Prediction of ligand-binding sites of proteins by molecular docking calculation for a random ligand library

Phytol exerts sedative-like effects and modulates the diazepam and flumazenil's action, possibly through the GABAA receptor interaction pathway

This study aimed at the evaluation of the sedative effect of phytol (PHY) with possible molecular mechanisms through in vivo and in silico studies. For this, adult male mice were randomly divided into six individual groups, namely control (vehicle), two standards (DZP: diazepam at 2 m/kg, FLU: flumazenil at 0.1 mg/kg), three test groups (PHY at 25, 50, and 75 mg/kg), and three combined groups with the DZP-2 and/or FLU-0.1 with PHY-75 mg/kg. After thirty minutes, each animal was treated with thiopental sodium (TS) at 40 mg/kg to produce sedation and observed for latency and duration of sleep up to 4 h. In silico studies were performed with the 6X3X protein of the GABAA receptor α1 and β2 subunits. The results demonstrate that PHY dose-dependently enhanced sleep duration in animals. However, it produced an insignificant sleep duration compared to the control and standard groups. It also significantly (p < 0.05) decreased the latency and increased the duration of sleep with DZP-2, while reducing these parameters with FLU-0.1. In in silico studies, DZP and FLU exhibited binding affinities with 6X3X by -6.8 and -6.9 kcal/mol, respectively, while PHY exhibited -6.9 kcal/mol. Taken together, PHY may exert a sedative-like effect in TS-induced sleeping mice and modulate the effects of DZP and FLU, possibly through interacting with the 6X3X protein of the GABAA receptor. PHY may be one of the good candidates for the management of sleep disturbances, such as insomnia.

Sclareol antagonizes the sedative effect of diazepam in thiopental sodium-induced sleeping animals: In vivo and in silico studies

BACKGROUND

Sclareol (SCL), a labdane diterpene compound found in Salvia sclarea L., exhibited therapeutic effects. This study investigated the potential interaction between SCL and diazepam (DZP) in modulating sedation in the thiopental sodium-induced sleeping animal model, supported by in-silico molecular docking analysis.

METHODS

The control, sclareol (5, 10 and 20 mg/kg), and the reference drugs [diazepam: 3 mg/kg and Caffeine (CAF): 10 mg/kg] were used in male albino mice. Then, sodium thiopental (40 mg/kg, i.p.) was administrated to induce sleep. The latent period, percentage of sleep incidence and modulation of latency were measured. Further, homology modeling of human γ-aminobutyric acid (GABA) was conducted examine the binding mode of GABA interaction with SCL, DZP, and CAF compounds RESULTS: SCL (low dose) slightly increased the sleep latency, while the higher dose significantly prolonged sleep latency. DZP, a GABAA receptor agonist, exhibited strong sleep-inducing properties, reducing sleep latency, and increasing sleeping time. Caffeine (CAF) administration prolonged sleep latency and reduced sleeping time, consistent with its stimulant effects. The combination treatments involving SCL, DZP, and CAF showed mixed effects on sleep parameters. The molecular docking revealed good binding affinities of SCL, DZP, and CAF for GABAA receptor subunits A2 and A5.

CONCLUSIONS

Our findings highlighted the complex interplay between SCL, DZP, and CAF in regulating sleep behaviors and provided insights into potential combination therapies for sleep disorders.

Cobdock: an accurate and practical machine learning-based consensus blind docking method

Probing the surface of proteins to predict the binding site and binding affinity for a given small molecule is a critical but challenging task in drug discovery. Blind docking addresses this issue by performing docking on binding regions randomly sampled from the entire protein surface. However, compared with local docking, blind docking is less accurate and reliable because the docking space is too largetly sampled. Cavity detection-guided blind docking methods improved the accuracy by using cavity detection (also known as binding site detection) tools to guide the docking procedure. However, it is worth noting that the performance of these methods heavily relies on the quality of the cavity detection tool. This constraint, namely the dependence on a single cavity detection tool, significantly impacts the overall performance of cavity detection-guided methods. To overcome this limitation, we proposed Consensus Blind Dock (CoBDock), a novel blind, parallel docking method that uses machine learning algorithms to integrate docking and cavity detection results to improve not only binding site identification but also pose prediction accuracy. Our experiments on several datasets, including PDBBind 2020, ADS, MTi, DUD-E, and CASF-2016, showed that CoBDock has better binding site and binding mode performance than other state-of-the-art cavity detector tools and blind docking methods.

Sclareol exerts synergistic antidepressant effects with quercetin and caffeine, possibly suppressing GABAergic transmission in chicks

This study evaluated the effects of sclareol (SCL) with or without caffeine (CAF) and quercetin (QUR) using in-vivo and in-silico studies. For this, 5-day-old chicks weighing between 45 and 48 g were randomly divided into five groups and treated accordingly. The chicks were monitored to compare the occurrence, latency, and duration of sleep as well as the loss and gain of righting reflex in response to SCL-10 mg/kg, CAF-10 mg/kg, and QUR-50 mg/kg using a thiopental sodium (TS)-induced sleeping model. Data were analyzed by one-way ANOVA followed by t-Student-Newman-Keuls' as a posthoc test at 95% confidence intervals with multiple comparisons. An in-silico study was also performed to investigate the possible antidepressant mechanisms of the test and/or standard drugs with different subunits of GABAA receptors. In comparison to the SCL, CAF, and QUR individual groups, SCL+CAF+QUR significantly increased the latency while decreasing the length of sleep. The incidence of loss and gain of the righting reflex was also modulated in the combination group. SCL showed better interaction with GABAA (α2 and α5) subunits than QUR with α2, α3, and α5. All these compounds showed stronger interactions with the GABAA receptor subunits than the standard CAF. Taken together, SCL, CAF, and QUR reduced the TS-induced righting reflex and sleeping time in the combination group more than in the individual treatments. SCL may show its antidepressant effects, possibly through interactions with GABAA receptor subunits.

