the search for efficient ways of discovering new antitrypanosomal compounds.

Advances in the development of SUMO specific protease (SENP) inhibitors

Sumoylation is a reversible post-translational modification that involves the covalent attachment of small ubiquitin-like modifier (SUMO) proteins to their substrate proteins. Prior to their conjugation, SUMO proteins need to be proteolytically processed from its precursor form to mature or active form. SUMO specific proteases (SENPs) are cysteine proteases that cleave the pro or inactive form of SUMO at C-terminus using its hydrolase activity to expose two glycine residues. SENPs also catalyze the de-conjugation of SUMO proteins using their isopeptidase activity, which is crucial for recycling of SUMO from substrate proteins. SENPs are important for maintaining the balance between sumoylated and unsumoylated proteins required for normal cellular physiology. Several studies reported the overexpression of SENPs in disease conditions and highlighted their role in the development of various diseases, especially cancer. In this review, we will address the current biological understanding of various SENP isoforms and their role in the pathogenesis of different cancers and other diseases. We will then discuss the advances in the development of protein-based, peptidyl and small molecule inhibitors of various SENP isoforms. Finally, we will summarize successful examples of computational screening that allowed the identification of SENP inhibitors with therapeutic potential.

Enhancing the Reliability of GPCR Models by Accounting for Flexibility of Their Pro-Containing Helices: the Case of the Human mAChR1 Receptor

To better investigate the GPCR structures, we have recently proposed to explore their flexibility by simulating the bending of their Pro-containing TM helices so generating a set of models (the so-called chimeras) which exhaustively combine the two conformations (bent and straight) of these helices. The primary objective of the study is to investigate whether such an approach can be exploited to enhance the reliability of the GPCR models generated by distant templates. The study was focused on the human mAChR1 receptor for which a presumably reliable model was generated using the congener mAChR3 as the template along with a second less reliable model based on the distant β2-AR template. The second model was then utilized to produce the chimeras by combining the conformations of its Pro-containing helices (i.e., TM4, TM5, TM6 and TM7 with 16 modeled chimeras). The reliability of such chimeras was assessed by virtual screening campaigns as evaluated using a novel skewness metric where they surpassed the predictive power of the more reliable mAChR1 model. Finally, the virtual screening campaigns emphasize the opportunity of synergistically combining the scores of more chimeras using a specially developed tool which generates highly predictive consensus functions by maximizing the corresponding enrichment factors.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.

Discovery of Mer kinase inhibitors by virtual screening using Structural Protein-Ligand Interaction Fingerprints

Mer is a receptor tyrosine kinase implicated in acute lymphoblastic leukemia (ALL), the most common malignancy in children. The currently available data provide a rationale for development of Mer kinase inhibitors as cancer therapeutics that can target both cell autologous and immune-modulatory anti-tumor effects. We have previously reported several series of potent Mer inhibitors and the objective of the current report is to identify a chemically dissimilar back-up series that might circumvent potential, but currently unknown, flaws inherent to the lead series. To this end, we virtually screened a database of ∼3.8million commercially available compounds using high-throughput docking followed by a filter involving Structural Protein-Ligand Interaction Fingerprints (SPLIF). SPLIF permits a quantitative assessment of whether a docking pose interacts with the protein target similarly to an endogenous or known synthetic ligand, and therefore helps to improve both sensitivity and specificity with respect to the docking score alone. Of the total of 62 experimentally tested compounds, 15 demonstrated reliable dose-dependent responses in the Mer in vitro kinase activity assay with inhibitory potencies ranging from 0.46μM to 9.9μM.

