Pharmacophore Filtering Research Articles

Targeting LIMK1 in Alzheimer's Disease: A Multifaceted Computational Investigation Involving ADMET, Virtual Screening, Molecular Docking, and Molecular Dynamics

LIM domain kinases (LIMKs), which include LIMK1 and LIMK2, are key proteins in actin dynamics. On this basis, the inhibition of LIMK1 enhances dendritic spine density and size in dementia, reducing Alzheimer's disease (AD) effects. Therefore, several small molecules were discovered as potential therapeutic targets for AD. Herein, a pharmacophore-based virtual screening was employed to identify novel potential LIMK1 inhibitors. The pharmacophore model derived from the co-crystallized receptor structure of PubChem-329823760: LIMK1 (PDB ID: 5NXC) was then used for virtual screening. After applying Lipinski's rules and pharmacophore filters, 29 potential hits were identified. Molecular docking simulations were performed to determine the binding affinities of these candidates against LIMK1, with results ranging from -5.20 to -10.60 kcal/mol. Notably, PubChem-136621040 showed the highest binding affinity against the target protein, with a docking score of -10.60 kcal/mol, slightly surpassing the native ligand, PubChem-329823760, possessing a lower docking score of -9.80 kcal/mol. The drug-likeness and toxicity properties of target compounds were assessed through ADMET evaluations. A series of 75 nanosecond molecular dynamics (MD) simulations were conducted on the complexes generated by the best-docked molecule and the native ligand. RMSD, RMSF, SASA, and Rg calculations of their trajectories were also calculated. PubChem-136621040 possessed an average RMSD value of 0.23 nm, lower than the native ligand's 0.31 nm, indicating a greater binding stability. The RMSF results also revealed that the best-docked compound had a lower value (0.10 nm), while the native ligand possessed a value of 0.12 nm. The SASA values for both the native ligand and the best-docked compound were nearly identical, at 150.20 nm2 and 150.80 nm2, respectively. The Rg results demonstrated that both complexes maintained their rigidity throughout the simulation, with similar average values of 2.04 nm for the native ligand and 2.06 nm for the best-docked compound.

Virtual Screening of a Marine Natural Product Database for In Silico Identification of a Potential Acetylcholinesterase Inhibitor.

Alzheimer's disease is characterized by amyloid-beta aggregation and neurofibrillary tangles. Acetylcholinesterase (AChE) hydrolyses acetylcholine and induces amyloid-beta aggregation. Acetylcholinesterase inhibitors (AChEI) inhibit this aggregation by binding to AChE, making it a potential target for the treatment of AD. In this study, we have focused on the identification of potent and safe AChEI from the Comprehensive Marine Natural Product Database (CMNPD) using computational tools. For the screening of CMNPD, a structure-based pharmacophore model was generated using a structure of AChE complexed with the co-crystallized ligand galantamine (PDB ID: 4EY6). The 330 molecules that passed through the pharmacophore filter were retrieved, their drug-likeness was determined, and they were then subjected to molecular docking studies. The top ten molecules were selected depending upon their docking score and were submitted for toxicity profiling. Based on these studies, molecule 64 (CMNPD8714) was found to be the safest and was subjected to molecular dynamics simulations and density functional theory calculations. This molecule showed stable hydrogen bonding and stacked interactions with TYR341, mediated through a water bridge. In silico results can be correlated with in vitro studies for checking its activity and safety in the future.

An Integrated Pharmacophore/Docking/3D-QSAR Approach to Screening a Large Library of Products in Search of Future Botulinum Neurotoxin A Inhibitors.

Botulinum toxins are neurotoxins produced by Clostridium botulinum. This toxin can be lethal for humans as a cause of botulism; however, in small doses, the same toxin is used to treat different conditions. Even if the therapeutic doses are effective and safe, the adverse reactions could be local and could unmask a subclinical impairment of neuromuscular transmissions. There are not many cases of adverse events in the literature; however, it is possible that sometimes they do not occur as they are transient and, if they do occur, there is no possibility of a cure other than to wait for the pharmacological effect to end. Inhibition of botulinum neurotoxin type A (BoNT/A) effects is a strategy for treating botulism as it can provide an effective post-exposure remedy. In this paper, 13,592,287 compounds were screened through a pharmacophore filter, a 3D-QSAR model, and a virtual screening; then, the compounds with the best affinity were selected. Molecular dynamics simulation studies on the first four compounds predicted to be the most active were conducted to verify that the poses foreseen by the docking were stable. This approach allowed us to identify compounds with a calculated inhibitory activity in the range of 316–500 nM.