Determination of biological activities of malabar spinach (Basellaalba) fruit extracts and molecular docking against COX-II enzyme

To achieve the health benefit from the natural of Basella. Albafruit. This study intended to figure out the bioactive compounds in the two varieties of B. alba (native and hybrid) fruit extract and measurement its biological activities like antioxidant, anti-inflammatory, cytotoxic activities and a molecular docking were performed to observed the pharmaceutical impact on the anti-inflammatory Cyclooxygenase-2 (COX-2) enzyme. The cold extractions along with GC-MS were used for the extraction of and analysis of phytoconstituents from B. alba fruit. The hemolytic inhibitory and BSA (Bovine serum albumin)-denaturation assay, DPPH(2,2-diphenyl-1-picrylhydrazyl) and H2O2-free radical scavenging analysis, and brine shrimp lethalness test were performed to measure the biological activities of the extracted The biological activities assay results showed that the ethanol extract of native malabar spinach exhibited dose-dependent antioxidant activity. The IC50 value 21.55 ± 1.51 μg/mL was for DPPH scavenging assay and 23.36 ± 0.36 μg/mL was for H2O2 scavenging analysis. In anti-inflammatory activity assessment study, the IC50 values of the ethanol extracts were 20.52 ± 0.91 μg/mL for BSA inhibition and 20.43 ± 1.30 μg/mL for RBC hemolytic inhibitory study. In this study, cytotoxicity test results reveal that aqueous extract exhibited no cytotoxicity as compared to ethanol and ethyl acetate extract (LD50 = 875.27 μg/mL). Conversely, the current study insist the in silico analysis, to find out the anti-inflammatory activity of the investigated two fruit varieties due to pharmacokinetics analysis, toxicity properties analysis, ADMETand molecular docking. The result of this study signified that both (native and hybrid) malabar spinach fruit varieties contain phytoconstituents with potent antioxidant, anti-inflammatory, and cytotoxic action.Moreover, the in vitro and in silico results suggest that the native and hybrid fruit varieties of the extracts could be a superior striver for future appraisal as a prospective therapeutically active ingredient.

FDA approved fused-pyrimidines as potential PI3K inhibitors: a computational repurposing approach

Fused pyrimidine scaffold is present in several US FDA-approved drugs for various therapeutic indications. Drug repurposing (or drug repositioning) involves the analysis of existing clinically approved drugs for new therapeutic indications. Phosphoinositide-3-kinase (PI3K), via the regulatory PI3K pathway, is involved in cell growth, proliferation, differentiation, survival, and angiogenesis. It is also considered a target in anticancer drug development as it promotes the growth of cancerous cells and increases resistance to anticancer therapy. The present work employed computational techniques like molecular docking, MMGBSA analysis, and molecular dynamics simulations to explore the PI3K inhibition by FDA-approved drugs with fused pyrimidine scaffold. The work identifies Lapatinib as a pan-class I PI3K inhibitor and Dipyridamole as an γ isoform-specific PI3K inhibitor and is reported here.Communicated by Ramaswamy H. Sarma.

Computer simulation of molecular recognition in biomolecular system: from in silico screening to generalized ensembles

Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.

Profiling of lysophosphatidylethanolamine molecular species in human serum and in silico prediction of the binding site on albumin

Lysophosphatidylethanolamine (LPE) is a major lysophospholipid produced by phospholipids and binds to human serum albumin (HSA). LPEs may play various roles in vivo depending on the differences in their acyl chains. However, only few reports have been published on the biological functions of LPEs. Hence, we determined the exact relative abundance of the major LPEs in the serum of healthy participants (n = 8) using liquid chromatography-tandem mass spectrometry. Consequently, LPE 18:2 (24.1 ± 5.2%) was found to be the most abundant in serum. To understand the distribution of LPEs, the serum separated via gel-filtration high-performance liquid chromatography was subjected to quantitative measurement. LPEs were more observed in the albumin fraction than the lipoprotein fraction. We also performed a fluorescence displacement assay and an in silico molecular docking experiment using AutoDock to confirm the affinity and binding sites of the LPEs on HSA. The binding affinities of the LPEs for drug sites 1 and 2 on HSA were relatively low, with Ki values of approximately 11 and 3.8 μM, respectively. AutoDock analysis revealed the conformation of the LPEs bound to drug sites and the possibility of LPEs binding to other HSA sites. These findings could help to elucidate the biological and pathological functions of LPEs.