Docking and binding free energy calculations of sirtuin inhibitors

Sirtuins form a unique and highly conserved class of NAD(+)-dependent lysine deacylases. Among these the human subtypes Sirt1-3 has been implicated in the pathogenesis of numerous diseases such as cancer, metabolic syndromes, viral diseases and neurological disorders. Most of the sirtuin inhibitors that have been identified so far show limited potency and/or isoform selectivity. Here, we introduce a promising method to generate protein-inhibitor complexes of human Sirt1, Sirt2 and Sirt3 by means of ligand docking and molecular dynamics simulations. This method highly reduces the complexity of such applications and can be applied to other protein targets beside sirtuins. To the best of our knowledge, we present the first binding free energy method developed by using a validated data set of sirtuin inhibitors, where both a fair number of compounds (33 thieno[3,2-d]pyrimidine-6-carboxamide derivatives) was developed and tested in the same laboratory and also crystal structures in complex with the enzyme have been reported. A significant correlation between binding free energies derived from MM-GBSA calculations and in vitro data was found for all three sirtuin subtypes. The developed MM-GBSA protocol is computationally inexpensive and can be applied as a post-docking filter in virtual screening to find novel Sirt1-3 inhibitors as well as to prioritize compounds with similar chemical structures for further biological characterization.

Design of Novel Mycobacterium tuberculosis Pantothenate Synthetase Inhibitors: Virtual Screening, Synthesis and In Vitro Biological Activities

Pantothenate synthetase (PS) enzyme involved in the pantothenate biosynthetic pathway is essential for the virulence and persistent growth of Mycobacterium tuberculosis (MTB). It is encoded by the panC gene, and has become an appropriate target for developing new therapeutics for tuberculosis. Here we report new inhibitors active against MTB PS developed using energy based pharmacophore modelling of the available proteininhibitor complex (3IVX) and virtual screening of a large commercial library. The e-pharmacophore model consisted of a ring aromatic (R), negative ionizable (N) and acceptor (A) sites. Compounds 5 and 10 emerged as promising hits with IC50 s 2.18 µM and 6.63 µM respectively. Further structural optimization was attempted to optimize lead 10 using medicinal chemistry approach and six compounds were found to exhibit better enzyme inhibition compared to parent compound lead 10 (<6 µM).

When drug discovery meets web search: Learning to Rank for ligand-based virtual screening

Background

The rapid increase in the emergence of novel chemical substances presents a substantial demands for more sophisticated computational methodologies for drug discovery. In this study, the idea of Learning to Rank in web search was presented in drug virtual screening, which has the following unique capabilities of 1). Applicable of identifying compounds on novel targets when there is not enough training data available for these targets, and 2). Integration of heterogeneous data when compound affinities are measured in different platforms.

Results

A standard pipeline was designed to carry out Learning to Rank in virtual screening. Six Learning to Rank algorithms were investigated based on two public datasets collected from Binding Database and the newly-published Community Structure-Activity Resource benchmark dataset. The results have demonstrated that Learning to rank is an efficient computational strategy for drug virtual screening, particularly due to its novel use in cross-target virtual screening and heterogeneous data integration.

Conclusions

To the best of our knowledge, we have introduced here the first application of Learning to Rank in virtual screening. The experiment workflow and algorithm assessment designed in this study will provide a standard protocol for other similar studies. All the datasets as well as the implementations of Learning to Rank algorithms are available at http://www.tongji.edu.cn/~qiliu/lor_vs.html.

Graphical Abstract

Stereoselective virtual screening of the ZINC database using atom pair 3D-fingerprints

BACKGROUND

Tools to explore large compound databases in search for analogs of query molecules provide a strategically important support in drug discovery to help identify available analogs of any given reference or hit compound by ligand based virtual screening (LBVS). We recently showed that large databases can be formatted for very fast searching with various 2D-fingerprints using the city-block distance as similarity measure, in particular a 2D-atom pair fingerprint (APfp) and the related category extended atom pair fingerprint (Xfp) which efficiently encode molecular shape and pharmacophores, but do not perceive stereochemistry. Here we investigated related 3D-atom pair fingerprints to enable rapid stereoselective searches in the ZINC database (23.2 million 3D structures).

RESULTS

Molecular fingerprints counting atom pairs at increasing through-space distance intervals were designed using either all atoms (16-bit 3DAPfp) or different atom categories (80-bit 3DXfp). These 3D-fingerprints retrieved molecular shape and pharmacophore analogs (defined by OpenEye ROCS scoring functions) of 110,000 compounds from the Cambridge Structural Database with equal or better accuracy than the 2D-fingerprints APfp and Xfp, and showed comparable performance in recovering actives from decoys in the DUD database. LBVS by 3DXfp or 3DAPfp similarity was stereoselective and gave very different analogs when starting from different diastereomers of the same chiral drug. Results were also different from LBVS with the parent 2D-fingerprints Xfp or APfp. 3D- and 2D-fingerprints also gave very different results in LBVS of folded molecules where through-space distances between atom pairs are much shorter than topological distances.

