Virtual screening and docking analysis of novel ligands for selective enhancement of tea (Camellia sinensis) flavonoids

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Tea-specific flavonoid biosynthetic pathway (FBP) was retrieved from KEGG.

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Putative ligands were predicted to enhance enzymes-substrate binding affinity.

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FBP genes showed moderately higher expression & relatively strong codon adaptation.

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Most of the genes were AT-rich and biased to A/U-ending synonymous codons.

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Mutational selection was determining the selective constraints on codon bias.

Flavour of tea is mainly contributed by a group of polyphenols – flavonoids. However, the content of flavonoid fluctuates seasonally and is found to be higher in the first flush of tea, when compared to the second flush. This disparity in the flavonoid content, and hence taste, incurs heavy economic losses to the tea plantation industry each harvest season. For our present study, four key product-specific enzymes (PAL, FNS, FLS and ANS) of the tea-specific flavonoid pathway were selected to perform molecular docking studies with specific virtually screened allosteric modulators. Results of docking analyses showed Naringenin, 2-Morpholin-4-ium-4-ylethanesulfonate, 6-C-Glucosylquercetin, 2-Oxoglutaric acid, 3,5,7,3′,4′-pentahydroxyflavone to be capable of improving the spontaneity of the enzyme-substrate reactions in terms of docking score, RMSD values, and non-covalent interactions (H-bond,hydrophobic interaction, Π-stacking, salt bridge, etc.). Further, the evolutionary relationship of tea flavonoid pathway enzymes was constructed and compared with related taxa. The codon usage-based of tea flavonoid biosynthetic genes indicated the non-biasness of their nucleotide composition. Overall this study will provide a direction towards putative ligand-dependent enhancement of flavonoid content, irrespective of seasonal variation.

Targeting a cryptic allosteric site of SIRT6 with small-molecule inhibitors that inhibit the migration of pancreatic cancer cells

SIRT6 belongs to the conserved NAD⁺-dependent deacetylase superfamily and mediates multiple biological and pathological processes. Targeting SIRT6 by allosteric modulators represents a novel direction for therapeutics, which can overcome the selectivity problem caused by the structural similarity of orthosteric sites among deacetylases. Here, developing a reversed allosteric strategy AlloReverse, we identified a cryptic allosteric site, Pocket Z, which was only induced by the bi-directional allosteric signal triggered upon orthosteric binding of NAD⁺. Based on Pocket Z, we discovered an SIRT6 allosteric inhibitor named JYQ-42. JYQ-42 selectively targets SIRT6 among other histone deacetylases and effectively inhibits SIRT6 deacetylation, with an IC₅₀ of 2.33 μmol/L. JYQ-42 significantly suppresses SIRT6-mediated cancer cell migration and pro-inflammatory cytokine production. JYQ-42, to our knowledge, is the most potent and selective allosteric SIRT6 inhibitor. This study provides a novel strategy for allosteric drug design and will help in the challenging development of therapeutic agents that can selectively bind SIRT6.

Dataset for dynamics and conformational changes in human NEIL2 protein analyzed by integrative structural biology approach

This work presents new data on human endonuclease VIII-like 2 protein (hNEIL2), a part of DNA glycosylases of the helix–two-turn–helix structural superfamily. While X-ray structure of oNEIL2 (opossum Monodelphis) was resolved partially [1], the structure of hNEIL2 has not yet been determined. This data article describes a powerful combination of hydrogen-deuterium exchange mass spectrometry, homology modeling, and molecular dynamics simulations for protein conformational dynamics analysis.

The data supplied in this work are related to the research article entitled “Dynamics and Conformational Changes in Human NEIL2 DNA Glycosylase Analyzed by Hydrogen/Deuterium Exchange Mass Spectrometry”.

