A comparison between 2D and 3D descriptors in QSAR modeling based on bio-active conformations.

QSAR models are widely and successfully used in many research areas. The success of such models highly depends on molecular descriptors typically classified as 1D, 2D, 3D, or 4D. While 3D information is likely important, e. g., for modeling ligand-protein binding, previous comparisons between the performances of 2D and 3D descriptors were inconclusive. Yet in such comparisons the modeled ligands were not necessarily represented by their bioactive conformations. With this in mind, we mined the PDB for sets of protein-ligand complexes sharing the same protein for which uniform activity data were reported. The results, totaling 461 structures spread across six series were compiled into a carefully curated, first of its kind dataset in which each ligand is represented by its bioactive conformation. Next, each set was characterized by 2D, 3D and 2D + 3D descriptors and modeled using three machine learning algorithms, namely, k-Nearest Neighbors, Random Forest and Lasso Regression. Models' performances were evaluated on external test sets derived from the parent datasets either randomly or in a rational manner. We found that many more significant models were obtained when combining 2D and 3D descriptors. We attribute these improvements to the ability of 2D and 3D descriptors to code for different, yet complementary molecular properties.

Stability Prediction for Mutations in the Cytosolic Domains of Cystic Fibrosis Transmembrane Conductance Regulator.

Cystic Fibrosis (CF) is caused by mutations to the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) chloride channel. CFTR is composed of two membrane spanning domains, two cytosolic nucleotide-binding domains (NBD1 and NBD2) and a largely unstructured R-domain. Multiple CF-causing mutations reside in the NBDs and some are known to compromise the stability of these domains. The ability to predict the effect of mutations on the stability of the cytosolic domains of CFTR and to shed light on the mechanisms by which they exert their effect is therefore important in CF research. With this in mind, we have predicted the effect on domain stability of 59 mutations in NBD1 and NBD2 using 15 different algorithms and evaluated their performances via comparison to experimental data using several metrics including the correct classification rate (CCR), and the squared Pearson correlation (R2) and Spearman's correlation (ρ) calculated between the experimental ΔTm values and the computationally predicted ΔΔG values. Overall, the best results were obtained with FoldX and Rosetta. For NBD1 (35 mutations), FoldX provided R2 and ρ values of 0.64 and -0.71, respectively, with an 86% correct classification rate (CCR). For NBD2 (24 mutations), FoldX R2, ρ, and CCR were 0.51, -0.73, and 75%, respectively. Application of the Rosetta high-resolution protocol (Rosetta_hrp) to NBD1 yielded R2, ρ, and CCR of 0.64, -0.75, and 69%, respectively, and for NBD2 yielded R2, ρ, and CCR of 0.29, -0.27, and 50%, respectively. The corresponding numbers for the Rosetta's low-resolution protocol (Rosetta_lrp) were R2 = 0.47, ρ = -0.69, and CCR = 69% for NBD1 and R2 = 0.27, ρ = -0.24, and CCR = 63% for NBD2. For NBD1, both algorithms suggest that destabilizing mutations suffer from destabilizing vdW clashes, whereas stabilizing mutations benefit from favorable H-bond interactions. Two triple consensus approaches based on FoldX, Rosetta_lpr, and Rosetta_hpr were attempted using either "majority-voting" or "all-voting". The all-voting consensus outperformed the individual predictors, albeit on a smaller data set. In summary, our results suggest that the effect of mutations on the stability of CFTR's NBDs could be largely predicted. Since NBDs are common to all ABC transporters, these results may find use in predicting the effect and mechanism of the action of multiple disease-causing mutations in other proteins.

Rescoring of docking poses under Occam's Razor: are there simpler solutions?

