Structure-based virtual screening,molecular docking,ADMET and molecular simulations to develop benzoxaborole analogs as potential inhibitor against Leishmania donovani trypanothione reductase

Visceral leishmaniasis (VL) is the most fatal form of leishmaniasis and it affects 70 countries worldwide. Increasing drug resistant for antileishmanial drugs such as miltefosine, sodium stibogluconate and pentamidine has been reported in the VL endemic region. Amphotericin B has shown potential antileishmanial activity in different formulations but its cost of treatment and associated nephrotoxicity have limited its use by affected people living in the endemic zone. To control the VL infection in the affected countries, it is necessary to develop new antileishmanial compounds with high efficacy and negligible toxicity. Computer aided programs such as binding free energy estimation; ADMET prediction and molecular dynamics simulation can be used to investigate novel antileishmanial molecules in shorter duration. To develop antileishmanial lead molecule, we performed standard precision (SP) docking for 1160 benzoxaborole analogs along with reference inhibitors against trypanothione reductase of Leishmania parasite. Furthermore, extra precision (XP) docking, ADMET prediction, prime MM-GBSA was conducted over 115 ligands, showing better docking score than reference inhibitors to get potential antileishmanial compounds. Simultaneously, area under the curve (AUC) was estimated using ROC plot to validate the SP and XP docking protocol. Later on, two benzoxaborole analogs with best MM-GBSA ΔG-bind were subjected to molecular simulation and docking confirmation to ensure the ligand interaction with TR. The presented drug discovery based on computational study confirms that BOB27 can be used as a potential drug candidate and warrants further experimental investigation to fight against VL in endemic areas.     

Identification of potential inhibitors of Fasciola gigantica thioredoxin1: computational screening,molecular dynamics simulation,and binding free energy studies

Fasciola gigantica is the causative organism of fascioliasis and is responsible for major economic losses in livestock production globally. F. gigantica thioredoxin1 (FgTrx1) is an important redox-active enzyme involved in maintaining the redox homeostasis in the cell. To identify a potential anti-fasciolid compound, we conducted a structure-based virtual screening of natural compounds from the ZINC database (n = 1,67,740) against the FgTrx1 structure. The ligands were docked against FgTrx1 and 309 ligands were found to have better docking score. These compounds were evaluated for Lipinski and ADMET prediction, and 30 compounds were found to fit well for re-docking studies. After refinement by molecular docking and drug-likeness analysis, three potential inhibitors (ZINC15970091, ZINC9312362, and ZINC9312661) were identified. These three ligands were further subjected to molecular dynamics simulation (MDS) to compare the dynamics and stability of the protein structure after binding of the ligands. The binding free energy analyses were calculated to determine the intermolecular interactions. The results suggested that the two compounds had a binding free energy of –82.237, and –109.52 kJ.mol^?1 for compounds with IDs ZINC9312362 and ZINC9312661, respectively. These predicted compounds displayed considerable pharmacological and structural properties to be drug candidates. We concluded that these two compounds could be potential drug candidates to fight against F. gigantica parasites.     

Structural insights into suppressor of cytokine signaling 1 protein‐ identification of new leads for type 2 diabetes mellitus

The study considers the Suppressor of cytokine signaling 1 (SOCS1) protein as a novel Type 2 diabetes mellitus (T2DM) drug target. T2DM in human beings is also triggered by the over expression of SOCS proteins. The SOCS1 acts as a ubiquitin ligase (E3), degrades Insulin Receptor Substrate 1 and 2 (IRS1 and IRS2) proteins, and causes insulin resistance. Therefore, the structure of the SOCS1 protein was evaluated using homology‐modeling and molecular dynamics methods and validated using standard computational protocols. The Protein‐Protein docking study of SOCS1 with its natural substrates, IRS1 and IRS2, and subsequent solvent accessible surface area analysis gave insight into the binding region of the SOCS1 protein. The in silico active site prediction tools highlight the residues Val155 to Ile211 in SOCS1 being implicated in the ubiquitin mediated protein degradation of the proteins IRS1 and IRS2. Virtual screening in the active site region, using large structural databases, results in selective lead structures with 3‐Pyridinol, Xanthine, and Alanine moieties as Pharmacophore. The virtual screening study shows that the residues Glu149, Gly187, Arg188, Leu191, and Ser205 of the SOCS1 are important for binding. The docking study with current anti‐diabetic therapeutics shows that the drugs Glibenclamide and Glyclopyramide have a partial affinity towards SOCS1. The predicted ADMET and IC₅₀ properties for the identified ligands are within the acceptable range with drug‐like properties. The structural data of SOCS1, its active site, and the identified lead structures are expedient in the development of new T2DM therapeutics.     