Targeting Peptidyl-prolyl Cis-trans Isomerase NIMA-interacting 1: A Structure-based Virtual Screening Approach to Find Novel Inhibitors.

Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (Pin1) is an enzyme that isomerizes phosphorylated serine or threonine motifs adjacent to proline residues. Pin1 has important roles in several cellular signaling pathways, consequently impacting the development of multiple types of cancers. Based on the previously reported inhibitory activity of pentacyclic triterpenoids isolated from the gum resin of Boswellia genus against Pin1, we designed a computational experiment using molecular docking, pharmacophore filtering, and structural clustering allied to molecular dynamics (MD) simulations and binding free energy calculations to explore the inhibitory activity of new triterpenoids against Pin1 structure. Here, we report different computational evidence that triterpenoids from neem (Azadirachta indica A. Juss), such as 6-deacetylnimbinene, 6-Oacetylnimbandiol, and nimbolide, replicate the binding mode of the Pin1 substrate peptide, interacting with high affinity with the binding site and thus destabilizing the Pin1 structure. Our results are supported by experimental data, and provide interesting structural insights into their molecular mechanism of action, indicating that their structural scaffolds could be used as a start point to develop new inhibitors against Pin1.

Identification of potential inhibitors for HCV NS5b of genotype 4a by combining dynamic simulation, protein–ligand interaction fingerprint, 3D pharmacophore, docking and 3D QSAR

HCV NS5B polymerase has been one of the most attractive targets for developing new drugs for HCV infection and many drugs were successfully developed, but all of them were designed for targeting Hepatitis C Virus genotype 1 (HCV GT1). Hepatitis C virus genotype 4a (HCV GT4a) dominant in Egypt has paid less attention. Here, we describe our protocol of virtual screening in identification of novel potential potent inhibitors for HCV NS5B polymerase of GT4a using homology modeling, protein–ligand interaction fingerprint (PLIF), docking, pharmacophore, and 3D CoMFA quantitative structure activity relationship (QSAR). Firstly, a high-quality 3D model of HCV NS5B polymerase of GT4a was constructed using crystal structure of HCV NS5B polymerase of GT1 (PDB ID: 3hkw) as a template. Then, both the model and the template were simulated to compare conformational stability. PLIF was generated using five crystal structures of HCV NS5B (PDB ID: 4mia, 4mib, 4mk9, 4mka, and 4mkb), which revealed the most important residues and their interactions with the co-crystalized ligands. After that, a 3D pharmacophore model was developed from the generated PLIF data and then used as a screening filter for 17000328 drug-like zinc database compounds. 900 compounds passed the pharmacophore filter and entered the docking-based virtual screening stage. Finally, a 3D CoMFA QSAR was developed using 42 compounds as a training and 19 compounds as a test set. The 3D CoMFA QSAR was used to design and screen some potential inhibitors, these compounds were further evaluated by the docking stage. The highest ranked five hits from docking result (compounds (p1–p4) and compound q1) were selected for further analysis.Communicated by Ramaswamy H. Sarma

In Silico Discovery and Pharmacological Analysis of Potential Ligands Targeting the Serotonin Transporter