CONCLUSIONS

3DAPfp and 3DXfp are suitable for stereoselective searches for shape and pharmacophore analogs of query molecules in large databases. Web-browsers for searching ZINC by 3DAPfp and 3DXfp similarity are accessible at www.gdb.unibe.ch and should provide useful assistance to drug discovery projects. Graphical abstractAtom pair fingerprints based on through-space distances (3DAPfp) provide better shape encoding than atom pair fingerprints based on topological distances (APfp) as measured by the recovery of ROCS shape analogs by fp similarity.

Structure based virtual screening to identify selective phosphodiesterase 4B inhibitors

Phosphodiesterase 4 (PDE4), is a hydrolytic enzyme, is proposed as a promising target in asthma and chronic obstructive pulmonary disease. PDE4B selective inhibitors are desirable to reduce the dose limiting adverse effect associated with non-selective PDE4B inhibitors. To achieve this goal, ligand based pharmacophore modeling and molecular docking approach is employed. Pharmacophore hypotheses for PDE4B and PDE4D are generated using HypoGen algorithm. The best PDE4B pharmacophore hypothesis (Hypo1_PDE4B) consist of one hydrogen-bond acceptor and two ring aromatic features, whereas PDE4D pharmacophore hypothesis (Hypo1_PDE4D) consist of one hydrogen-bond acceptor, one hydrophobic aliphatic, and two ring aromatic features. The validated pharmacophore hypotheses are used in virtual screening to identify selective PDE4B inhibitors. The hits were screened for their estimated activity, FitValue, and quantitative estimation of drug likeness. After molecular docking analysis, ten hits were purchased for in vitro analysis. Out of these, six hits have shown potent and selective inhibitory activity against PDE4B with IC50 values ranging from 2 to 378nM.

A ranking method for the concurrent learning of compounds with various activity profiles

Background

In this study, we present a SVM-based ranking algorithm for the concurrent learning of compounds with different activity profiles and their varying prioritization. To this end, a specific labeling of each compound was elaborated in order to infer virtual screening models against multiple targets. We compared the method with several state-of-the-art SVM classification techniques that are capable of inferring multi-target screening models on three chemical data sets (cytochrome P450s, dehydrogenases, and a trypsin-like protease data set) containing three different biological targets each.

Results

The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening. Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods.

Conclusions

SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design. The utilization of such methods is most helpful when dealing with compounds with various activity profiles and the finding of many ligands with an already perfectly matching activity profile is not to be expected.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-014-0050-6) contains supplementary material, which is available to authorized users.

Cell-based and virtual fragment screening for adrenergic α2C receptor agonists

Fragment-based drug discovery has emerged as an alternative to conventional lead identification and optimization strategies generally supported by biophysical detection techniques. Membrane targets like G protein-coupled receptors (GPCRs), however, offer challenges in lack of generic immobilization or stabilization methods for the dynamic, membrane-bound supramolecular complexes. Also modeling of different functional states of GPCRs proved to be a challenging task. Here we report a functional cell-based high concentration screening campaign for the identification of adrenergic α2C receptor agonists compared with the virtual screening of the same ligand set against an active-like homology model of the α2C receptor. The conventional calcium mobilization-based assay identified active fragments with a similar incidence to several other reported fragment screens on GPCRs. 16 out of 3071 screened fragments turned out as specific ligands of α2C, two of which were identified by virtual screening as well and several of the hits possessed surprisingly high affinity and ligand efficiency. Our results indicate that in vitro biological assays can be utilized in the fragment hit identification process for GPCR targets.