Identification of novel natural inhibitors of Opisthorchis felineus cytochrome P450 using structure-based screening and molecular dynamic simulation

Opisthorchis felineus is the etiological agent of opisthorchiasis in humans. O. felineus cytochrome P450 (OfCYP450) is an important enzyme in the parasite xenobiotic metabolism. To identify the potential anti-opisthorchid compound, we conducted a structure-based virtual screening of natural compounds from the ZINC database (n = 1,65,869) against the OfCYP450. The ligands were screened against OfCYP450 in four sequential docking modes that resulted in 361 ligands having better docking score. These compounds were evaluated for Lipinski and ADMET prediction, and 10 compounds were found to fit well with re-docking studies. After refinement by docking and drug-likeness analyses, four potential inhibitors (ZINC2358298, ZINC8790946, ZINC70707116, and ZINC85878789) were identified. These ligands with reference compounds (itraconazole and fluconazole) were further subjected to molecular dynamics simulation (MDS) and binding energy analyses to compare the dynamic structure of protein after ligand binding and the stability of the OfCYP450 and bound complexes. The binding energy analyses were also calculated. The results suggested that the compounds had a negative binding energy with ?259.41, ?110.09, ?188.25, ?163.30, ?202.10, and ?158.79 kJ mol^?1 for itraconazole, fluconazole, and compounds with IDs ZINC2358298, ZINC8790946, ZINC70707116, and ZINC85878789, respectively. These lead compounds displayed significant pharmacological and structural properties to be drug candidates. On the basis of MDS results and binding energy analyses, we concluded that ZINC8790946, ZINC70707116, and ZINC85878789 have excellent potential to inhibit OfCYP450.     

Structure-based screening and molecular dynamics simulations offer novel natural compounds as potential inhibitors of Mycobacterium tuberculosis isocitrate lyase

Mycobacterium tuberculosis is the etiological agent of tuberculosis in humans and is responsible for more than two million deaths annually. M. tuberculosis isocitrate lyase (MtbICL) catalyzes the first step in the glyoxylate cycle, plays a pivotal role in the persistence of M. tuberculosis, which acts as a potential target for an anti-tubercular drug. To identify the potential anti-tuberculosis compound, we conducted a structure-based virtual screening of natural compounds from the ZINC database (n = 1,67,748) against the MtbICL structure. The ligands were docked against MtbICL in three sequential docking modes that resulted in 340 ligands having better docking score. These compounds were evaluated for Lipinski and ADMET prediction, and 27 compounds were found to fit well with re-docking studies. After refinement by molecular docking and drug-likeness analyses, three potential inhibitors (ZINC1306071, ZINC2111081, and ZINC2134917) were identified. These three ligands and the reference compounds were further subjected to molecular dynamics simulation and binding energy analyses to compare the dynamic structure of protein after ligand binding and the stability of the MtbICL and bound complexes. The binding free energy analyses were calculated to validate and capture the intermolecular interactions. The results suggested that the three compounds had a negative binding energy with ?96.462, ?143.549, and ?122.526 kJ mol^?1 for compounds with IDs ZINC1306071, ZINC2111081, and ZINC2134917, respectively. These lead compounds displayed substantial pharmacological and structural properties to be drug candidates. We concluded that ZINC2111081 has a great potential to inhibit MtbICL and would add to the drug discovery process against tuberculosis.     