The serotonin transporter (SERT) protein is responsible for terminating serotonergic signaling by actively removing serotonin from the synapse through a sodium‐ and chloride‐dependent uptake mechanism into the neuron. The SERT is the primary target of the widely prescribed antidepressant drugs known as the Selective Serotonin Reuptake Inhibitors (SSRIs), which include Citalopram, Fluoxetine, and Paroxetine. Consequently, interest in SERT as a target for drug discovery is substantial due to the limited efficacy and side effects of current antidepressants. Computational approaches have previously been used with homology models of SERT with success, and the recent publication of the human SERT crystal structures has provided an opportunity to revisit virtual screening. For this study, our approach was to modify the SERT structure (PDB id. 5I71), which had 13 mutations introduced to crystallize it, back to its wildtype structure by replacing the 13 mutations with the wildtype amino acids using the Molecular Operating Environment (MOE) software. Also, the backbone of the protein was relaxed using molecular dynamics with ACEMD software. The S1 binding pocket was identified using the Site Finder module in MOE, and the Maybridge Hit Discoverer library, which contained 45,000 compounds, was virtually screened through the Dock module that incorporated a pharmacophore filter on aspartic acid 98. The screen scored and ranked 33,000 compounds of which 450 were selected as the top hits based on predicted affinity (S‐score under −17). The 450 compounds were then narrowed down to 44 by filtering the dataset with the Lipinski rule of 5, and predicted affinity (S‐score < −18). The 44 compounds were subjected to fingerprinting and chemical similarity analysis using R, Python, and Pearl scripts so the compounds could be placed into various groups. Of the 44 compounds, 10 were chosen for further pharmacological analysis based on the available chemical space. These compounds represent the most unique structures found within the dataset as measured by their Tanimoto coefficient in relationship to each compound utilizing scripts in the MayaChemTools software collection. The identified compounds had characteristics ranging from 6 to 10 hydrogen bond acceptors, 1 to 4 hydrogen bond donors, and a −6 to 4.87 cLogP. A stably transfected SERT cell line was used to pharmacologically evaluate the compounds using in vitro assays. Radioligand competition assays were used to determine experimental binding affinity to the SERT. Efficacy as an inhibitor was determined using an uptake inhibition assay using radioactive serotonin.In conclusion, computational methods were utilized to identify potential ligands targeted at the serotonin transporter. From this virtual screen, 10 compounds were selected based on their chemical uniqueness to be evaluated pharmacologically.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Decoding the antineoplastic efficacy of Aplysin targeting Bcl-2: A de novo perspective

The B-cell lymphoma-2 (Bcl-2) family proteins have been attributed to be the key regulators in programmed cell death and apoptosis with a prominent role in human cancer. Understanding the fundamental principles of cell survival and death have been the main cornerstone in cancer drug discovery for identification of novel anticancer agents. In this context the Bcl-2 family of anti-and pro-apoptotic proteins provide an excellent opportunity for development of anticancer agents, as blocking the Bcl-2 or Bcl-XL functionally promotes apoptosis in tumor cells and also sensitize them to chemo- and radiotherapies. The present study reports the identification of novel Aplysin analogs as BCL-2 inhibitors from a sequential virtual screening approach using drug-like, ADMET, docking, pharmacophore filters and molecular dynamics simulation. We identified promising Aplysin analogs that have a potential to be Bcl-2 inhibitors just like the standard drug Obatoclax. One of the compound analog 11 was identified to be a promising inhibitor of Bcl-2 in the docking, pharmacophore and simulation based models.The molecular modeling information provided here can be vital in designing of the novel Bcl-2 inhibitors.

Virtual Screening Against Carbohydrate-Binding Proteins: Evaluation and Application to Bacterial Burkholderia ambifaria Lectin

Bacterial adhesion to human epithelia via lectins constitutes a therapeutic opportunity to prevent infection. Specifically, BambL (the lectin from Burkholderia ambifaria) is implicated in cystic fibrosis, where lectin-mediated bacterial adhesion to fucosylated lung epithelia is suspected to play an important role. We employed structure-based virtual screening to identify inhibitors of BambL-saccharide interaction with potential therapeutic value. To enable such discovery, a virtual screening protocol was iteratively developed via 194 retrospective screening protocols against 4 bacterial lectins (BambL, BC2L-A, FimH, and LecA) with known ligands. Specific attention was given to the rigorous evaluation of retrospective screening, including calculation of analytical errors for enrichment metrics. The developed virtual screening workflow used crystallographic constraints, pharmacophore filters, and a final manual selection step. The protocol was applied to BambL, predicting 15 active compounds from virtual libraries of approximately 7 million compounds. Experimental validation using fluorescence polarization confirmed micromolar inhibitory activity for two compounds, which were further characterized by isothermal titration calorimetry and surface plasmon resonance. Subsequent testing against LecB from Pseudomonas aeruginosa demonstrated binding specificity of one of the hit compounds. This report demonstrates the utility of virtual screening protocols, integrating ligand-based pharmacophore filtering and structure-based constraints, in the search for bacterial lectin inhibitors.