Natural Compounds or Their Derivatives against Breast Cancer: A Computational Study

BACKGROUND

Breast cancer is one of the most common types of cancer diagnosed and the second leading cause of death among women. Breast cancer susceptibility proteins of type 1 and 2 are human tumor suppressor genes. Genetic variations/mutations in these two genes lead to overexpression of human breast tumor suppressor genes (e.g., BRCA1, BRCA2), which triggers uncontrolled duplication of cells in humans. In addition, multidrug resistance protein 1 (MDR1), an important cell membrane protein that pumps many foreign substances from cells, is also responsible for developing resistance to cancer chemotherapy. Aim of the Study. The aim of this study was to analyze some natural compounds or their derivatives as part of the development of strong inhibitors for breast cancer. Methodology. Molecular docking studies were performed using compounds known in the literature to be effective against BRCA1 and BRCA2 and MDR1, with positive control being 5-fluorouracil, an antineoplastic drug as a positive control.

RESULTS

The binding affinity of the compounds was analyzed, and it was observed that they had a better binding affinity for the target proteins than the standard drug 5-fluorouracil. Among the compounds analyzed, α-hederin, andrographolide, apigenin, asiatic acid, auricular acid, sinularin, curcumin, citrinin, hispolon, nerol, phytol, retinol palmitate, and sclareol showed the best binding affinity energy to the BRCA1, BRCA2, and MDR1 proteins, respectively.

CONCLUSIONS

α-Hederin, andrographolide, apigenin, asiatic acid, auricular acid, hispolon, sclareol, curcumin, citrinin, and sinularin or their derivatives can be a good source of anticancer agents in breast cancer.

Isolation of phytochemical constituents from Stevia rebaudiana (Bert.) and evaluation of their anticancer, antimicrobial and antioxidant properties via in vitro and in silico approaches

The current study was designed to isolate and characterize some bioactive secondary metabolites by using repeated chromatographic and spectroscopic techniques, targeting their anticancer, antimicrobial, and antioxidant properties through in vitro and in silico approaches. Six compounds were isolated and analyzed by thin layer chromatographic technique and the compounds were identified as 5-O-caffeoyl quinic acid (1), syringin (2), luteolin (3), apigenin (4), jhanol (5), and jhanidiol (6) based on spectroscopic methods. The cytotoxic effect of each compound was dose-dependent, and compound 1 showed a higher anti-proliferative effect (IC₅₀ = 181.3 μg/ml) than other compounds (compound 2, 4, 5, and 6). Besides, compound 1 showed the most promising antibacterial activity with a zone of inhibition ranges from 12-15 mm against different strains compared to ciprofloxacin (14-22 mm). In contrast, compound 3 exerted the highest scavenging property against DPPH free radical. Finally, the in vitro bioactivities were also supported by molecular docking studies. The computational study demonstrated that the isolated compounds exerted stronger affinity compared to the standard drugs towards the binding sites of dihydrofolate reductase (DHFR), glutathione reductase, and urase oxidase.

Metabolite profiling, anti-inflammatory, analgesic potentials of edible herb Colocasia gigantea and molecular docking study against COX-II enzyme

ETHNOPHARMACOLOGICAL RELEVANCE

Consumable herbs play a basic part in sustenance and human health. Traditionally, Colocasia gigantea Hook (Araceae) is used to treat fever, infection, wounds healing, drowsiness, tuberculosis, stomach problems etc. AIM OF THE STUDY: The study aspired to identify bioactive compounds, to evaluate anti-inflammatory and analgesic potentials of edible herb C. gigantea, and to molecular docking study against anti-inflammatory enzyme Cyclooxygenase-2 (COX-2).

MATERIALS AND METHODS

Chemical components of C. gigantea were discerned by HPLC and GCMS assays. In vitro anti-inflammatory activity was appraised by heat-induced, hypotonicity, and hydrogen peroxide-induced hemolysis assays and in vivo by formalin-induced paw edema assay. In vivo analgesic activity was evaluated by acetic acid-induced pain modulation assay. Also, molecular docking of the identified compounds was explored against the anti-inflammatory enzyme cyclooxygenase-2.

RESULTS

HPLC-DAD analysis divulged the presence of trans-cinnamic acid along with (-)-epicatechin as a prime component. Also, 9, 12-Octadecadienoic acid (37.86%) and n-Hexadecanoic acid (25.89%) as the major as well as 24 other compounds were confirmed through GCMS in the extract. In in vitro anti-inflammatory study, C. gigantea extract indicated prominent erythrocyte membrane stabilization activity with good percentage aegis in all experimental assays. In addition to, formalin-induced in vivo anti-inflammatory assay revealed the maximum (42.37% and 48.72%) suppression of edema at the fourth hour at 250 and 500 mg/kg body weight, respectively. Moreover, an in-vivo pain modulation assay exposed significant (p < 0.05) activity at experimental doses. Furthermore, in the docking study, (-)-epicatechin was more active rather than other identified compounds with strong binding affinity to COX-2 protein.

CONCLUSIONS

The extract evinced remarkable anti-inflammatory and analgesic activities. Identified bioactive components along with other components of the extract might play a pivotal role in the observed bioactivity and the results vindicate the use of edible herb C. gigantea in ancestral medicine.