Mechanism of allosteric activation of SIRT6 revealed by the action of rationally designed activators

The recent discovery of activator compounds binding to an allosteric site on the NAD⁺-dependent protein lysine deacetylase, sirtuin 6 (SIRT6) has attracted interest and presents a pharmaceutical target for aging-related and cancer diseases. However, the mechanism underlying allosteric activation of SIRT6 by the activator MDL-801 remains largely elusive because no major conformational changes are observed upon activator binding. By combining molecular dynamics simulations with biochemical and kinetic analyses of wild-type SIRT6 and its variant M136A, we show that conformational rotation of 2-methyl-4-fluoro-5-bromo substituent on the right phenyl ring (R-ring) of MDL-801, which uncovers previously unseen hydrophobic interactions, contributes to increased activating deacetylation activity of SIRT6. This hypothesis is further supported by the two newly synthesized MDL-801 derivatives through the removal of the 5-Br atom on the R-ring (MDL-801-D1) or the restraint of the rotation of the R-ring (MDL-801-D2). We further propose that the 5-Br atom serves as an allosteric driver that controls the ligand allosteric efficacy. Our study highlights the effect of allosteric enzyme catalytic activity by activator binding and provides a rational approach for enhancing deacetylation activity.

Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods

Purpose

Mutation-induced variation of protein-ligand binding affinity is the key to many genetic diseases and the emergence of drug resistance, and therefore predicting such mutation impacts is of great importance. In this work, we aim to predict the mutation impacts on protein-ligand binding affinity using efficient structure-based, computational methods.

Methods

Relying on consolidated databases of experimentally determined data we characterize the affinity change upon mutation based on a number of local geometrical features and monitor such feature differences upon mutation during molecular dynamics (MD) simulations. The differences are quantified according to average difference, trajectory-wise distance or time-vary differences. Machine-learning methods are employed to predict the mutation impacts using the resulting conventional or time-series features. Predictions based on estimation of energy and based on investigation of molecular descriptors were conducted as benchmarks.

Results

Our method (machine-learning techniques using time-series features) outperformed the benchmark methods, especially in terms of the balanced F1 score. Particularly, deep-learning models led to the best prediction performance with distinct improvements in balanced F1 score and a sustained accuracy.

Conclusion

Our work highlights the effectiveness of the characterization of affinity change upon mutations. Furthermore, deep-learning techniques are well designed for handling the extracted time-series features. This study can lead to a deeper understanding of mutation-induced diseases and resistance, and further guide the development of innovative drug design.

Structural insights into human Arginase-1 pH dependence and its inhibition by the small molecule inhibitor CB-1158

Arginase-1 is a manganese-dependent metalloenzyme that catalyzes the hydrolysis of L-arginine into L-ornithine and urea. Arginase-1 is abundantly expressed by tumor-infiltrating myeloid cells that promote tumor immunosuppression, which is relieved by inhibition of Arginase-1. We have characterized the potencies of the Arginase-1 reference inhibitors (2S)-2-amino-6-boronohexanoic acid (ABH) and N ^ω-hydroxy-nor-L-arginine (nor-NOHA), and studied their pH-dependence and binding kinetics. To gain a better understanding of the structural changes underlying the high pH optimum of Arginase-1 and its pH-dependent inhibition, we determined the crystal structure of the human Arginase-1/ABH complex at pH 7.0 and 9.0. These structures revealed that at increased pH, the manganese cluster assumes a more symmetrical coordination structure, which presumably contributes to its increase in catalytic activity. Furthermore, we show that binding of ABH involves the presence of a sodium ion close to the manganese cluster. We also studied the investigational new drug CB-1158 (INCB001158). This inhibitor has a low-nanomolar potency at pH 7.4 and increases the thermal stability of Arginase-1 more than ABH and nor-NOHA. Moreover, CB-1158 displays slow association and dissociation kinetics at both pH 9.5 and 7.4, as indicated by surface plasmon resonance. The potent character of CB-1158 is presumably due to its increased rigidity compared to ABH as well as the formation of an additional hydrogen-bond network as observed by resolution of the Arginase-1/CB-1158 crystal structure.

Identifying natural compounds as multi-target-directed ligands against Alzheimer's disease: an in silico approach

Alzheimer's disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, β-secretase, monoamine oxidase B, glycogen synthase kinase 3β, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/ . The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database ( https://www.ibscreen.com/natural-compounds ). Further, the natural compounds that were categorized as 'actives' in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.