Identification of potential inhibitors for HCV NS3 genotype 4a by combining protein–ligand interaction fingerprint, 3D pharmacophore, docking, and dynamic simulation

HCV NS3 protease domain has been one of the most attractive targets for developing new drugs for HCV infection and many drugs were successfully developed, but all of them were designed for targeting HCV genotype 1 infection. HCV genotype 4a dominant in Egypt has paid less attention. Here, we describe our protocol of virtual screening in identification of novel potential potent inhibitors for HCV NS3 of genotype 4a using homology modeling, PLIF (protein–ligand interaction fingerprint), docking, pharmacophore, and dynamic simulation. A high-quality 3D model of HCV NS3 protease of genotype 4a was constructed using crystal structure of HCV NS3 protease of genotype 1b (PDB ID: 4u01) as a template. PLIF was generated using five crystal structures of HCV NS3 (PDB ID: 4u01, 3kee, 4ktc, 4i33, and 5epn) which revealed the most important residues and their interactions with the co-crystalized ligands. A 3D pharmacophore model consisting of six features was developed from the generated PLIF data and then used as a screening filter for 11,244 compounds. Only 423 compounds passed the pharmacophore filter and entered the docking-based virtual screening stage. The highest ranked five hits from docking result (compound (C1–C5)) were selected for further analysis. They exhibited stronger interaction and higher binding affinity than HCV NS3 protease ligands. Dynamic simulation of the protein–best lead complex was performed to validate and augment the virtual screening results and it showed that these compounds have a strong binding affinity and could be very effective in treating HCV genotype 4a infections.

A strategy for screening active lead compounds and functional compound combinations from herbal medicines based on pharmacophore filtering and knockout/knockin chromatography

Screening and deciphering active natural products of herbal medicines are of great importance for modern drug discovery. In this study, a novel strategy was proposed to rapidly filter ineffective compounds and target the most potential leads. The aim is to answer the key question of what components are responsible for the holistic bioactivity of an herbal product. To support the strategy, the pharmacophore-guided knockout/knockin chromatography was established for the first time. The greatest advantage of this method is that any interesting components could be automatically fished or knocked out. The method validation shows that the herbal extract was accurately reconstructed according to the experimental design. By combining with bioactivity assays, we demonstrated that “functional compound combination (FCC)”, which is the core and indispensable effective part, could be discovered from an herbal medicine and suitable as marker compounds for quality control. The applicable objects of the strategy include single herbs, herbal formulas and commercially herbal preparations. As an illustrative case study, the strategy was successfully applied to simultaneously determine active leads and the FCC in Dan-Qi formula which shows excellent free radical scavenging activity. The potential mechanisms of compounds in Dan-Qi formula reacting with three different free radicals were systematically reported for the first time. This strategy was expected to unveil the mystery of herbal medicines and inspire a natural product-based drug discovery.

E-pharmacophore filtering and molecular dynamics simulation studies in the discovery of potent drug-like molecules for chronic kidney disease

Chronic kidney disease (CKD) is a prominent health issue reported globally. The level of the vitamin D receptor (VDR) and cytochrome P450 enzyme 24-hydroxylase (CYP24A1) are crucial in the pathogenesis of secondary hyperparathyroidism (sHPT) in CKD. An elevated expression of the CYP24A1 leads to the deficiency of vitamin D and resistance to vitamin D therapy. Hence, VDR agonists and CYP24A1 antagonists are suggested to CKD patients for the management of biochemical complications. CTA-018 is a recently reported analog and acts as a potent CYP24A1 inhibitor. It inhibits CYP24A1 with an IC50 27 ± 6 nM, about 10 times more potentially than the non-selective inhibitor ketoconazole (253 ± 20 nM), and it is also been reported to induce the VDR expression. Thus, CTA-018 is under clinical trial among CKD patients. In this study, combined molecular docking and pharmacophore filtering were employed to identify compounds better than CTA-018. A huge set of 9127 compounds from Sweet Lead database were docked into the active site of VDR using Glide XP program. E-pharmacophore was developed from both the targets along with CTA-018. The compounds retrieved from the two different pharmacophore-based screening were re-docked into the active site of CYP24A1. The hits that bind well at both the active sites and matched with the pharmacophore models were considered as possible dual functional molecules against VDR and CYP24A1. Further, molecular dynamics simulation and subsequent energy decomposition analyses were also performed to study the role of specific amino acids in the active site of both VDR and CYP24A1.