Identification of novel leads applying in silico studies for Mycobacterium multidrug resistant (MMR) protein

Multidrug efflux mechanism is the main cause of intrinsic drug resistance in bacteria. Mycobacterium multidrug resistant (MMR) protein belongs to small multidrug resistant family proteins (SMR), causing multidrug resistance to proton (H⁺)-linked lipophilic cationic drug efflux across the cell membrane. In the present work, MMR is treated as a novel target to identify new molecular entities as inhibitors for drug resistance in Mycobacterium tuberculosis. In silico techniques are applied to evaluate the 3D structure of MMR protein. The putative amino acid residues present in the active site of MMR protein are predicted. Protein–ligand interactions are studied by docking cationic ligands transported by MMR protein. Virtual screening is carried out with an in-house library of small molecules against the grid created at the predicted active site residues in the MMR protein. Absorption distribution metabolism and elimination (ADME) properties of the molecules with best docking scores are predicted. The studies with cationic ligands and those of virtual screening are analysed for identification of new lead molecules as inhibitors for drug resistance caused by the MMR protein.     

In silico hit optimization toward AKT inhibition: fragment-based approach,molecular docking and molecular dynamics study

Abstract

Protein kinase B also known as AKT is a cardinal node in different signaling pathways that regulates diverse cell processes. AKT has three isoforms that share high homology. Hyperactivation of each isoform is related with different types of cancer. This work describes the computational search for new inhibitors using a hit optimization process of the previously reported AKT pan inhibitor, a 2,4,6-trisubstituted pyridine. A database of new molecules was proposed using a variant of fragment-based docking methodology and previous reported considerations. Molecular docking followed by molecular dynamics studies were performed to select the best compounds and analyze their behavior. Protein–ligand complexes energy was calculated using molecular mechanics Poisson–Boltzmann surface area protocol. Further, proposed molecules were compared with the ChEMBL database of compounds assayed against AKT. Data analysis leads to determine the structural requirements necessary for a favorable interaction of the proposed ligands with the AKT pocket. Molecular dynamics data suggested that the pK_a of the ligands is important for the stability in the AKT pocket. Molecular similarity analysis shows that proposed ligands have not been previously reported. Thus, ligands with high docking scores and favorable behavior on molecular dynamics simulations are proposed as potential AKT inhibitors.     

Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule

Ligand-protein docking has been developed and used in facilitating new drug discoveries. In this approach, docking single or multiple small molecules to a receptor site is attempted to find putative ligands. A number of studies have shown that docking algorithms are capable of finding ligands and binding conformations at a receptor site close to experimentally determined structures. These algorithms are expected to be equally applicable to the identification of multiple proteins to which a small molecule can bind or weakly bind. We introduce a ligand-protein inverse-docking approach for finding potential protein targets of a small molecule by the computer-automated docking search of a protein cavity database. This database is developed from protein structures in the Protein Data Bank (PDB). Docking is conducted with a procedure involving multiple-conformer shape-matching alignment of a molecule to a cavity followed by molecular-mechanics torsion optimization and energy minimization on both the molecule and the protein residues at the binding region. Scoring is conducted by the evaluation of molecular-mechanics energy and, when applicable, by the further analysis of binding competitiveness against other ligands that bind to the same receptor site in at least one PDB entry. Testing results on two therapeutic agents, 4H-tamoxifen and vitamin E, showed that 50% of the computer-identified potential protein targets were implicated or confirmed by experiments. The application of this approach may facilitate the prediction of unknown and secondary therapeutic target proteins and those related to the side effects and toxicity of a drug or drug candidate. Proteins 2001;43:217-226.     

ADMET properties, database screening, molecular dynamics, density functional, and docking studies of novel potential anti-cancer compounds

Database screening was performed in a large database (hundreds of thousands of molecules which we optimized at the AM1 level) yielding a set of potential bioactive ligands. One new ligand was selected among the top solutions and optimized at the B3LYP/6-31G* level, yielding also NBO (Natural Bond Order) charges. A flexible docking program was used to investigate the interactions between the receptor and the new ligand. The stability as well as the main protein-ligand contacts of our proposed novel ligand as well as the crystallographic RAR ligand was investigated by molecular dynamics. The ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) properties as well as the parameters of the Rule of Five were investigated. The result of this work is compared with a crystallographic ligand of RAR. Our novel proposed anti-cancer ligand indicates hydrophobic interactions and strong polar interactions with the receptor.     