Structural insights into chemokine CCL17 recognition by antibody M116

The homeostatic chemokine CCL17, also known as thymus and activation regulated chemokine (TARC), has been associated with various diseases such as asthma, idiopathic pulmonary fibrosis, atopic dermatitis and ulcerative colitis. Neutralization of CCL17 by antibody treatment ameliorates the impact of disease by blocking influx of T cells. Monoclonal antibody M116 derived from a combinatorial library shows potency in neutralizing CCL17-induced signaling. To gain insight into the structural determinants of antigen recognition, the crystal structure of M116 Fab was determined in complex with CCL17 and in the unbound form. Comparison of the structures revealed an unusual induced-fit mechanism of antigen recognition that involves cis-trans isomerization in two CDRs. The structure of the CCL17-M116 complex revealed the antibody binding epitope, which does not overlap with the putative receptor epitope, suggesting that the current model of chemokine-receptor interactions, as observed in the CXCR4-vMIP-II system, may not be universal.

Exploring the effect of D61G mutation on SHP2 cause gain of function activity by a molecular dynamics study

Noonan syndrome (NS) is a common autosomal dominant congenital disorder which could cause the congenital cardiopathy and cancer predisposition. Previous studies reported that the knock-in mouse models of the mutant D61G of SHP2 exhibited the major features of NS, which demonstrated that the mutation D61G of SHP2 could cause NS. To explore the effect of D61G mutation on SHP2 and explain the high activity of the mutant, molecular dynamic simulations were performed on wild type (WT) of SHP2 and the mutated SHP2-D61G, respectively. The principal component analysis and dynamic cross-correlation mapping, associated with secondary structure, showed that the D61G mutation affected the motions of two regions (residues Asn 58-Thr 59 and Val 460-His 462) in SHP2 from β to turn. Moreover, the residue interaction networks analysis, the hydrogen bond occupancy analysis and the binding free energies were calculated to gain detailed insight into the influence of the mutant D61G on the two regions, revealing that the major differences between SHP2-WT and SHP2-D61G were the different interactions between Gly 61 and Gly 462, Gly 61 and Ala 461, Gln 506 and Ile 463, Gly 61 and Asn 58, Ile 463 and Thr 466, Gly 462 and Cys 459. Consequently, our findings here may provide knowledge to understand the increased activity of SHP2 caused by the mutant D61G.

The antitumor effect of tanshinone IIA on anti-proliferation and decreasing VEGF/VEGFR2 expression on the human non-small cell lung cancer A549 cell line

The effects of tanshinone IIA on the proliferation of the human non-small cell lung cancer cell line A549 and its possible mechanism on the VEGF/VEGFR signal pathway were investigated. The exploration of the interaction between tanshinone IIA and its target proteins provides a feasible platform for studying the anticancer mechanism of active components of herbs. The CCK-8 assay was used to evaluate the proliferative activity of A549 cells treated with tanshinone IIA (2.5-80 μmol/L) for 24, 48 and 72 h, respectively. Flow cytometry was used for the detection of cell apoptosis and cell cycle perturbation. VEGF and VEGFR2 expression were studied by Western blotting. The binding mode of tanshinone IIA within the crystal structure of the VEGFR2 protein was evaluated with molecular docking analysis by use of the CDOCKER algorithm in Discovery Studio 2.1. The CCK-8 results showed that tanshinone IIA can significantly inhibit A549 cell proliferation in a dose- and time-dependent manner. Flow cytometry results showed that the apoptosis rate of tested group was higher than the vehicle control, and tanshinone IIA-treated cells accumulated at the S phase, which was higher than the vehicle control. Furthermore, the expression of VEGF and VEGFR2 was decreased in Western blot. Finally, molecular docking analysis revealed that tanshinone IIA could be stably docked into the kinase domain of VEGFR2 protein with its unique modes to form H-bonds with Cys917 and π-π stacking interactions with Val848. In conclusion, tanshinone IIA may suppress A549 proliferation, induce apoptosis and cell cycle arrest at the S phase. This drug may suppress angiogenesis by targeting the protein kinase domains of VEGF/VEGFR2.