Ligand and structure-based approaches for the identification of SIRT1 activators

SIRT1 is a NAD(+)-dependent deacetylase that involved in various important metabolic pathways. Combined ligand and structure-based approach was utilized for identification of SIRT1 activators. Pharmacophore models were developed using DISCOtech and refined with GASP module of Sybyl X software. Pharmacophore models were composed of two hydrogen bond acceptor (HBA) atoms, two hydrogen bond donor (HBD) sites and one hydrophobic (HY) feature. The pharmacophore models were validated through receiver operating characteristic (ROC) and Güner-Henry (GH) scoring methods. Model-2 was selected as best model among the model 1-3, based on ROC and GH score value, and found reliable in identification of SIRT1 activators. Model-2 (3D search query) was searched against Zinc database. Several compounds with different chemical scaffold were retrieved as hits. Currently, there is no experimental SIRT1 3D structure available, therefore, we modeled SIRT1 protein structure using homology modeling. Compounds with Qfit value of more than 86 were selected for docking study into the SIRT1 homology model to explore the binding mode of retrieved hits in the active allosteric site. Finally, in silico ADMET prediction study was performed with two best docked compounds. Combination of ligand and structure-based modeling methods identified active hits, which may be good lead compounds to develop novel SIRT1 activators.

Identification of chikungunya virus nsP2 protease inhibitors using structure-base approaches

The nsP2 protease of chikungunya virus (CHIKV) is one of the essential components of viral replication and it plays a crucial role in the cleavage of polyprotein precursors for the viral replication process. Therefore, it is gaining attention as a potential drug design target against CHIKV. Based on the recently determined crystal structure of the nsP2 protease of CHIKV, this study identified potential inhibitors of the virus using structure-based approaches with a combination of molecular docking, virtual screening and molecular dynamics (MD) simulations. The top hit compounds from database searching, using the NCI Diversity Set II, with targeting at five potential binding sites of the nsP2 protease, were identified by blind dockings and focused dockings. These complexes were then subjected to MD simulations to investigate the stability and flexibility of the complexes and to gain a more detailed insight into the interactions between the compounds and the enzyme. The hydrogen bonds and hydrophobic contacts were characterized for the complexes. Through structural alignment, the catalytic residues Cys1013 and His1083 were identified in the N-terminal region of the nsP2 protease. The absolute binding free energies were estimated by the linear interaction energy approach and compared with the binding affinities predicted with docking. The results provide valuable information for the development of inhibitors for CHIKV.

Virtual screening for the identification of novel inhibitors of Mycobacterium tuberculosis cell wall synthesis: inhibitors targeting RmlB and RmlC

BACKGROUND

Tuberculosis remains one of the deadliest infectious diseases in humans. It has caused more than 100 million deaths since its discovery in 1882. Currently, more than 5 million people are infected with TB bacterium each year. The cell wall of Mycobacterium tuberculosis plays an important role in maintaining the ability of mycobacteria to survive in a hostile environment. Therefore, we report a virtual screening (VS) study aiming to identify novel inhibitors that simultaneously target RmlB and RmlC, which are two essential enzymes for the synthesis of the cell wall of M. tuberculosis.

METHODS

A hybrid VS method that combines drug-likeness prediction, pharmacophore modeling and molecular docking studies was used to indentify inhibitors targeting RmlB and RmlC.

RESULTS

The pharmacophore models HypoB and HypoC of RmlB inhibitors and RmlC inhibitors, respectively, were developed based on ligands complexing with their corresponding receptors. In total, 20 compounds with good absorption, distribution, metabolism, excretion, and toxicity properties were carefully selected using the hybird VS method.

DISCUSSION

We have established a hybrid VS method to discover novel inhibitors with new scaffolds. The molecular interactions of the selected potential inhibitors with the active-site residues are discussed in detail. These compounds will be further evaluated using biological activity assays and deserve consideration for further structure-activity relationship studies.

Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R²=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q²_ext = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential.