Ligand affinity prediction from docking simulations is usually performed by means of highly empirical and diverse protocols. These protocols often involve the re-scoring of poses generated by a force field (FF) based Hamiltonian to provide either estimated binding affinities-or alternatively, some empirical goodness score. Re-scoring is performed by so-called scoring functions-typically, a reweighted sum of FF terms augmented by additional terms (e.g., desolvation/entropic penalty, hydrophobicity, aromatic interactions etc.). Sometimes, the scoring function actually drives ligand positioning, but often it only operates on the best scoring poses ranked top by the initial ligand positioning tool. In either of these rather intricate scenarios, scoring functions are docking-specific models, and most require machine-learning-based calibration. Therefore, docking simulations are less straightforward when compared to "standard" molecular simulations in which the FF Hamiltonian defines the energy, and affinity emerges as an ensemble average property over pools of representative conformers (i.e., the trajectory). Paraphrasing on Occam's Razor principle, additional model complexity is only acceptable if demonstrated to bring a significant improvement of prediction quality. In this work we therefore examined whether the complexity inherent to scoring functions is indeed justified. For this purpose we compared sampler for multiple protein-ligand entities, a general purpose conformation sampler based on the AMBER/GAFF FF, complemented with continuum solvation terms, with several state of the art docking tools that rely on calibrated scoring functions (Glide, Gold, Autodock-Vina) in terms of its ability to top-rank the actives from large and diverse ligand series associated with various proteins. There is no clear winner of this study, where each program performed well on most of the targets, but also failed with respect to at least one of them. Therefore, a well-parameterized force field with a simple, energy-based ligand ranking protocol appears to be an as effective docking protocol as intricate rescoring strategies based on scoring functions. A tool that can sample the conformational space of the free ligand, the bound ligand and the protein binding site using the same force field may avoid many of the approximations common to contemporary docking protocols and allow e.g., for docking into highly flexible active sites, when current scoring functions are not well suited to estimate receptor strain energies.

Putative dual inhibitors of Janus kinase 1 and 3 (JAK1/3): Pharmacophore based hierarchical virtual screening.

Janus kinase 1 and 3 are non-receptor protein tyrosine kinases, involved in the regulation of various cytokines implicated in the pathogenesis of autoimmune and inflammatory disease conditions. Thus, they serve as therapeutic targets for the designing of multi-targeted agents for the treatment of inflammatory-mediated pathological conditions. In the present study, diverse inhibitors of JAK1 and JAK3 were considered for the development of ligand-based pharmacophore models, followed by docking analysis to design putative dual inhibitors. The pharmacophore models were generated in PHASE 3.4, and top five models for each target were selected on the basis of survival minus inactive score. The best model for JAK1 (AAADH.25) and JAK3 (ADDRR.142) were selected corresponding to the highest value of Q2test. Both models were employed for the screening of a PHASE database, and subsequently, the retrieved hits were filtered employing molecular docking in JAK1 and JAK3 proteins. The stable interactions between retrieved hits and proteins were confirmed using molecular dynamics simulations. Finally, ADME properties of screened dual inhibitors displaying essential interactions with both proteins were calculated. Thus, the new leads obtained in this way may be prioritized for experimental validation as potential novel therapeutic agents in the treatment of various autoimmune and inflammatory disorders related to JAK1 and JAK3.

Bitterness prediction in-silico: A step towards better drugs.

Bitter taste is innately aversive and thought to protect against consuming poisons. Bitter taste receptors (Tas2Rs) are G-protein coupled receptors, expressed both orally and extra-orally and proposed as novel targets for several indications, including asthma. Many clinical drugs elicit bitter taste, suggesting the possibility of drugs re-purposing. On the other hand, the bitter taste of medicine presents a major compliance problem for pediatric drugs. Thus, efficient tools for predicting, measuring and masking bitterness of active pharmaceutical ingredients (APIs) are required by the pharmaceutical industry. Here we highlight the BitterDB database of bitter compounds and survey the main computational approaches to prediction of bitter taste based on compound's chemical structure. Current in silico bitterness prediction methods provide encouraging results, can be constantly improved using growing experimental data, and present a reliable and efficient addition to the APIs development toolbox.

Computational design of new protein kinase D 1 (PKD1) inhibitors: homology-based active site prediction, energy-optimized pharmacophore, docking and database screening.

