Identifying new piperazine-based PARP1 inhibitors using text mining and integrated molecular modeling approaches

One of the important molecular targets for antitumor drug discovery is the polyadenosine diphosphate-ribose polymerase-1 (PARP1) enzyme. It is linked with various biological functions including DNA repair and apoptosis. It is primarily a nuclear enzyme linked to chromatin, which is activated by DNA damage. Improved expression of PARP1 in melanomas, breast cancer, lung cancer and other neoplastic diseases is often observed. A tremendous PARP research concerning cancer and ischemia is progressing very rapidly. There are currently four PARP1 inhibitors approved by the FDA on the market, namely Olaparib, Rucaparib, Niraparib and Talazoparib. All of these molecules are non-selective inhibitors of PARP1. Currently there is an urgent need for novel and selective PARP1 inhibitors. In this work, asmall molecule database (Specs SC) were used to identify the new selective lead inhibitors of PARP1. Piperazine scaffold is an important fragment that is used in many currently used FDA approved drugs in different diseases including PARP1 inhibitor Olaparib. Thus, based on text mining studies, 4674 compounds thatinclude piperazine fragments were identified and virtually screened at the binding pocket of target protein PARP1. Compounds that have high docking scores were used in molecular dynamics (MD) simulations. Free energy calculations were also performed to compare the predicted binding energies with known PARP1 inhibitors. The critical amino acid interactions of these newly identified hits in the binding pocket were also investigated in detail for better understanding of the structural features required for next generation PARP1 inhibitors. Thus, here together with combination of text-mining and integrated molecular modeling approaches, we identified novel piperazine-based hits against PARP1 enzyme. Communicated by Ramaswamy H. Sarma</p

Investigation of PDE5/PDE6 and PDE5/PDE11 selective potent tadalafil-like PDE5 inhibitors using combination of molecular modeling approaches, molecular fingerprint-based virtual screening protocols and structure-based pharmacophore development

<p>The essential biological function of phosphodiesterase (PDE) type enzymes is to regulate the cytoplasmic levels of intracellular second messengers, 3′,5′-cyclic guanosine monophosphate (cGMP) and/or 3′,5′-cyclic adenosine monophosphate (cAMP). PDE targets have 11 isoenzymes. Of these enzymes, PDE5 has attracted a special attention over the years after its recognition as being the target enzyme in treating erectile dysfunction. Due to the amino acid sequence and the secondary structural similarity of PDE6 and PDE11 with the catalytic domain of PDE5, first-generation PDE5 inhibitors (i.e. sildenafil and vardenafil) are also competitive inhibitors of PDE6 and PDE11. Since the major challenge of designing novel PDE5 inhibitors is to decrease their cross-reactivity with PDE6 and PDE11, in this study, we attempt to identify potent tadalafil-like PDE5 inhibitors that have PDE5/PDE6 and PDE5/PDE11 selectivity. For this aim, the similarity-based virtual screening protocol is applied for the “clean drug-like subset of ZINC database” that contains more than 20 million small compounds. Moreover, molecular dynamics (MD) simulations of selected hits complexed with PDE5 and off-targets were performed in order to get insights for structural and dynamical behaviors of the selected molecules as selective PDE5 inhibitors. Since tadalafil blocks hERG1 K channels in concentration dependent manner, the cardiotoxicity prediction of the hit molecules was also tested. Results of this study can be useful for designing of novel, safe and selective PDE5 inhibitors.</p

Novel tumor necrosis factor-α (TNF-α) inhibitors from small molecule library screening for their therapeutic activity profiles against rheumatoid arthritis using target-driven approaches and binary QSAR models