In silico analysis of consequences of non-synonymous SNPs of Slc11a2 gene in Indian bovines

The aim of our study was to analyze the consequences of non-synonymous SNPs in Slc11a2 gene using bioinformatic tools. There is a current need of efficient bioinformatic tools for in-depth analysis of data generated by the next generation sequencing technologies. SNPs are known to play an imperative role in understanding the genetic basis of many genetic diseases. Slc11a2 is one of the major metal transporter families in mammals and plays a critical role in host defenses. In this study, we performed a comprehensive analysis of the impact of all non-synonymous SNPs in this gene using multiple tools like SIFT, PROVEAN, I-Mutant and PANTHER. Among the total 124 SNPs obtained from amplicon sequencing of Slc11a2 gene by Ion Torrent PGM involving 10 individuals of Gir cattle and Murrah buffalo each, we found 22 non-synonymous. Comparing the prediction of these 4 methods, 5 nsSNPs (G369R, Y374C, A377V, Q385H and N492S) were identified as deleterious. In addition, while tested out for polar interactions with other amino acids in the protein, from above 5, Y374C, Q385H and N492S showed a change in interaction pattern and further confirmed by an increase in total energy after energy minimizations in case of mutant protein compared to the native.

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22 nsSNPs were predicted to decrease the stability of protein based on I-Mutant.

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From these SNPs, 5 was identified as deleterious by SIFT, PROVEAN, and PANTHER.

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Y374C, Q385H and N492S were found to be damaging.

Inhibition of acetylcholinesterase by two genistein derivatives: kinetic analysis, molecular docking and molecular dynamics simulation

In this study two genistein derivatives (G1 and G2) are reported as inhibitors of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE), and differences in the inhibition of AChE are described. Although they differ in structure by a single methyl group, the inhibitory effect of G1 (IC₅₀=264 nmol/L) on AChE was 80 times stronger than that of G2 (IC₅₀=21,210 nmol/L). Enzyme-kinetic analysis, molecular docking and molecular dynamics (MD) simulations were conducted to better understand the molecular basis for this difference. The results obtained by kinetic analysis demonstrated that G1 can interact with both the catalytic active site and peripheral anionic site of AChE. The predicted binding free energies of two complexes calculated by the molecular mechanics/generalized born surface area (MM/GBSA) method were consistent with the experimental data. The analysis of the individual energy terms suggested that a difference between the net electrostatic contributions (ΔE_ele+ΔG_GB) was responsible for the binding affinities of these two inhibitors. Additionally, analysis of the molecular mechanics and MM/GBSA free energy decomposition revealed that the difference between G1 and G2 originated from interactions with Tyr124, Glu292, Val294 and Phe338 of AChE. In conclusion, the results reveal significant differences at the molecular level in the mechanism of inhibition of AChE by these structurally related compounds.

Structural and degradative aspects of ornithine decarboxylase antizyme inhibitor 2

Ornithine decarboxylase (ODC) is the key enzyme in the polyamine biosynthetic pathway. ODC levels are controlled by polyamines through the induction of antizymes (AZs), small proteins that inhibit ODC and target it to proteasomal degradation without ubiquitination. Antizyme inhibitors (AZIN1 and AZIN2) are proteins homologous to ODC that bind to AZs and counteract their negative effect on ODC. Whereas ODC and AZIN1 are well-characterized proteins, little is known on the structure and stability of AZIN2, the lastly discovered member of this regulatory circuit. In this work we first analyzed structural aspects of AZIN2 by combining biochemical and computational approaches. We demonstrated that AZIN2, in contrast to ODC, does not form homodimers, although the predicted tertiary structure of the AZIN2 monomer was similar to that of ODC. Furthermore, we identified conserved residues in the antizyme-binding element, whose substitution drastically affected the capacity of AZIN2 to bind AZ1. On the other hand, we also found that AZIN2 is much more labile than ODC, but it is highly stabilized by its binding to AZs. Interestingly, the administration of the proteasome inhibitor MG132 caused differential effects on the three AZ-binding proteins, having no effect on ODC, preventing the degradation of AZIN1, but unexpectedly increasing the degradation of AZIN2. Inhibitors of the lysosomal function partially prevented the effect of MG132 on AZIN2. These results suggest that the degradation of AZIN2 could be also mediated by an alternative route to that of proteasome. These findings provide new relevant information on this unique regulatory mechanism of polyamine metabolism.