The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q2cv=0.610, Nopt=7, SEPcv=0.505, r2pr=0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development.

Applying Ligands Profiling Using Multiple Extended Electron Distribution Based Field Templates and Feature Trees Similarity Searching in the Discovery of New Generation of Urea-Based Antineoplastic Kinase Inhibitors

This study provides a comprehensive computational procedure for the discovery of novel urea-based antineoplastic kinase inhibitors while focusing on diversification of both chemotype and selectivity pattern. It presents a systematic structural analysis of the different binding motifs of urea-based kinase inhibitors and the corresponding configurations of the kinase enzymes. The computational model depends on simultaneous application of two protocols. The first protocol applies multiple consecutive validated virtual screening filters including SMARTS, support vector-machine model (ROC = 0.98), Bayesian model (ROC = 0.86) and structure-based pharmacophore filters based on urea-based kinase inhibitors complexes retrieved from literature. This is followed by hits profiling against different extended electron distribution (XED) based field templates representing different kinase targets. The second protocol enables cancericidal activity verification by using the algorithm of feature trees (Ftrees) similarity searching against NCI database. Being a proof-of-concept study, this combined procedure was experimentally validated by its utilization in developing a novel series of urea-based derivatives of strong anticancer activity. This new series is based on 3-benzylbenzo[d]thiazol-2(3H)-one scaffold which has interesting chemical feasibility and wide diversification capability. Antineoplastic activity of this series was assayed in vitro against NCI 60 tumor-cell lines showing very strong inhibition of GI50 as low as 0.9 uM. Additionally, its mechanism was unleashed using KINEX™ protein kinase microarray-based small molecule inhibitor profiling platform and cell cycle analysis showing a peculiar selectivity pattern against Zap70, c-src, Mink1, csk and MeKK2 kinases. Interestingly, it showed activity on syk kinase confirming the recent studies finding of the high activity of diphenyl urea containing compounds against this kinase. Allover, the new series, which is based on a new kinase scaffold with interesting chemical diversification capabilities, showed that it exhibits its “emergent” properties by perturbing multiple unexplored kinase pathways.

Identification of Novel β3-Adrenoceptor Agonists Using Energetic Analysis, Structure Based Pharmacophores and Virtual Screening

β3 Adrenergic receptor (β3-AR), is a potential therapeutic target for the treatment of type II diabetes and obesity. We report the identification of novel compounds as β3-AR agonists by integrating different approaches of energetic analysis, structure based pharmacophore designing and virtual screening. In a step wise filtering protocol, structure based virtual screening of 2, 33, 450 compounds was done. These molecules were docked into the active site of the receptor utilizing three levels of accuracy; ligands passing the HTVS (high throughput virtual screening) step were subsequently analyzed in Glide SP (Standard Precision) and finally in Glide XP (Extra Precision) to estimate the receptor ligand binding affinities. In the second step a total of 300 pharmacophore hypotheses were generated from a set of known and diverse β3-AR agonists. The best hypothesis showed six features: three hydrogen bond acceptors, one positively charged group, and two aromatic rings. To cross validate, pharmacophore filtering was done on the set of shortlisted compounds from structure based VS (virtual screening). The different screening techniques employed were validated using enrichment factor calculations. The energetic based Pharmacophore performed fairly well at distinguishing active from the inactive compounds and yielded a greater diversity of active molecules whereas the number of actives retrieved in the case of structure based screening was the highest.