<p>Tumor necrosis factor alpha (TNF-α) is a multifunctional cytokine that acts as a central biological mediator for critical immune functions, including inflammation, infection, and antitumor responses. It plays pivotal role in autoimmune diseases like rheumatoid arthritis (RA). The synthetic antibodies etanercept, infliximab, and adalimumab are approved drugs for the treatment of inflammatory diseases bind to TNF-α directly, preventing its association with the tumor necrosis factor receptor (TNFR). These biologics causes serious side effects such as triggering an autoimmune anti-antibody response or the weakening of the body's immune defenses. Therefore, alternative small-molecule based therapies for TNF-α inhibition is a hot topic both in academia and industry. Most of small-molecule inhibitors reported in the literature target TNF-α, indirectly. In this study, combined <i>in silico</i> approaches have been applied to better understand the important direct interactions between TNF-α and small inhibitors. Our effort executed with the extensive literature review to select the compounds that inhibit TNF-α. High-throughput structure-based and ligand-based virtual screening methods are applied to identify TNF-α inhibitors from 3 different small molecule databases (∼256.000 molecules from Otava drug-like green chemical collection, ∼ 500.000 molecules from Otava Tangible database, ∼2.500.000 Enamine small molecule database) and ∼240.000 molecules from ZINC natural products libraries. Moreover, therapeutic activity prediction, as well as pharmacokinetic and toxicity profiles are also investigated using MetaCore/MetaDrug platform which is based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism and toxicity information, uses binary QSAR models. Particular therapeutic activity and toxic effect predictions are based on the ChemTree ability to correlate structural descriptors to that property using recursive partitioning algorithm. Molecular Dynamics (MD) simulations were also performed for selected hits to investigate their detailed structural and dynamical analysis beyond docking studies. As a result, at least one hit from each database were identified as novel TNF-α inhibitors after comprehensive virtual screening, multiple docking, e-Pharmacophore modeling (structure-based pharmacophore modeling), MD simulations, and MetaCore/MetaDrug analysis. Identified hits show predicted promising anti-arthritic activity and no toxicity.</p> <p>Communicated by Ramaswamy H. Sarma</p

Toward Understanding the Impact of Dimerization Interfaces in Angiotensin II Type 1 Receptor

Angiotensin II type 1 receptor (AT1R) is a prototypical class A G protein-coupled receptor (GPCR) that has an important role in cardiovascular pathologies and blood pressure regulation as well as in the central nervous system. GPCRs may exist and function as monomers; however, they can assemble to form higher order structures, and as a result of oligomerization, their function and signaling profiles can be altered. In the case of AT1R, the classical Gαq/11 pathway is initiated with endogenous agonist angiotensin II binding. A variety of cardiovascular pathologies such as heart failure, diabetic nephropathy, atherosclerosis, and hypertension are associated with this pathway. Recent findings reveal that AT1R can form homodimers and activate the noncanonical (β-arrestin-mediated) pathway. Nevertheless, the exact dimerization interface and atomic details of AT1R homodimerization have not been still elucidated. Here, six different symmetrical dimer interfaces of AT1R are considered, and homodimers were constructed using other published GPCR crystal dimer interfaces as template structures. These AT1R homodimers were then inserted into the model membrane bilayers and subjected to all-atom molecular dynamics simulations. Our simulation results along with the principal component analysis and water pathway analysis suggest four different interfaces as the most plausible: symmetrical transmembrane (TM)­1,2,8; TM5; TM4; and TM4,5 AT1R dimer interfaces that consist of one inactive and one active protomer. Moreover, we identified ILE2386.33 as a hub residue in the stabilization of the inactive state of AT1R

<i>In silico</i> design of novel hERG-neutral sildenafil-like PDE5 inhibitors

<p>Cyclic nucleotide phosphodiesterase enzymes (PDEs) have functions in regulating the levels of intracellular second messengers, 3′, 5′-cyclic adenosine monophosphate (cAMP) and 3′, 5′-cyclic guanosine monophosphate (cGMP), via hydrolysis and decomposing mechanisms in cells. They take essential roles in modulating various cellular activities such as memory and smooth muscle functions. PDE type 5 (PDE5) inhibitors enhance the vasodilatory effects of cGMP in the corpus cavernosum and they are used to treat erectile dysfunction. Patch clamp experiments showed that the <i>IC</i>₅₀ values of the human ether-à-go-go-related gene (hERG1) potassium (K) ion channel blocking affinity of PDE5 inhibitors sildenafil, vardenafil, and tadalafil as 33, 12, and 100 μM, respectively. hERG1 channel is responsible for the regulation of the action potential of human ventricular myocyte by contributing the rapid component of delayed rectifier K⁺ current (<i>I</i>_Kr) component of the cardiac action potential. In this work, interaction patterns and binding affinity predictions of selected PDE5 inhibitors against the hERG1 channel are studied. It is attempted to develop PDE5 inhibitor analogs with lower binding affinity to hERG1 ion channel while keeping their pharmacological activity against their principal target PDE5 using <i>in silico</i> methods. Based on detailed analyses of docking poses and predicted interaction energies, novel analogs of PDE5 inhibitors with lower predicted binding affinity to hERG1 channels without loosing their principal target activity were proposed. Moreover, molecular dynamics (MD) simulations and post-processing MD analyses (i.e. Molecular Mechanics<i>/</i>Generalized Born Surface Area calculations) were performed. Detailed analysis of molecular simulations helped us to better understand the PDE5 inhibitor–target binding interactions in the atomic level. Results of this study can be useful for designing of novel and safe PDE5 inhibitors with enhanced activity and other tailored properties.</p