Identification of Putative Plant-Based ALR-2 Inhibitors to Treat Diabetic Peripheral Neuropathy

Diabetic peripheral neuropathy (DPN) is a common diabetes complication (DM). Aldose reductase -2 (ALR-2) is an oxidoreductase enzyme that is most extensively studied therapeutic target for diabetes-related complications that can be inhibited by epalrestat, which has severe adverse effects; hence the discovery of potent natural inhibitors is desired. In response, a pharmacophore model based on the properties of eplarestat was generated. The specified pharmacophore model searched the NuBBE_DB database of natural compounds for prospective lead candidates. To assess the drug-likeness and ADMET profile of the compounds, a series of in silico filtering procedures were applied. The compounds were then put through molecular docking and interaction analysis. In comparison to the reference drug, four compounds showed increased binding affinity and demonstrated critical residue interactions with greater stability and specificity. As a result, we have identified four potent inhibitors: ZINC000002895847, ZINC000002566593, ZINC000012447255, and ZINC000065074786, that could be used as pharmacological niches to develop novel ALR-2 inhibitors.     

ADMET Properties,Database Screening,Molecular Dynamics,Density Functional,and Docking Studies Of Novel Potential Anti-Cancer Compounds

Abstract

Database screening was performed in a large database (hundreds of thousands of molecules which we optimized at the AM1 level) yielding a set of potential bioactive ligands. One new ligand was selected among the top solutions and optimized at the B3LYP/6–31G* level, yielding also NBO (Natural Bond Order) charges. A flexible docking program was used to investigate the interactions between the receptor and the new ligand. The stability as well as the main protein-ligand contacts of our proposed novel ligand as well as the crystallographic RAR ligand was investigated by molecular dynamics. The ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) properties as well as the parameters of the Rule of Five were investigated. The result of this work is compared with a crystallographic ligand of RAR. Our novel proposed anti-cancer ligand indicates hydrophobic interactions and strong polar interactions with the receptor.     

Identification of novel acetylcholinesterase inhibitors through e-pharmacophore-based virtual screening and molecular dynamics simulations

Alzheimer’s disease (AD), a progressive neurodegenerative disorder is the most common cause of dementia among elderly people. To date, the successful therapeutic strategy to treat AD is maintaining the levels of acetylcholine via inhibiting acetylcholinesterase (AChE). The present study involves identification of newer AChE inhibitors by dual approach of e-pharmacophore and structure-based virtual screening of Asinex library. Robustness of docking protocol was validated by enrichment calculation with ROC value .71 and BEDROC value .028. Among 11 selected hits, ZINC72338524 with best MM-GBSA dG binding shows optimal range of CNS properties and ligand–AChE complex stability. Further, molecular dynamics study revealed its molecular interactions with Trp86, Phe338, and Tyr341 amino acid residues of catalytic anionic site and Tyr124, Ser125, and Trp286 amino acid residues of peripheral anionic site. Physicochemical properties and ADMET risk prediction indicates their potential in druggability and safety.     

In silico insights into the identification of potential novel angiogenic inhibitors against human VEGFR-2: a new SAR-based hierarchical clustering approach

Abstract

In this study, binding efficiency of new pyrrolopyrimidine structural analogs against human vascular endothelial growth factor receptor-2 (VEGFR-2) were elucidated using integrated in silico methods. Optimized high-resolution model of VEGFR-2 was generated and adopted for structure-based virtual screening approaches. Pyrrolopyrimidine inhibitor (CP15) associated compounds were screened from PubChem database and subjected to virtual screening and comparative docking methods against the receptor ligand-binding domain. Accordingly, high efficient compounds were clustered with similarity indices through PubChem structure cluster module using single-linkage algorithm. Moreover, pharmacokinetics including drug metabolism activities of high-binding leads under investigation was portrayed using ADMET and similarity ensemble analysis. Optimal energy orientations of the selected protein model have been shown to be reliable, and highly recommended for screening and docking studies. Docking and clustering strategies were shown that nineteen candidates as most effective binders for VEGFR-2 than CP15, and are grouped as three classes. Lys⁸⁶⁸, Glu⁸⁸⁵, Cys⁹¹⁹, His¹⁰²⁶, Arg¹⁰²⁷, Asp¹⁰⁴⁶, and Gly¹⁰⁴⁸ residues were predicted as novel hotspot residues, and participate in H-bonds, π-cation, π-stacking, halogen bonds, and salt-bridges formation with ligands. These additional bonds are contributing extent stability that holds the receptor structure at flexible state, this make difficult to any further conformational changes for evoking angiogenic signals. The ADMET and similarity ensemble analysis results were strongly indicated that thirteen candidates as best ligands for angiogenesis targets. Altogether, these findings indicate potential angiogenic templates and their binding levels with VEGFR-2; sorted viewpoints could be useful as a promising way to describe potential angiogenesis inhibitors with related molecular targets.     