Protein structure prediction provides comparable performance to crystallographic structures in docking-based virtual screening

Structure based virtual screening has largely been limited to protein targets for which either an experimental structure is available or a strongly homologous template exists so that a high-resolution model can be constructed. The performance of state of the art protein structure predictions in virtual screening in systems where only weakly homologous templates are available is largely untested. Using the challenging DUD database of structural decoys, we show here that even using templates with only weak sequence homology (<30% sequence identity) structural models can be constructed by I-TASSER which achieve comparable enrichment rates to using the experimental bound crystal structure in the majority of the cases studied. For 65% of the targets, the I-TASSER models, which are constructed essentially in the apo conformations, reached 70% of the virtual screening performance of using the holo-crystal structures. A correlation was observed between the success of I-TASSER in modeling the global fold and local structures in the binding pockets of the proteins versus the relative success in virtual screening. The virtual screening performance can be further improved by the recognition of chemical features of the ligand compounds. These results suggest that the combination of structure-based docking and advanced protein structure modeling methods should be a valuable approach to the large-scale drug screening and discovery studies, especially for the proteins lacking crystallographic structures.

Establishment of a screening protocol for identification of aminopeptidase N inhibitors

•

Two pharmacophore models have been developed.

•

Virtual screening was performed by the pharmacophore models and docking.

•

Six selected hits were discovered to have inhibitory activities.

Inhibitors of aminopeptidase N (APN) have been thought as potential drugs for the treatment of tumor angiogenesis, invasion and metastasis and a considerable number of APN inhibitors have been reported recently. To clarify the essential structure–activity relationship for the APN inhibitors as well as identify new potent leads against APN, pharmacophore models were established using structure- and common feature-based approaches and validated with a database of active and inactive compounds. These validated pharmacophores were then used in database screening for novel virtual leads. The hit compounds were further subjected to molecular docking studies to refine the retrieved hits. Finally, six structurally diverse compounds that showed strong interactions with the key amino acids and the zinc ion were selected for biological evaluation, where two hits showed more than 70% inhibition against APN at 60 μM concentration. The evaluation results show the potential of our screening approach in identifying APN inhibitors.

Construction of a genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum provides new strategies for bactericide discovery

We reconstructed the first genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum subsp. carotovorum PC1 based on its genomic sequence, annotation, and physiological data. Metabolic characteristics were analyzed using flux balance analysis (FBA), and the results were afterwards validated by phenotype microarray (PM) experiments. The reconstructed genome-scale metabolic model, iPC1209, contains 2235 reactions, 1113 metabolites and 1209 genes. We identified 19 potential bactericide targets through a comprehensive in silico gene-deletion study. Next, we performed virtual screening to identify candidate inhibitors for an important potential drug target, alkaline phosphatase, and experimentally verified that three lead compounds were able to inhibit both bacterial cell viability and the activity of alkaline phosphatase in vitro. This study illustrates a new strategy for the discovery of agricultural bactericides.

De novo design based pharmacophore query generation and virtual screening for the discovery of Hsp-47 inhibitors

Heat shock protein-47 (Hsp-47) is exclusive collagen specific molecular chaperone involved in the maturation, processing and secretion of procollagen. Hsp-47 is consistently upregulated in several fibrotic diseases. Till date there is no potential antifibrotic small molecule drug available and Hsp-47 is known to be potential therapeutic target for fibrotic disorder and drug designing. We used the de novo drug design approach followed by pharmacophore generation and virtual screening to propose Hsp-47 based antifibrotic molecules. We used e-LEAD server for de novo drug design and ZINCPharmer for 3D pharmacophore generation and virtual screening. The virtually screened molecule may inhibit direct recruitment of collagen triple helix to interact with Hsp-47 and act as antifibrotic drug.

EDCs DataBank: 3D-Structure database of endocrine disrupting chemicals

Endocrine disrupting chemicals (EDCs) are a group of compounds that affect the endocrine system, frequently found in everyday products and epidemiologically associated with several diseases. The purpose of this work was to develop EDCs DataBank, the only database of EDCs with three-dimensional structures. This database was built on MySQL using the EU list of potential endocrine disruptors and TEDX list. It contains the three-dimensional structures available on PubChem, as well as a wide variety of information from different databases and text mining tools, useful for almost any kind of research regarding EDCs. The web platform was developed employing HTML, CSS and PHP languages, with dynamic contents in a graphic environment, facilitating information analysis. Currently EDCs DataBank has 615 molecules, including pesticides, natural and industrial products, cosmetics, drugs and food additives, among other low molecular weight xenobiotics. Therefore, this database can be used to study the toxicological effects of these molecules, or to develop pharmaceuticals targeting hormone receptors, through docking studies, high-throughput virtual screening and ligand-protein interaction analysis. EDCs DataBank is totally user-friendly and the 3D-structures of the molecules can be downloaded in several formats. This database is freely available at http://edcs.unicartagena.edu.co.