Atomistic molecular dynamics simulations of typical and atypical antipsychotic drugs at the dopamine D2 receptor (D2R) elucidates their inhibition mechanism

<p>Dopamine D2 receptor (D2R) plays a pivotal role in nervous systems. Its dysfunction leads to the schizophrenia, Parkinson’s diseases and drug addiction. Since the crystal structure of the D2R was not solved yet, discovering of potent and highly selective anti-psychotic drugs carry challenges for different neurodegenerative diseases. In the current study, we modeled the three-dimensional (3D) structure of the D2R based on a recently crystallized structure of the dopamine D3 receptor. These two receptors share a high amino acid sequence homology (>70%). The interaction of the modeled receptor with well-known atypical and typical anti-psychotic drugs and the inhibition mechanisms of drugs at the catalytic domain were studied via atomistic molecular dynamics simulations. Our results revealed that, class-I and class-II forms of atypical and typical D2R antagonists follow different pathways in the inhibition of the D2Rs.</p

Combined Receptor and Ligand-Based Approach to the Universal Pharmacophore Model Development for Studies of Drug Blockade to the hERG1 Pore Domain

Long QT syndrome, LQTS, results in serious cardiovascular disorders, such as tachyarrhythmia and sudden cardiac death. A promiscuous binding of different drugs to the intracavitary binding site in the pore domain (PD) of human ether-a-go-go related gene (hERG) channels leads to a similar dysfunction, known as a drug-induced LQTS. Therefore, an assessment of the blocking ability for potent drugs is of great pragmatic value for molecular pharmacology and medicinal chemistry of hERGs. Thus, we attempted to create an in silico model aimed at blinded drug screening for their blocking ability to the hERG1 PD. Two distinct approaches to the drug blockage, ligand-based QSAR and receptor-based molecular docking methods, are combined for development of a universal pharmacophore model, which provides rapid assessment of drug blocking ability to the hERG1 channel. The best 3D-QSAR model (AAADR.7) from PHASE modeling was selected from a pool consisting of 44 initial candidates. The constructed model using 31 hERG blockers was validated with 9 test set compounds. The resulting model correctly predicted the pIC50 values of test set compounds as true unknowns. To further evaluate the pharmacophore model, 14 hERG blockers with diverse hERG blocking potencies were selected from literature and they were used as additional external blind test sets. The resulting average deviation between in vitro and predicted pIC50 values of external test set blockers is found as 0.29 suggesting that the model is able to accuretely predict the pIC50 values as true unknowns. These pharmacophore models were merged with a previously developed atomistic receptor model for the hERG1 PD and exhibited a high consistency between ligand-based and receptor-based models. Therefore, the developed 3D-QSAR model provides a predictive tool for profiling candidate compounds before their synthesis. This model also indicated the key functional groups determining a high-affinity blockade of the hERG1 channel. To cross-validate consistency between the constructed hERG1 pore domain and the pharmacophore models, we performed docking studies using the homology model of hERG1. To understand how polar or nonpolar moieties of inhibitors stimulate channel inhibition, critical amino acid replacement (i.e., T623, S624, S649, Y652 and F656) at the hERG cavity was examined by in silico mutagenesis. The average docking score differences between wild type and mutated hERG channels was found to have the following order: F656A > Y652A > S624A > T623A > S649A. These results are in agreement with experimental data

Structural Investigation of the Dopamine‑2 Receptor Agonist Bromocriptine Binding to Dimeric D2^HighR and D2^LowR States