Pharmacophore and structure-based drug design,molecular dynamics and admet/tox studies to design novel potential pad4 inhibitors

We have used docking (GLIDE), pharmacophore modeling (Discovery Studio), long trajectory molecular dynamics (Discovery Studio) and ADMET/Tox (QikProp and DEREK) to investigate PAD4 in order to determine potential novel inhibitors and hits. We have carried out virtual screening in the ZINC natural compounds database. Pharmacokinetics and Toxicity of the best hits were assessed using databases implemented in softwares that create models based on chemical structures taking into account consideration about the toxicophoric groups. A wide variety of pharmaceutical relevant properties are determined in order to make decisions about molecular suitability. After screening and analysis, the 6 most promising PAD4 inhibitors are suggested, with strong interactions (pi-stacking, hydrogen bonds, hydrophobic contacts) and suitable pharmacotherapeutic profile as well.     

Extrapolation of phenolic compounds as multi-target agents against cancer and inflammation

Natural products acquire massive structural and chemical diversity, which cannot be coordinated by any synthetic libraries for small molecules and they are continuing to inspire novel discoveries in health sciences. We have performed the computational calculations for geometry optimization and prediction of electronic and structural properties of some plant phenolic compounds through Gaussian 09 program. Energies of molecular orbitals were computed, to mimic out the stabilities arising from charge delocalization and intramolecular interactions. This process indicated the eventual charge transfer within the molecules. The molecular docking and ADMET properties of these compounds with a novel anticancer (HER2) and anti-inflammatory (COX-2) targets revealed that two molecules were capable of inhibiting both the targets, and could be used as multi target inhibitors. Furthermore, molecular dynamics simulation studies were performed to elucidate the binding mechanism and the comparison of inhibitor’s binding mode with diverse biological activities as anticancer and anti-inflammatory agents. A high-quality association was reported among quantum chemical, ADMET, docking, dynamics and MMGBSA results.

Communicated By Ramaswamy H. Sarma     

Identification of selective MMP-9 inhibitors through multiple e-pharmacophore,ligand-based pharmacophore,molecular docking,and density functional theory approaches

Matrix metalloproteinase-9 (MMP-9) is a significant target for the development of drugs for the treatment of arthritis, CNS disorders, and cancer metastasis. The structure-based and ligand-based methods were used for the virtual screening (VS) of database compounds to obtain potent and selective MMP-9 inhibitors. Experimentally known MMP-9 inhibitors were used to grow up ligand-based three pharmacophore models utilizing Schrodinger suite. The X-ray crystallographic structures of MMP-9 with different inhibitors were used to develop five energy-optimized structure-based (e-pharmacophore) models. All developed pharmacophores were validated and applied to screen the Zinc database. Pharmacophore matched compounds were subjected to molecular docking to retrieve hits with novel scaffolds. The molecules with diverse structures, high docking scores and low binding energies for various crystal structures of MMP-9, were selected as final hits. The Induced fit docking (IFD) analysis provided significant information about the driving of inhibitor to approve a suitable bioactive conformational position in the active site of protein. Since charge transfer reaction occurs during receptor–ligand interaction, therefore, electronic features of hits (ligands) are interesting parameters to explain the binding interactions. Density functional theory (DFT) at B3LYP/6-31G* level was utilized to explore electronic features of hits. The docking study of hits using AutoDock was helpful to establish the binding interactions. The study illustrates that the combined pharmacophore approach is advantageous to identify diverse hits which have better binding affinity to the active site of the enzyme for all possible bioactive conformations. The approach used in the study is worthy to design drugs for other targets.     