Quercetin and/or Ascorbic Acid Modulatory Effect on Phenobarbital-Induced Sleeping Mice Possibly through GABAA and GABAB Receptor Interaction Pathway

Depressive disorder is a recurrent illness that affects large numbers of the general population worldwide. In recent years, the goal of depression treatment has moved from symptomatic response to that of full remission. However, treatment-resistant depression is a major challenge in the treatment of depression or depression-related disorders. Consensus opinion, therefore, suggests that effective combined aggressive initial treatment is the most appropriate strategy. This study aimed to evaluate the effects of quercetin (QUR) and/or ascorbic acid (AA) on Phenobarbital-induced sleeping mice. QUR (50 mg/kg) and/or AA (25 mg/kg) with or without intraperitoneally pre-treated with GABA receptor agonist (diazepam: 2 mg/kg, i.p.) or antagonist (Flumazenil: 2.5 mg/kg, i.p.) to underscore the effects, as well as the possible involvement of the GABA receptor in the modulatory action of QUR and AA in sleeping mice. Additionally, an in silico study was undertaken to predict the involvement of GABA receptors in the sleep mechanism. Findings suggest that the pretreatment of QUR and AA modulated the onset and duration of action of the standard drugs in experimental animals. The acute administration of QUR and/or AA significantly (p < 0.05) reversed the DZP-mediated onset of action and slightly reversed the duration of sleep time in comparison to the vehicle (control) group. A further combination of QUR or AA with the FLU resulted in an enhancement of the onset of action while reducing the duration of action, suggesting a FLU-like effect on the test animals. In in silico studies, AA and QUR showed good to moderate binding affinities with GABA_A and GABA_B receptors. Both QUR and AA produced a stimulatory-like effect on mice, possibly through the GABA_A and GABA_B receptor interaction pathways. Further studies are necessary to verify this activity and clarify the exact mechanism of action(s) involved.

Phosphate binding sites prediction in phosphorylation-dependent protein-protein interactions

MOTIVATION

Phosphate binding plays an important role in modulating protein-protein interactions, which are ubiquitous in various biological processes. Accurate prediction of phosphate binding sites is an important but challenging task. Small size and diversity of phosphate binding sites lead to a substantial challenge for developing accurate prediction methods.

RESULTS

Here we present the phosphate binding site predictor (PBSP), a novel and accurate approach to identifying phosphate binding sites from protein structures. PBSP combines an energy-based ligand-binding sites identification method with reverse focused docking using a phosphate probe. We show that PBSP outperforms not only general ligand binding sites predictors but also other existing phospholigand-specific binding sites predictors. It achieves ∼95% success rate for top 10 predicted sites with an average Matthews correlation coefficient (MCC) value of 0.84 for successful predictions. PBSP can accurately predict phosphate binding modes, with average position error of 1.4 Å and 2.4 Å in bound and unbound datasets, respectively. Lastly, visual inspection of the predictions is conducted. Reasons for failed predictions are further analyzed and possible ways to improve the performance are provided. These results demonstrate a novel and accurate approach to phosphate binding sites identification in protein structures.

AVAILABILITY

The software and benchmark datasets are freely available at http://web.pkusz.edu.cn/wu/PBSP/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Withanone from Withania somnifera Attenuates SARS-CoV-2 RBD and Host ACE2 Interactions to Rescue Spike Protein Induced Pathologies in Humanized Zebrafish Model

Purpose

SARS-CoV-2 engages human ACE2 through its spike (S) protein receptor binding domain (RBD) to enter the host cell. Recent computational studies have reported that withanone and withaferin A, phytochemicals found in Withania somnifera, target viral main protease (M^Pro) and host transmembrane TMPRSS2, and glucose related protein 78 (GRP78), respectively, implicating their potential as viral entry inhibitors. Absence of specific treatment against SARS-CoV-2 infection has encouraged exploration of phytochemicals as potential antivirals.

Aim

This study aimed at in silico exploration, along with in vitro and in vivo validation of antiviral efficacy of the phytochemical withanone.

Methods

Through molecular docking, molecular dynamic (MD) simulation and electrostatic energy calculation the plausible biochemical interactions between withanone and the ACE2-RBD complex were investigated. These in silico observations were biochemically validated by ELISA-based assays. Withanone-enriched extract from W. somnifera was tested for its ability to ameliorate clinically relevant pathological features, modelled in humanized zebrafish through SARS-CoV-2 recombinant spike (S) protein induction.

Results

Withanone bound efficiently at the interacting interface of the ACE2-RBD complex and destabilized it energetically. The electrostatic component of binding free energies of the complex was significantly decreased. The two intrachain salt bridge interactions (K31-E35) and the interchain long-range ion-pair (K31-E484), at the ACE2-RBD interface were completely abolished by withanone, in the 50 ns simulation. In vitro binding assay experimentally validated that withanone efficiently inhibited (IC₅₀=0.33 ng/mL) the interaction between ACE2 and RBD, in a dose-dependent manner. A withanone-enriched extract, without any co-extracted withaferin A, was prepared from W. somnifera leaves. This enriched extract was found to be efficient in ameliorating human-like pathological responses induced in humanized zebrafish by SARS-CoV-2 recombinant spike (S) protein.