Protein kinase D 1 (PKD1) overexpression has a well-validated role in cancer progression and its inhibitors have defined a protective role-play of PKD1 for various cancers such as prostate, pancreatic and noninvasive breast cancers, and more. Therefore, the current research was aimed at designing new PKD1 inhibitors combining different ligand- and structure-based computational drug designing methodologies. Initially, the three-dimensional structure of PKD1's active site was computationally modeled, corrected using molecular dynamic simulations and validated for docking experiments. The highest active PKD1 inhibitor was used to develop a structure-based energetic pharmacophore (e-pharmacophore) model, and a final model was selected with five structural features (Pmodel_AADHR). Pmodel_AADHR was validated and used for database screening to obtain new hits against PKD1. These newly retrieved hits were docked against our PKD1 protein model, and those displaying essential interactions are reported herein as new hits, which could serve as new leads for cancer research, especially pancreatic cancer.

Benzimidazole derivatives: search for GI-friendly anti-inflammatory analgesic agents.

Non-steroidal anti-inflammatory drugs (NSAIDs) have been successfully used for the alleviation of pain and inflammation in the past and continue to be used daily by millions of patients worldwide. However, gastrointestinal (GI) toxicity associated with NSAIDs is an important medical and socioeconomic problem. Local generation of various reactive oxygen species plays a significant role in the formation of gastric ulceration associated with NSAIDs therapy. Co-medication of antioxidants along with NSAIDs has been found to be beneficial in the prevention of GI injury. This paper describes the synthesis and biological evaluation of N-1-(phenylsulfonyl)-2-methylamino-substituted-1H-benzimidazole derivatives as anti-inflammatory analgesic agents with lower GI toxicity. Studies in vitro and in vivo demonstrated that the antioxidant activity of the test compounds decreased GI toxicity.

Aldose reductase inhibitors for diabetic complications: Receptor induced atom-based 3D-QSAR analysis, synthesis and biological evaluation.

Herein, atom-based 3D-QSAR analysis was performed using receptor-guided alignment of 46 flavonoid inhibitors of aldose reductase (ALR2) enzyme. 3D-QSAR models were generated in PHASE programme, and the best model corresponding to PLS factor four (QSAR4), was selected based on different statistical parameters (i.e., Rtrain(2), 0.96; Qtest(2) 0.81; SD, 0.26). The contour plots of different structural properties generated from the selected model were utilized for the designing of five new congener molecules. These designed molecules were duly synthesized, and evaluated for their in vitro ALR2 inhibitory activity that resulted in the micromolar (IC50<22µM) activity of all molecules. Thus, the newly designed molecules having ALR inhibitory potential could be employed for the management of diabetic complications.

Thiazolidine-2,4-dione derivatives: programmed chemical weapons for key protein targets of various pathological conditions.

Thiazolidine-2,4-dione is an extensively explored heterocyclic nucleus for designing of novel agents implicated for a wide variety of pathophysiological conditions, that is, diabetes, diabetic complications, cancer, arthritis, inflammation, microbial infection, and melanoma, etc. The current paradigm of drug development has shifted to the structure-based drug design, since high-throughput screenings have continued to generate disappointing results. The gap between hit generation and drug establishment can be narrowed down by investigation of ligand interactions with its receptor protein. Therefore, it would always be highly beneficial to gain knowledge of molecular level interactions between specific protein target and developed ligands; since this information can be maneuvered to design new molecules with improved protein fitting. Thus, considering this aspect, we have corroborated the information about molecular (target) level implementations of thiazolidine-2,4-diones (TZD) derivatives having therapeutic implementations such as, but not limited to, anti-diabetic (glitazones), anti-cancer, anti-arthritic, anti-inflammatory, anti-oxidant and anti-microbial, etc. The structure based SAR of TZD derivatives for various protein targets would serve as a benchmark for the alteration of existing ligands to design new ones with better binding interactions.

Interleukin-1 receptor associated kinase inhibitors: potential therapeutic agents for inflammatory- and immune-related disorders.