Pharmacophore filtering and 3D-QSAR in the discovery of new JAK2 inhibitors

Janus kinase 2 (JAK2) plays a crucial role in the patho-mechanism of cardiovascular pathologies, myeloproliferative disorders and many other diseases. Thus, effective JAK2 kinase inhibitors may be of significant therapeutic importance. In this study, a pharmacophore mapping studies were undertaken for a series of phenylaminopyrimidines derivatives. A five point pharmacophore with two hydrogen bond donors (D), two hydrogen bond acceptors (A) and one aromatic ring (R) as pharmacophoric features were developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R 2 = 0.970 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of Q 2 = 0.822. The external validation indicated that our QSAR models possessed high predictive powers with r 0 2 value of 0.999 and r m 2 value of 0.637 respectively. The model was then employed as 3D search query to screen against public compound libraries (Asinex, TOSLab, Maybride and Binding database) in-order to identify a new scaffold. We have identified thirteen distinct drug-like molecules binding to the JAK2. Interestingly, some of the compounds show activity against JAK2 by PASS biological activity prediction. Hence, these molecules could be potential selective inhibitors of JAK2 that can be experimentally validated and their backbone structural scaffold could serve as building blocks in designing drug-like molecules for JAK2.

Pharmacophore modeling and structure-based virtual screening for a novel “switch region” target of bacterial RNA polymerase

RNA polymerase (RNAP), an essential enzyme involved in the DNA transcription pathway, has been suggested as an attractive target for treating tuberculosis. The novel “switch region” active site has been validated and the high-resolution crystallographic structure of the inhibitor–RNAP complexes has been produced. And virtual screening target “switch region” has been performed. We proposed pharmacophore model which based on the crystallographic structure and a new combination of three docking procedures (Surflex-Dock, Gold, and AutoDock) to increase enrichment. The virtual screening protocol combined 3D pharmacophore filtering, Lipinski’s rule of 5, docking and scoring, focusing on finding compounds predicted to form interactions mimicking those of a previously known binder-Myx. The virtual screen resulted in the selection of 27 compounds that were acquired from 321, 374 starting structures. This is the first example of virtual screening which target “switch region” of RNAP.

An integrative in silico methodology for the identification of modulators of macrophage migration inhibitory factor (MIF) tautomerase activity

Macrophage migration inhibitory factor (MIF) is a major proinflammatory cytokine that has been increasingly implicated in the pathogenesis of several inflammatory, autoimmune, infectious and oncogenic diseases. Accumulating evidence suggests that the tautomerase activity of MIF plays a role in modulating some of its intra- and extra-cellular activities. Therefore, the identification and development of small-molecule inhibitors targeting the catalytic activity of MIF has emerged as an attractive and viable therapeutic strategy to attenuate its function in health and disease. Herein we report a novel virtual screening protocol for the discovery of new inhibitors of MIF’s tautomerase activity. Our protocol takes into account the flexibility and dynamics of the catalytic site by coupling molecular dynamics (MD) simulations aimed at modeling the protein’s flexibility in solution to (i) docking with FlexX, or (ii) docking with FlexX and pharmacophoric filtering with Unity. In addition, we applied in parallel a standalone docking using the new version of Surflex software. The three approaches were used to screen the ChemBridge chemical library and the inhibitory activity of the top-ranked 333 compound obtained from each approach (1000 compound in total) was assessed in vitro using the tautomerase assay. This biochemical validation process resulted in the identification of 12 novel MIF inhibitors corresponding to a 1.2% hit rate. Six of these hits came from Surflex docking; two from FlexX docking with MD simulations and four hits were identified with MDS and pharmacophore filtering with minimal overlap between the hits from each approach. Six hits were identified with IC50 values lower than 10μM (three hits with IC50 lower than 1μM); four were shown to be suicide inhibitors and act via covalent modification of the N-terminal catalytic residues Pro1. One additional inhibitor, N-phenyl-N-1,3,4-thiadiazol-2-yl-thiourea, (IC50=300nM) was obtained from FlexX docking combined to pharmacophoric filtering on one of the eight MD structures. These results demonstrate the power of integrative in silico approaches in the discovery of new modulator of MIF’s tautomerase activity. The chemical diversity and mode of action of these compounds suggest that they could be used as molecular probes to elucidate the functions and biology of MIF and as lead candidates in drug developments of anti-MIF drugs.