Conclusion

In conclusion, this study provided experimental validation for computational insight into the potential of withanone as a potent inhibitor of SARS-CoV-2 coronavirus entry into the host cells.

Molecular optimization, docking, and dynamic simulation profiling of selective aromatic phytochemical ligands in blocking the SARS-CoV-2 S protein attachment to ACE2 receptor: an in silico approach of targeted drug designing

OBJECTIVES

The comprehensive in silico study aims to figure out the most effective aromatic phytochemical ligands among a number from a library, considering their pharmacokinetic efficacies in blocking "angiotensin-converting enzyme 2 (ACE2) receptor-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) S protein" complex formation as part of a target-specific drug designing.

MATERIALS AND METHODS

A library of 57 aromatic pharmacophore phytochemical ligands was prepared from where the top five ligands depending on Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) and quantitative structure-activity relationship (QSAR)-based pharmacokinetic properties were considered. The selected ligands were optimized for commencing molecular docking and dynamic simulation as a complex with the ACE2 receptor to compare their blocking efficacy with the control drug. The ligand-receptor complexes' accuracy in preventing the Spike (S) protein of SARS-CoV-2 penetration inside the host cells has been analyzed through hydrogen-hydrophobic bond interactions, principal component analysis (PCA), root mean square deviation (RMSD), root mean square fluctuation (RMSF), and B-Factor. Advanced in silico programming language and bioanalytical software were used for high throughput and authentic results.

RESULTS

ADMET and QSAR revealed Rhamnetin, Lactupicrin, Rhinacanthin D, Flemiflavanone D, and Exiguaflavanone A as the ligands of our interest to be compared with the control Cassiarin D. According to the molecular docking binding affinity to block ACE2 receptor, the efficiency mountings were Rhinacanthin D > Flemiflavanone D > Lactupicrin > Exiguaflavanone A > Rhamnetin. The binding affinity of the Cassiarin D-ACE2 complex was (-10.2 KJ/mol) found inferior to the Rhinacanthin D-ACE2 complex (-10.8 KJ/mol), referring to Rhinacanthin D as a more stable candidate to use as drugs. The RMSD values of protein-ligand complexes evaluated according to their structural conformation and stable binding pose ranged between 0.1~2.1 Å. The B-factor showed that very few loops were present in the protein structure. The RMSF peak fluctuation regions ranged 5-250, predicting efficient ligand-receptor interactions.

CONCLUSION

The experiment sequentially measures all the parameters required in referring to any pharmacophore as a drug, considering which all aromatic components analyzed in the study can strongly be predicted as target-specific medication against the novel coronavirus 2019 infection.

Structural Fluctuations of Aromatic Residues in an Apo-Form Reveal Cryptic Binding Sites: Implications for Fragment-Based Drug Design

Cryptic sites are binding pockets that are transiently formed in an apo form or that are induced by ligand binding. The investigation of cryptic sites is crucial for drug discovery, since these sites are ubiquitous in disease-related human proteins, and targeting them expands the number of drug targets greatly. However, although many computational studies have attempted to identify cryptic sites, the detection remains challenging. Here, we aimed to characterize and detect cryptic sites in terms of structural fluctuations in an apo form, investigating proteins each of which possesses a cryptic site. From their X-ray structures, we saw that aromatic residues tended to be found in cryptic sites. To examine structural fluctuations of the apo forms, we performed molecular dynamics (MD) simulations, producing probability distributions of the solvent-accessible surface area per aromatic residue. To detect aromatic residues in cryptic sites, we have proposed a "cryptic-site index" based on the distribution, demonstrating the performance via several measures, such as recall and specificity. Besides, we found that high-ranking aromatic residues were likely to probe concaves in a cryptic site. This implies that such fluctuations provide a profile of scaffolds of compounds with the potential to bind to a particular cryptic site.

NAGbinder: An approach for identifying N-acetylglucosamine interacting residues of a protein from its primary sequence

N-acetylglucosamine (NAG) belongs to the eight essential saccharides that are required to maintain the optimal health and precise functioning of systems ranging from bacteria to human. In the present study, we have developed a method, NAGbinder, which predicts the NAG-interacting residues in a protein from its primary sequence information. We extracted 231 NAG-interacting nonredundant protein chains from Protein Data Bank, where no two sequences share more than 40% sequence identity. All prediction models were trained, validated, and evaluated on these 231 protein chains. At first, prediction models were developed on balanced data consisting of 1,335 NAG-interacting and noninteracting residues, using various window size. The model developed by implementing Random Forest using binary profiles as the main principle for identifying NAG-interacting residue with window size 9, performed best among other models. It achieved highest Matthews Correlation Coefficient (MCC) of 0.31 and 0.25, and Area Under Receiver Operating Curve (AUROC) of 0.73 and 0.70 on training and validation data set, respectively. We also developed prediction models on realistic data set (1,335 NAG-interacting and 47,198 noninteracting residues) using the same principle, where the model achieved MCC of 0.26 and 0.27, and AUROC of 0.70 and 0.71, on training and validation data set, respectively. The success of our method can be appraised by the fact that, if a sequence of 1,000 amino acids is analyzed with our approach, 10 residues will be predicted as NAG-interacting, out of which five are correct. Best models were incorporated in the standalone version and in the webserver available at https://webs.iiitd.edu.in/raghava/nagbinder/.