The various cells of innate immune system quickly counter-attack invading pathogens, and mount up "first line" defense through their trans-membrane receptors including Toll-like receptors (TLRs) and interleukin receptors (IL-Rs) that result in the secretion of pro-inflammatory cytokines. Albeit such inflammatory responses are beneficial in pathological conditions, their overstimulation may cause severe inflammatory damage; thus, make this defense system a "double edged sword". IRAK-4 has been evaluated as an indispensable element of IL-Rs and TLR pathways that can regulate the abnormal levels of cytokines, and therefore could be employed to manage immune- and inflammation-related disorders. Historically, the identification of selective and potent inhibitors has been challenging; thus, a limited number of small molecule IRAK-4 inhibitors are available in literature. Recently, IRAK-4 achieved great attention, when Ligand® pharmaceutical and Nimbus Discovery® reported the beneficial potentials of IRAK-4 inhibitors in the pre-clinical evaluation for various inflammatory- and immune-related disorders, but not limited to, such as rheumatoid arthritis, inflammatory bowel disease, psoriasis, gout, asthma and cancer.

Inhibitors of microsomal prostaglandin E2 synthase-1 enzyme as emerging anti-inflammatory candidates.

Cyclooxygenases (COX-1 and COX-2) catalyze the conversion of arachidonic acid (AA) into PGH2 that is further metabolized by terminal prostaglandin (PG) synthases into biologically active PGs, for example, prostaglandin E2 (PGE2), prostacyclin I2 (PGI2), thromboxane A2 (TXA2), prostaglandin D2 (PGD2), and prostaglandin F2 alpha (PGF2α). Among them, PGE2 is a widely distributed PG in the human body, and an important mediator of inflammatory processes. The successful modulation of this PG provides a beneficial strategy for the potential anti-inflammatory therapy. For instance, nonsteroidal anti-inflammatory agents (NSAIDs), both classical nonselective (cNSAIDs) and the selective COX-2 inhibitors (coxibs) attenuate the generation of PGH2 from AA that in turn reduces the synthesis of PGE2 and modifies the inflammatory conditions. However, the long-term use of these agents causes severe side effects due to the nonselective inhibition of other PGs, such as PGI2 and TXA2, etc. Microsomal prostaglandin E2 synthase-1 (mPGES-1), a downstream PG synthase, specifically catalyzes the biosynthesis of COX-2-derived PGE2 from PGH2, and describes itself as a valuable therapeutic target for the treatment of acute and chronic inflammatory disease conditions. Therefore, the small molecule inhibitors of mPGES-1 would serve as a beneficial anti-inflammatory therapy, with reduced side effects that are usually associated with the nonselective inhibition of PG biosynthesis.

Synthesis, cytotoxic study and docking based multidrug resistance modulator potential analysis of 2-(9-oxoacridin-10(9H)-yl)-N-phenyl acetamides.

The present study describes the synthesis of fifteen 2-(9-oxoacridin-10(9H)-yl)-N-phenyl acetamide derivatives (13a-o) through condensation of 2-chloro-N-phenyl acetamides (12a-o) with acridone molecule (10). All the synthesized compounds were screened for their anti-cancer activity against three diverse cell lines including breast (MCF-7), cervical (HeLa) and lung adenocarcinoma (A-549) employing standard MTT assay. Among synthesized molecules, 13k and 13l showed good cytotoxicity activity against considered three cancer cell lines. Additionally, in silico studies of multidrug resistance modulator (MDR) effects of these compounds was performed by docking simulation in the ATP binding site of P-gp. The results of docking simulation displayed important interactions of these molecules in the active site of this protein and predicted their MDR modulator behavior.

Purinergic receptor P2X7: a novel target for anti-inflammatory therapy.

Purinergic receptors, also known as purinoceptors, are ligand gated membrane ion channels involved in many cellular functions. Among all identified purinergic receptors, P2X7 subform is unique since it induces the caspase activity, cytokine secretion, and apoptosis. The distribution of P2X7 receptors, and the need of high concentration of ATP required to activate this receptor exhibited its ability to function as 'danger' sensor associated with tissue inflammation and damage. Further, the modulation of other signalling pathways associated with P2X7 has also been proposed to play an important role in the control of macrophage functions and inflammatory responses, especially towards lipopolysaccharides. Experimentally, researchers have also observed the decreased severity of inflammatory responses in P2X7 receptor expressing gene (P2RX7) knockout (KO) phenotypes. Therefore, newly developed potent antagonists of P2X7 receptor would serve as novel therapeutic agents to combat various inflammatory conditions. In this review article, we tried to explore various aspects of P2X7 receptors including therapeutic potential, and recent discoveries and developments of P2X7 receptor antagonists.