Ligand- and structure-based in silico studies to identify kinesin spindle protein (KSP) inhibitors as potential anticancer agents

Kinesin spindle protein (KSP) belongs to the kinesin superfamily of microtubule-based motor proteins. KSP is responsible for the establishment of the bipolar mitotic spindle which mediates cell division. Inhibition of KSP expedites the blockade of the normal cell cycle during mitosis through the generation of monoastral MT arrays that finally cause apoptotic cell death. As KSP is highly expressed in proliferating/cancer cells, it has gained considerable attention as a potential drug target for cancer chemotherapy. Therefore, this study envisaged to design novel KSP inhibitors by employing computational techniques/tools such as pharmacophore modelling, virtual database screening, molecular docking and molecular dynamics. Initially, the pharmacophore models were generated from the data-set of highly potent KSP inhibitors and the pharmacophore models were validated against in house test set ligands. The validated pharmacophore model was then taken for database screening (Maybridge and ChemBridge) to yield hits, which were further filtered for their drug-likeliness. The potential hits retrieved from virtual database screening were docked using CDOCKER to identify the ligand binding landscape. The top-ranked hits obtained from molecular docking were progressed to molecular dynamics (AMBER) simulations to deduce the ligand binding affinity. This study identified MB-41570 and CB-10358 as potential hits and evaluated these experimentally using in vitro KSP ATPase inhibition assays.     

Comparative study of the interaction of meso-tetrakis (N-para-trimethyl-anilium) porphyrin (TMAP) in its free base and Fe derivative form with oligo(dA.dT)15 and oligo(dG.dC)15

Interaction between a cationic porphyrin and its ferric derivative with oligo(dA.dT)₁₅ and oligo(dG.dC)₁₅ was studied by UV–vis spectroscopy, resonance light scattering (RLS), and circular dichroism (CD) at different ionic strengths; molecular docking and molecular dynamics simulation were also used for completion. Followings are the observed changes in the spectral properties of meso-tetrakis (N-para-trimethyl-anilium) porphyrin (TMAP), as a free-base porphyrin with no axial ligand, and its Fe derivative (FeTMAP) upon interaction with oligo(dA.dT)₁₅ and oligo(dG.dC)₁₅: (1) the substantial red shift and hypochromicity at the Soret maximum in the UV–vis spectra; (2) the increased RLS intensity by increasing the ionic strength; and (3) an intense bisignate excitonic CD signal. All of them are the reasons for TMAP and FeTMAP binding to oligo(dA.dT)₁₅ and oligo(dG.dC)₁₅ with the outside binding mode, accompanied by the self-stacking of the ligands along the oligonucleotide helix. The CD results demonstrated a drastic change from excitonic in monomeric behavior at higher ionic strengths, which indicates the groove binding of the ligands with oligonucleotides. Molecular docking also confirmed the groove binding mode of the ligands and estimated the binding constants and energies of the interactions. Their interaction trend was further confirmed by molecular dynamics technique and structure parameters obtained from simulation. It showed that TMAP reduced the number of intermolecular hydrogen bonds and increased the solvent accessible surface area in the oligonucleotide. The self-aggregation of ligands at lower concentrations was also confirmed.     

Computational Structure-Based De Novo Design of Hypothetical Inhibitors against the Anti- Inflammatory Target COX-2

Cyclooxygenase-2 (COX-2) produces prostaglandins in inflamed tissues and hence has been considered as an important target for the development of anti-inflammatory drugs since long. Administration of traditional non-steroidal anti-inflammatory drugs (NSAIDs) and other COX-2 selective inhibitors (COXIBS) for the treat of inflammation has been found to be associated with side effects, which mainly includes gastro-intestinal (GI) toxicity. The present study involves developing a virtual library of novel molecules with high druglikeliness using structure-based de novo drug designing and 2D fingerprinting approach. A library of 2657 drug like molecules was generated. 2D fingerprinting based screening of the designed library gave a unique set of compounds. Molecular docking approach was then used to identify two compounds highly specific for COX-2 isoform. Molecular dynamics simulations of protein-ligand complexes revealed that the candidate ligands were dynamically stable within the cyclooxygenase binding site of COX-2. The ligands were further analyzed for their druglikeliness, ADMET properties and synthetic accessibility using knowledge based set of rules. The results revealed that the molecules are predicted to selectively bind to COX-2 enzyme thereby potentially overcoming the limitations posed by the drugs in clinical use.