Investigating the Binding Mode of Reversible LSD1 Inhibitors Derived from Stilbene Derivatives by 3D-QSAR, Molecular Docking, and Molecular Dynamics Simulation

Overexpression of lysine specific demethylase 1 (LSD1) has been found in many cancers. New anticancer drugs targeting LSD1 have been designed. The research on irreversible LSD1 inhibitors has entered the clinical stage, while the research on reversible LSD1 inhibitors has progressed slowly so far. In this study, 41 stilbene derivatives were studied as reversible inhibitors by three-dimensional quantitative structure-activity relationship (3D-QSAR). Comparative molecular field analysis (CoMFA q 2 = 0.623, r 2 = 0.987, r pred 2 = 0.857) and comparative molecular similarity indices analysis (CoMSIA q 2 = 0.728, r 2 = 0.960, r pred 2 = 0.899) were used to establish the model, and the structure-activity relationship of the compounds was explained by the contour maps. The binding site was predicted by two different kinds of software, and the binding modes of the compounds were further explored. A series of key amino acids Val288, Ser289, Gly314, Thr624, Lys661 were found to play a key role in the activity of the compounds. Molecular dynamics (MD) simulations were carried out for compounds 04, 17, 21, and 35, which had different activities. The reasons for the activity differences were explained by the interaction between compounds and LSD1. The binding free energy was calculated by molecular mechanics generalized Born surface area (MM/GBSA). We hope that this research will provide valuable information for the design of new reversible LSD1 inhibitors in the future.

Sertraline, chlorprothixene, and chlorpromazine characteristically interact with the REST-binding site of the corepressor mSin3, showing medulloblastoma cell growth inhibitory activities

Dysregulation of repressor-element 1 silencing transcription factor REST/NRSF is related to several neuropathies, including medulloblastoma, glioblastoma, Huntington’s disease, and neuropathic pain. Inhibitors of the interaction between the N-terminal repressor domain of REST/NRSF and the PAH1 domain of its corepressor mSin3 may ameliorate such neuropathies. In-silico screening based on the complex structure of REST/NRSF and mSin3 PAH1 yielded 52 active compounds, including approved neuropathic drugs. We investigated their binding affinity to PAH1 by NMR, and their inhibitory activity toward medulloblastoma cell growth. Interestingly, three antidepressant and antipsychotic medicines, sertraline, chlorprothixene, and chlorpromazine, were found to strongly bind to PAH1. Multivariate analysis based on NMR chemical shift changes in PAH1 residues induced by ligand binding was used to identify compound characteristics associated with cell growth inhibition. Active compounds showed a new chemo-type for inhibitors of the REST/NRSF-mSin3 interaction, raising the possibility of new therapies for neuropathies caused by dysregulation of REST/NRSF.

Sequence-based prediction of physicochemical interactions at protein functional sites using a function-and-interaction-annotated domain profile database

Background

Identifying protein functional sites (PFSs) and, particularly, the physicochemical interactions at these sites is critical to understanding protein functions and the biochemical reactions involved. Several knowledge-based methods have been developed for the prediction of PFSs; however, accurate methods for predicting the physicochemical interactions associated with PFSs are still lacking.

Results

In this paper, we present a sequence-based method for the prediction of physicochemical interactions at PFSs. The method is based on a functional site and physicochemical interaction-annotated domain profile database, called fiDPD, which was built using protein domains found in the Protein Data Bank. This method was applied to 13 target proteins from the very recent Critical Assessment of Structure Prediction (CASP10/11), and our calculations gave a Matthews correlation coefficient (MCC) value of 0.66 for PFS prediction and an 80% recall in the prediction of the associated physicochemical interactions.

Conclusions

Our results show that, in addition to the PFSs, the physical interactions at these sites are also conserved in the evolution of proteins. This work provides a valuable sequence-based tool for rational drug design and side-effect assessment. The method is freely available and can be accessed at http://202.119.249.49.

Computational methods in drug discovery

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Can We Rely on Computational Predictions To Correctly Identify Ligand Binding Sites on Novel Protein Drug Targets? Assessment of Binding Site Prediction Methods and a Protocol for Validation of Predicted Binding Sites

In the field of medicinal chemistry there is increasing focus on identifying key proteins whose biochemical functions can firmly be linked to serious diseases. Such proteins become targets for drug or inhibitor molecules that could treat or halt the disease through therapeutic action or by blocking the protein function respectively. The protein must be targeted at the relevant biologically active site for drug or inhibitor binding to be effective. As insufficient experimental data is available to confirm the biologically active binding site for novel protein targets, researchers often rely on computational prediction methods to identify binding sites. Presented herein is a short review on structure-based computational methods that (i) predict putative binding sites and (ii) assess the druggability of predicted binding sites on protein targets. This review briefly covers the principles upon which these methods are based, where they can be accessed and their reliability in identifying the correct binding site on a protein target. Based on this review, we believe that these methods are useful in predicting putative binding sites, but as they do not account for the dynamic nature of protein-ligand binding interactions, they cannot definitively identify the correct site from a ranked list of putative sites. To overcome this shortcoming, we strongly recommend using molecular docking to predict the most likely protein-ligand binding site(s) and mode(s), followed by molecular dynamics simulations and binding thermodynamics calculations to validate the docking results. This protocol provides a valuable platform for experimental and computational efforts to design novel drugs and inhibitors that target disease-related proteins.