Receptor guided 3D-QSAR analysis of thieno[2,3-b]pyridine-5- carbonitrile inhibitors of protein kinase C theta (PKC-θ).

In the present study, receptor induced 3D-QSAR model was developed for a set of 46 thieno[2,3-b]pyridine-5- carbonitrile PKC-θ inhibitors, to explore the structural requirements of the molecules necessary for PKC-θ inhibition. Since the chemical nature of the studied molecules was different from the crystal ligand of the selected protein, induced fit docking (IFD) protocol was employed to induce the conformational changes in the active site of the selected protein. Thereafter, all molecules were docked into the newly generated active site environment of the selected protein using glide docking program, and the 3D-QSAR analysis was performed in PHASE program utilizing the docking based alignment of the molecules. The best 3D-QSAR model was selected on the basis of the highest value of Q(2)test (0.600), and the selected model also showed high values of R(2)train , 0.915, Pearson-r, 0.801 and low value of SD, 0.241. The contour maps corresponding to the selected 3D-QSAR model, in combination with docking analysis, helped to explore the essential amino acid residues involved in binding, and structural requirements of the ligand molecules necessary for complementary fit with the active site of the protein. Therefore, the information revealed from the generated model can further be explored as a novel tool for the designing of new congener molecules that can serve as potential therapeutics for the treatment of various disease conditions associated with abnormal PKC-θ signalling.

Designing of new multi-targeted inhibitors of spleen tyrosine kinase (Syk) and zeta-associated protein of 70kDa (ZAP-70) using hierarchical virtual screening protocol.

In the present study, diverse inhibitor molecules of two protein tyrosine kinases i.e. Syk and ZAP-70 were considered for the pharmacophore and docking analyses to design new multi-targeted agents for these enzymes. These enzymes are non-receptor protein tyrosine kinases and both are expressed mainly in B and T-lymphocytes where they play a crucial role in immune signaling. The role of these two enzymes in inflammatory and autoimmune diseases makes them potential therapeutic targets for the designing of new multi-targeted agents to combat disease conditions associated with them. The pharmacophore models were developed for Syk and ZAP-70 inhibitors using PHASE module of Schrödinger software. The generated pharmacophore models for both enzymes were clustered and top five models for each target were selected on the basis of survival minus inactive score that were subsequently used for the 3D-QSAR analysis. The best model for Syk (ADHR.45-5) and ZAP-70 (AADRR.265-3) were selected corresponding to highest value of Q(2). Both models were employed for the screening of a PHASE database of approximately 1.5 million compounds, subsequently the retrieved hits were screened employing docking simulations with Syk and ZAP-70 proteins. Finally, the screened compounds having structural features of both pharmacophore models and displaying essential interactions with both proteins were investigated for ADME properties. Thus, the new leads obtained in this way would show inhibitory activity against Syk and ZAP-70, and may serve as novel therapeutic agents for the treatment of inflammatory disorders.

Exploring the biological potential of urea derivatives against mPGES-1: a combination of quantum mechanics, pharmacophore modelling and QSAR analyses.

In the present molecular modelling study, recently discovered 36 selective urea derivatives were considered to develop pharmacophore based 3D-QSAR model coupled with quantum mechanics (QM) calculations to uncover the essential structural features of urea molecules for mPGES-1 inhibition. The 3D-QSAR model was selected on the basis of highest values of external predictability parameters i.e. Q2 (0.775) and Pearson-r (0.912). The model also showed the highest values of R2, 0.985; F-value, 306.3 and least SD, 0.147. The selected model was further validated for its external prediction power by calculating k, k' , R2 o and R'2 o. The contour maps generated against the selected QSAR model helped to interpret the important molecular sites of urea derivatives where the suitable structural modifications would help in better complementary fit to the active site of mPGES-1, in turn would improve the potency of newly designed molecules.

Exploring the role of water molecules for docking and receptor guided 3D-QSAR analysis of naphthyridine derivatives as spleen tyrosine kinase (Syk) inhibitors.