Discovery of thienoquinolone derivatives as selective and ATP non-competitive CDK5/p25 inhibitors by structure-based virtual screening

Calpain mediated cleavage of CDK5 natural precursor p35 causes a stable complex formation of CDK5/p25, which leads to hyperphosphorylation of tau. Thus inhibition of this complex is a viable target for numerous acute and chronic neurodegenerative diseases involving tau protein, including Alzheimer's disease. Since CDK5 has the highest sequence homology with its mitotic counterpart CDK2, our primary goal was to design selective CDK5/p25 inhibitors targeting neurodegeneration. A novel structure-based virtual screening protocol comprised of e-pharmacophore models and virtual screening workflow was used to identify nine compounds from a commercial database containing 2.84 million compounds. An ATP non-competitive and selective thieno[3,2-c]quinolin-4(5H)-one inhibitor (10) with ligand efficiency (LE) of 0.3 was identified as the lead molecule. Further SAR optimization led to the discovery of several low micromolar inhibitors with good selectivity. The research represents a new class of potent ATP non-competitive CDK5/p25 inhibitors with good CDK2/E selectivity.

Novel fatty acid binding protein 4 (FABP4) inhibitors: virtual screening, synthesis and crystal structure determination

Fatty acid binding protein 4 (FABP4) is a potential drug target for diabetes and atherosclerosis. For discovering new chemical entities as FABP4 inhibitors, structure-based virtual screening (VS) was performed, bioassay demonstrated that 16 of 251 tested compounds are FABP4 inhibitors, among which compound m1 are more active than endogenous ligand linoleic acid (LA). Based on the structure of m1, new derivatives were designed and prepared, leading to the discovery of two more potent inhibitors, compounds 9 and 10. To further explore the binding mechanisms of these new inhibitors, we determined the X-ray structures of the complexes of FABP4-9 and FABP4-10, which revealed similar binding conformations of the two compounds. Residue Ser53 and Arg126 formed direct hydrogen bonding with the ligands. We also found that 10 could significantly reduce the levels of lipolysis on mouse 3T3-L1 adipocytes. Taken together, in silico, in vitro and crystallographic data provide useful hints for future development of novel inhibitors against FABP4.

A computational design approach for virtual screening of peptide interactions across K(+) channel families

Ion channels represent a large family of membrane proteins with many being well established targets in pharmacotherapy. The ‘druggability’ of heteromeric channels comprised of different subunits remains obscure, due largely to a lack of channel-specific probes necessary to delineate their therapeutic potential in vivo. Our initial studies reported here, investigated the family of inwardly rectifying potassium (Kir) channels given the availability of high resolution crystal structures for the eukaryotic constitutively active Kir2.2 channel. We describe a ‘limited’ homology modeling approach that can yield chimeric Kir channels having an outer vestibule structure representing nearly any known vertebrate or invertebrate channel. These computationally-derived channel structures were tested ""in silico for ‘docking’ to NMR structures of tertiapin (TPN), a 21 amino acid peptide found in bee venom. TPN is a highly selective and potent blocker for the epithelial rat Kir1.1 channel, but does not block human or zebrafish Kir1.1 channel isoforms. Our Kir1.1 channel-TPN docking experiments recapitulated published in vitro ""findings for TPN-sensitive and TPN-insensitive channels. Additionally, in silico site-directed mutagenesis identified ‘hot spots’ within the channel outer vestibule that mediate energetically favorable docking scores and correlate with sites previously identified with in vitro thermodynamic mutant-cycle analysis. These ‘proof-of-principle’ results establish a framework for virtual screening of re-engineered peptide toxins for interactions with computationally derived Kir channels that currently lack channel-specific blockers. When coupled with electrophysiological validation, this virtual screening approach may accelerate the drug discovery process, and can be readily applied to other ion channels families where high resolution structures are available.