myPresto/omegagene: a GPU-accelerated molecular dynamics simulator tailored for enhanced conformational sampling methods with a non-Ewald electrostatic scheme

Molecular dynamics (MD) is a promising computational approach to investigate dynamical behavior of molecular systems at the atomic level. Here, we present a new MD simulation engine named "myPresto/omegagene" that is tailored for enhanced conformational sampling methods with a non-Ewald electrostatic potential scheme. Our enhanced conformational sampling methods, e.g., the virtual-system-coupled multi-canonical MD (V-McMD) method, replace a multi-process parallelized run with multiple independent runs to avoid inter-node communication overhead. In addition, adopting the non-Ewald-based zero-multipole summation method (ZMM) makes it possible to eliminate the Fourier space calculations altogether. The combination of these state-of-the-art techniques realizes efficient and accurate calculations of the conformational ensemble at an equilibrium state. By taking these advantages, myPresto/omegagene is specialized for the single process execution with Graphics Processing Unit (GPU). We performed benchmark simulations for the 20-mer peptide, Trp-cage, with explicit solvent. One of the most thermodynamically stable conformations generated by the V-McMD simulation is very similar to an experimentally solved native conformation. Furthermore, the computation speed is four-times faster than that of our previous simulation engine, myPresto/psygene-G. The new simulator, myPresto/omegagene, is freely available at the following URLs: http://www.protein.osaka-u.ac.jp/rcsfp/pi/omegagene/ and http://presto.protein.osaka-u.ac.jp/myPresto4/.

Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design

Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.

Isolation, characterization and antifungal docking studies of wortmannin isolated from Penicillium radicum

During the search for a potent antifungal drug, a cell-permeable metabolite was isolated from a soil isolate taxonomically identified as Penicillium radicum. The strain was found to be a potent antifungal agent. Production conditions of the active compound were optimized and the active compound was isolated, purified, characterized and identified as a phosphoinositide 3-kinase (PI3K) inhibitor, commonly known as wortmannin (Wtmn). This is very first time we are reporting the production of Wtmn from P. radicum. In addition to its previously discovered anticancer properties, the broad spectrum antifungal property of Wtmn was re-confirmed using various fungal strains. Virtual screening was performed through molecular docking studies against potential antifungal targets, and it was found that Wtmn was predicted to impede the actions of these targets more efficiently than known antifungal compounds such as voriconazole and nikkomycin i.e. 1) mevalonate-5-diphosphate decarboxylase (1FI4), responsible for sterol/isoprenoid biosynthesis; 2) exocyst complex component SEC3 (3A58) where Rho- and phosphoinositide-dependent localization is present and 3) Kre2p/Mnt1p a Golgi alpha1,2-mannosyltransferase (1S4N) involved in the biosynthesis of yeast cell wall glycoproteins). We conclude that Wtmn produced from P. radicum is a promising lead compound which could be potentially used as an efficient antifungal drug in the near future after appropriate structural modifications to reduce toxicity and improve stability.

Methods for predicting protein-ligand binding sites

Ligand binding is required for many proteins to function properly. A large number of bioinformatics tools have been developed to predict ligand binding sites as a first step in understanding a protein's function or to facilitate docking computations in virtual screening based drug design. The prediction usually requires only the three-dimensional structure (experimentally determined or computationally modeled) of the target protein to be searched for ligand binding site(s), and Web servers have been built, allowing the free and simple use of prediction tools. In this chapter, we review the underlying concepts of the methods used by various tools, and discuss their different features and the related issues of ligand binding site prediction. Some cautionary notes about the use of these tools are also provided.

Ciprofloxacin containing Mannich base and its copper complex induce antitumor activity via different mechanism of action

The Mannich base containing ciprofloxacin and kojic acid structural units was prepared and evaluated in antitumor activity. The enhancement in antitumor activity was observed both from the Mannich base (IC(50): 103.3±5.0 µM for HepG2, 87.9±8.0 µM for HCT-116 cell) and its copper complex (IC(50): 11.5±1.8 µM for HepG2, 44.4±2.5 µM for HCT-116 cell) compared to the ciprofloxacin and kojic acid. The mechanistic studies via RT-PCR, cell cycle analysis, mitochondrial membrane potential measurement, inhibition of topoisomerase and molecular docking indicated that there is a different molecular mechanism between the Mannich base and its copper complex. The cytotoxicity of the Mannich base was involved in apoptosis, cell cycle arrest, depolarization of mitochondrial membrane and weaker topoisomerase II inhibition, but the copper complex exerted its cytotoxicity mainly through dual topoisomerase inhibition, especially stabilizing the intermediate of cleavage DNA-topoisomerase complex.