The active (D2HighR) and inactive (D2LowR) states of dimeric dopamine D2 receptor (D2R) models were investigated to clarify the binding mechanisms of the dopamine agonist bromocriptine, using Molecular Dynamics (MD) simulation. The aim of this comprehensive study was to investigate the critical effects of bromocriptine binding on each distinct receptor conformation. The different binding modes of the bromocriptine ligand in the active and inactive states have a significant effect on the conformational changes of the receptor. Based on the MM/GBSA approach, the calculated binding enthalpies of bromocriptine demonstrated selectivity toward the D2HighR active state. There is good agreement between the calculated and experimentally measured D2HighR selectivity. In the ligand-binding site, the key amino acids identified for D2HighR were Asp114(3.32) and Glu95(2.65), and for D2LowR, it was Ser193(5.42). Moreover, analysis of replicate MD trajectories demonstrated that the bromocriptine structure was more rigid at the D2HighR state and more flexible at the D2LowR state. However, the side chains of the ligand–receptor complex of D2HighR showed larger variations relative to the corresponding regions of D2LowR. The present study is part of an ongoing research program to study D2R conformational changes during ligand activation and to evaluate the conformational state selectivity for ligand binding

Proposing Novel MAO‑B Hit Inhibitors Using Multidimensional Molecular Modeling Approaches and Application of Binary QSAR Models for Prediction of Their Therapeutic Activity, Pharmacokinetic and Toxicity Properties

Monoamine oxidase (MAO) enzymes MAO-A and MAO-B play a critical role in the metabolism of monoamine neurotransmitters. Hence, MAO inhibitors are very important for the treatment of several neurodegenerative diseases such as Parkinson’s disease (PD), Alzheimer’s disease (AD), and amyotrophic lateral sclerosis (ALS). In this study, 256 750 molecules from Otava Green Chemical Collection were virtually screened for their binding activities as MAO-B inhibitors. Two hit molecules were identified after applying different filters such as high docking scores and selectivity to MAO-B, desired pharmacokinetic profile predictions with binary quantitative structure–activity relationship (QSAR) models. Therapeutic activity prediction as well as pharmacokinetic and toxicity profiles were investigated using MetaCore/MetaDrug platform which is based on a manually curated database of molecular interactions, molecular pathways, gene–disease associations, chemical metabolism, and toxicity information. Particular therapeutic activity and toxic effect predictions are based on the ChemTree ability to correlate structural descriptors to that property using recursive partitioning algorithm. Molecular dynamics (MD) simulations were also performed to make more detailed assessments beyond docking studies. All these calculations were made not only to determine if studied molecules possess the potential to be a MAO-B inhibitor but also to find out whether they carry MAO-B selectivity versus MAO-A. The evaluation of docking results and pharmacokinetic profile predictions together with the MD simulations enabled us to identify one hit molecule (ligand <b>1</b>, Otava ID: 3463218) which displayed higher selectivity toward MAO-B than a positive control selegiline which is a commercially used drug for PD therapeutic purposes

Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers

The human ether-à-go-go-related gene (hERG) channel plays a crucial role in membrane repolarization. Any disruptions in its function can lead to severe cardiovascular disorders such as long QT syndrome (LQTS), which increases the risk of serious cardiovascular problems such as tachyarrhythmia and sudden cardiac death. Drug-induced LQTS is a significant concern and has resulted in drug withdrawals from the market in the past. The main objective of this study is to pinpoint crucial heteroatoms present in ligands that initiate interactions leading to the effective blocking of the hERG channel. To achieve this aim, ligand-based quantitative structure–activity relationships (QSAR) models were constructed using extensive ligand libraries, considering the heteroatom types and numbers, and their associated hERG channel blockage pIC50 values. Machine learning-assisted QSAR models were developed to analyze the key structural components influencing compound activity. Among the various methods, the KPLS method proved to be the most efficient, allowing the construction of models based on eight distinct fingerprints. The study delved into investigating the influence of heteroatoms on the activity of hERG blockers, revealing their significant role. Furthermore, by quantifying the effect of heteroatom types and numbers on ligand activity at the hERG channel, six compound pairs were selected for molecular docking. Subsequent molecular dynamics simulations and per residue MM/GBSA calculations were performed to comprehensively analyze the interactions of the selected pair compounds