In the present study, 3D-QSAR analysis was performed utilizing docking based alignment of [1,6]-naphthyridine derivatives as Syk enzyme inhibitors. The role of the water molecules was explored for the docking based alignment that revealed two conserved water molecules important for proper orientation and alignment of naphthyridine inhibitors in the active site of Syk enzyme. The QSAR model was selected having highest value of Q(2) (0.624) and Pearson-r (0.862). The selected model also displayed the highest values of R(2) (0.978) and F-value (184.5) and the lowest SD (0.862). The contour plots developed on the basis of the best model helped to reveal the essential structural features of naphthyridines derivatives responsible for inhibition of Syk enzyme. The generated model and information revealed from it was utilized to design and predict new congeneric molecules that can be used as potential therapeutic agents.

Inhibitors of interleukin-2 inducible T-cell kinase as potential therapeutic candidates for the treatment of various inflammatory disease conditions.

Interleukin-2 inducible T-cell kinase (ITK), a member of Tec family of non-receptor protein tyrosine kinases plays a domineering role in the T-cell development, differentiation and production of pro-inflammatory cytokines such as IL-2, IL-4, IL-5, IL-10, IL-13 and IL-17. This kinase is also an important contributor in Th 2 cells mediated autoimmune and allergic disease conditions, e.g. psoriasis, atopic dermatitis and allergic asthma. ITK modulates T-cell signaling by activating PLCγ1 and regulating the extent of Ca²âº flux. It contributes in prolific T-cell responses by maintaining cellular adhesion and cytoskeleton reorganization via actin polymerization and integrin binding. This review article describes the structure of ITK and its role in T-cell signaling. In addition to this, data regarding small molecule inhibitors of ITK has also been reviewed from different papers and patents published.

Protein kinase C-theta inhibitors: a novel therapy for inflammatory disorders.

PKC-θ is a serine/threonine specific protein kinase and its activation depends upon the concentration of diacylglycerol (DAG) and phospholipids (phosphatidylserine). PKC-θ phosphorylates a variety of proteins that are known to be involved in the diverse cellular signaling pathways. It is predominantly expressed in the T-cells and localized in the center of immunological synapse upon T-cell receptor (TCR) and CD28 signaling. Activation of PKC-θ leads to the activation of various transcription factors in the nuclei of T-cells, e.g. NF-κB, NFAT, c-Jun, c-Fos and AP-1 that further control the proliferation and differentiation of T-cells. Defective T-cell activation in turn leads to the aberrant expression of apoptosis related proteins that cause the poor T-cell survival. Researchers have found that T-cells deficient in PKC-θ exhibit reduced interleukin-2 (IL-2) production. Apart from this role on IL-2 expression, it also plays crucial roles in the proliferation, differentiation and survival of the T-cells, which make it an attractive therapeutic target for a variety of immunological and T-cell mediated diseases. Hence, new molecules capable of modulating the expression or biological activity of PKC-θ are being developed and tested for their potential as novel therapy for several T-cells mediated disease conditions such as multiple sclerosis, rheumatoid arthritis, asthma, inflammatory bowel disease and organ transplantation, etc. In the present review, we tried to integrate the recent discoveries on PKC-θ including its pharmacology and therapeutic potential, along with brief update on its inhibitor molecules.

Structural Basis of Amino Pyrimidine Derivatives for Inhibitory Activity of PKC-θ: 3D-QSAR and Molecular Docking Studies.

In the present study, 3D-QSAR analysis was performed on a set of 56 amino pyrimidine PKC-θ inhibitors utilizing docking based alignment. The best 3D-QSAR model exhibited the highest value of Q(2) (0.825) and also displayed high values of R(2) (0.937), F (184.600) and low SD (0.240). The selected model was validated by determining the Pearson-r (0.915) for test set molecules. Docking simulation was carried out to explore the binding interactions of the molecules with active site amino acid residues of the receptor and subsequently to validate the generated 3D-QSAR model. The results of 3D-QSAR and docking analysis exerted complementary fit that strengthen the stability and reliability of the generated model. Therefore, the combined study of 3D-QSAR and docking analysis may successfully be used for the rational designing of new potent congeners.