Rapid identification of ligand-binding sites by using an assignment-free NMR approach

In this study, we developed an assignment-free approach for rapid identification of ligand-binding sites in target proteins by using NMR. With a sophisticated cell-free stable isotope-labeling procedure that introduces (15)N- or (13)C-labels to specific atoms of target proteins, this approach requires only a single series of ligand titrations with labeled targets. Using titration data, ligand-binding sites in the target protein can be identified without time-consuming assignment procedures. We demonstrated the feasibility of this approach by using structurally well-characterized interactions between mitogen-activated protein (MAP) kinase p38α and its inhibitor 2-amino-3-benzyloxypyridine. Furthermore, we confirmed the recently proposed fatty acid binding to p38α and confirmed the fatty acid-binding site in the MAP kinase insert region.

Pocket-based drug design: exploring pocket space

The identification and application of druggable pockets of targets play a key role in in silico drug design, which is a fundamental step in structure-based drug design. Herein, some recent progresses and developments of the computational analysis of pockets have been covered. Also, the pockets at the protein-protein interfaces (PPI) have been considered to further explore the pocket space for drug discovery. We have presented two case studies targeting the kinetic pockets generated by normal mode analysis and molecular dynamics method, respectively, in which we focus upon incorporating the pocket flexibility into the two-dimensional virtual screening with both affinity and specificity. We applied the specificity and affinity (SPA) score to quantitatively estimate affinity and evaluate specificity using the intrinsic specificity ratio (ISR) as a quantitative criterion. In one of two cases, we also included some applications of pockets located at the dimer interfaces to emphasize the role of PPI in drug discovery. This review will attempt to summarize the current status of this pocket issue and will present some prospective avenues of further inquiry.

Combining geometric pocket detection and desolvation properties to detect putative ligand binding sites on proteins

The accurate identification of cavities that can bind ligands on the surface of proteins is of major importance for the characterization of the function of proteins based on its structure. In addition it can be helpful for rational structure-based drug design on target proteins of medical relevance and for evaluating the tendency of proteins to aggregate or oligomerize. A new approach termed dPredGB to detect and evaluate putative binding cavities on protein surfaces has been developed. In contrast to existing prediction methods that are based on purely geometric features of binding sites or on possible direct interactions with a putative binding partner the dPredGB approach combines rapid geometric detection with an evaluation of the desolvation properties of the putative binding pocket. It has been tested on a variety of proteins known to bind ligands in bound and unbound conformations. The approach outperforms most available methods and offers also the spatial characterization of the desolvation properties of a binding region. On a test set of proteins the method identifies in 69% of the unbound cases and 85% of the bound cases the known ligand binding cavity as the top ranking prediction. Possibilities to improve the prediction performance even further are also discussed.

Bioinformatics and variability in drug response: a protein structural perspective

Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk-benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein-drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.

Novel allosteric sites on Ras for lead generation

Aberrant Ras activity is a hallmark of diverse cancers and developmental diseases. Unfortunately, conventional efforts to develop effective small molecule Ras inhibitors have met with limited success. We have developed a novel multi-level computational approach to discover potential inhibitors of previously uncharacterized allosteric sites. Our approach couples bioinformatics analysis, advanced molecular simulations, ensemble docking and initial experimental testing of potential inhibitors. Molecular dynamics simulation highlighted conserved allosteric coupling of the nucleotide-binding switch region with distal regions, including loop 7 and helix 5. Bioinformatics methods identified novel transient small molecule binding pockets close to these regions and in the vicinity of the conformationally responsive switch region. Candidate binders for these pockets were selected through ensemble docking of ZINC and NCI compound libraries. Finally, cell-based assays confirmed our hypothesis that the chosen binders can inhibit the downstream signaling activity of Ras. We thus propose that the predicted allosteric sites are viable targets for the development and optimization of new drugs.

Identification of alternative binding sites for inhibitors of HIV-1 ribonuclease H through comparative analysis of virtual enrichment studies

The ribonuclease H (RNase H) domain on the p66 monomer of HIV-1 reverse transcriptase enzyme has become a target for inhibition. The active site is one potential binding site, but other RNase H sites can accommodate inhibitors. Using a combination of experimental and computational studies, potential new binding sites and binding modes have been identified. Libraries of compounds were screened with an experimental assay to identify actives without knowledge of the binding site. The compounds were computationally docked at putative binding sites. Based on positive enrichment of natural-product actives relative to the database of compounds, we propose that many inhibitors bind to an alternative, potentially allosteric, site centered on Q507 of p66. For a series of hydrazone compounds, a small amount of positive enrichment was obtained when active compounds were bound by induced-fit docking at the interface between the DNA:RNA substrate and the RNase H domain near residue Q500.

Structure-based druggability assessment--identifying suitable targets for small molecule therapeutics

A target is druggable if it can be modulated in vivo by a drug-like molecule. The general properties of oral drugs are summarized by the 'rule of 5' which specifies parameters related to size and lipophilicity. Structure-based target druggability assessment consists of predicting ligand-binding sites on the protein that are complementary to these drug-like properties. Automated identification of ligand-binding sites can use geometrical considerations alone or include specific physicochemical properties of the protein surface. Features of a pocket's size and shape, together with measures of its hydrophobicity, are most informative in identifying suitable drug-binding pockets. The recent availability of several validation sets of druggable versus undruggable targets has helped fuel the development of more elaborate methods.