Virtual screening by high-throughput docking using hydrogen bonding constraints for targeting a protein-protein interface in M. tuberculosis

The resurgence of Tuberculosis, caused primarily by Mycobacterium tuberculosis, and the appearance of drug resistant tuberculosis strains encourage the need for new drugs with alternative mode of actions [1]. An interesting drug-target is the Thioredoxin-Reductase (TrxR)/Thioredoxin (Trx) System of M. tuberculosis, which is responsible for providing reducing equivalents for many cellular processes including bacterial antioxidant defence [2]. We performed a virtual screening approach for identifying novel drugs for the treatment of Tuberculosis that used the hydrogen bonding constraint functionality of GOLD for pre-processing instead of using pharmacophore filtering. Two important hydrogen bonding interactions could be identified at the protein-protein interface of the TrxR-Trx complex. A high-throughput docking was applied to filter the Intervet in-house compound library (~6.5 million compounds) for possible hits that interact with these two hydrogen bonding acceptors. This reduced the number of interesting compounds to 151768 that were redocked without any constraints and more accurate docking settings. Finally, the ranking of the reduced compound set was obtained using a normalisation based on molecular weight [3] and a consensus scoring in a restrictive way using Chemscore and ASPscore. So far, six out of the first 25 of the ranked compound list showed an activity with an IC50 value upto μM range. Especially three compounds with different scaffolds and a low molecular weight are promising candidates for further developments.

Pharmacophore Screening of the Protein Data Bank for Specific Binding Site Chemistry

A simple computational approach was developed to screen the Protein Data Bank (PDB) for putative pockets possessing a specific binding site chemistry and geometry. The method employs two commonly used 3D screening technologies, namely identification of cavities in protein structures and pharmacophore screening of chemical libraries. For each protein structure, a pocket finding algorithm is used to extract potential binding sites containing the correct types of residues, which are then stored in a large SDF-formatted virtual library; pharmacophore filters describing the desired binding site chemistry and geometry are then applied to screen this virtual library and identify pockets matching the specified structural chemistry. As an example, this approach was used to screen all human protein structures in the PDB and identify sites having chemistry similar to that of known methyl-lysine binding domains that recognize chromatin methylation marks. The selected genes include known readers of the histone code as well as novel binding pockets that may be involved in epigenetic signaling. Putative allosteric sites were identified on the structures of TP53BP1, L3MBTL3, CHEK1, KDM4A, and CREBBP.

Molecular Modeling for Screening Environmental Chemicals for Estrogenicity: Use of the Toxicant-Target Approach

There is a paucity of relevant experimental information available for the evaluation of the potential health and environmental effects of many man made chemicals. Knowledge of the potential pathways for activity provides a rational basis for the extrapolations inherent in the preliminary evaluation of risk and the establishment of priorities for obtaining missing data for environmental chemicals. The differential step in many mechanisms of toxicity may be generalized as the interaction between a small molecule (a potential toxicant) and one or more macromolecular targets. An approach based on computation of the interaction between a potential molecular toxicant and a library of macromolecular targets of toxicity has been proposed for preliminary chemical screening. In the current study, the interaction between a series of environmentally relevant chemicals and models of the rat estrogen receptors (ER) was computed and the results compared to an experimental data set of their relative binding affinities. The experimental data set consists of 281 chemicals, selected from the U.S. EPA's Toxic Substances Control Act (TSCA) inventory, that were initially screened using the rat uterine cytosolic ER-competitive binding assay. Secondary analysis, using Lineweaver-Burk plots and slope replots, was applied to confirm that only 15 of these test chemicals were true competitive inhibitors of ER binding with experimental inhibition constants (K(i)) less than 100 microM. Two different rapid computational docking methods have been applied. Each provides a score that is a surrogate for the strength of the interaction between each ligand-receptor pair. Using the score that indicates the strongest interaction for each pair, without consideration of the geometry of binding between the toxicant and the target, all of the active molecules were discovered in the first 16% of the chemicals. When a filter is applied on the basis of the geometry of a simplified pharmacophore for binding to the ER, the results are improved, and all of the active molecules were discovered in the first 8% of the chemicals. In order to obtain no false negatives in the model that includes the pharmacophore filter, only 8 molecules are false positives. These results indicate that molecular docking algorithms that were designed to find the chemicals that act most strongly at a receptor (and therefore are potential pharmaceuticals) can efficiently separate weakly active chemicals from a library of primarily inactive chemicals. The advantage of using a pharmacophore filter suggests that the development of filters of this type for other receptors will prove valuable.