Genomic variation reveals demographic history and biological adaptation of the ancient relictual, lotus (Nelumbo Adans).

Lotus (Nelumbo Adans.), a relict plant, is the testimony of long-term sustained ecological success, but the underlying genetic changes related to its survival strategy remains unclear. Here, we assembled the high-quality lotus genome, investigated genome variation of lotus mutation accumulation (MA) lines and reconstructed the demographic history of wild Asian lotus, respectively. We identified and validated 43 base substitutions fixed in MA lines, implying a spontaneous mutation rate of 1.4 × 10-9 base/generation in lotus shoot stem cells. The past history of lotus revealed that the ancestors of lotus in eastern and southern Asia could be traced back ~20 million years ago (Mya) and experienced twice significant bottlenecks and population splits. We further identified the selected genes among three lotus groups in different habitats, suggesting that 453 genes between tropical and temperate group and 410 genes between two subgroups from Northeastern China and the Yangtze River - Yellow River Basin might play important roles in natural selection in lotus's adaptation and resilience. Our findings not only improve an understanding of the lotus evolutionary history and the genetic basis of its survival advantages, but also provide valuable data for addressing various questions in evolution and protection for the relict plants.

Genetic Diversity and DNA Fingerprints of Three Important Aquatic Vegetables by EST-SSR Markers.

Twenty-two sacred lotus (Nelumbo nucifera), 46 taros (Colocasia esculenta) and 10 arrowheads (Sagittaria trifolia) were used as materials and combined with EST-SSR (expressed sequence tag-simple sequence repeats) primers developed by our laboratory. Core primers were screened from a large number of primers that were able to distinguish all materials with a high frequency of polymorphisms. Six pairs, twenty pairs and three pairs of core primers were screened from sacred lotus, taro, and arrowhead, respectively. The SSR fingerprints of these three important aquatic vegetables, producing 17-, 87- and 14-bit binary molecular identity cards, respectively, were separately determined by using the core primers. Since there were few core primers of sacred lotus and arrowhead, 3 and 9 primer pairs with higher polymorphic information content (PIC), respectively, were selected as candidate primers. These core and candidate primers were used to identify the purities of No.36 space lotus, Shandong 8502 taro and Wuhan arrowhead, which were 93.3% (84/90), 98.9% (89/90) and 100.0% (90/90), respectively. The fingerprints, displayed as binary molecular identification cards of three important aquatic vegetables, were obtained, and their purity was successfully determined with EST-SSR labeling technology. Phylogenetic trees were also constructed to analyze the genetic diversity of 22 sacred lotus, 46 taros and 10 arrowheads. This study classifies and identifies germplasm resources and is an important reference to test the authenticity and variety purity of other aquatic vegetables in the future.

Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds.

The discovery of novel scaffolds against a specific target has long been one of the most significant but challengeable goals in discovering lead compounds. A scaffold that binds in important regions of the active pocket is more favorable as a starting point because scaffolds generally possess greater optimization possibilities. However, due to the lack of sufficient chemical space diversity of the databases and the ineffectiveness of the screening methods, it still remains a great challenge to discover novel active scaffolds. Since the strengths and weaknesses of both fragment-based drug design and traditional virtual screening (VS), we proposed a fragment VS concept based on Bayesian categorization for the discovery of novel scaffolds. This work investigated the proposal through an application on VEGFR-2 target. Firstly, scaffold and structural diversity of chemical space for 10 compound databases were explicitly evaluated. Simultaneously, a robust Bayesian classification model was constructed for screening not only compound databases but also their corresponding fragment databases. Although analysis of the scaffold diversity demonstrated a very unevenly distribution of scaffolds over molecules, results showed that our Bayesian model behaved better in screening fragments than molecules. Through a literature retrospective research, several generated fragments with relatively high Bayesian scores indeed exhibit VEGFR-2 biological activity, which strongly proved the effectiveness of fragment VS based on Bayesian categorization models. This investigation of Bayesian-based fragment VS can further emphasize the necessity for enrichment of compound databases employed in lead discovery by amplifying the diversity of databases with novel structures.

Insight into the key interactions of bromodomain inhibitors based on molecular docking, interaction fingerprinting, molecular dynamics and binding free energy calculation.

The bromodomain is a key protein-protein interaction module that specifically reads the acetylation marks of histones in epigenetic regulation. Currently, lots of inhibitors targeting the bromodomain have been reported as therapeutic agents. To better understand the interaction mechanism of bromodomain inhibitors, 20 diverse bromodomain inhibitors were studied using a combination of computational methods, including molecular docking, interaction fingerprinting, molecular dynamics simulation and binding free energy calculation. As a result, interactions important for the activity were critically analyzed, and the energy contribution in terms of individual residues was explored. These integrated results provided insights into two hot spots in the active site of the bromodomain, where the hydrophobic hot spot formed by Trp81, Val87, Leu92 and Ile146 played a central role in the interaction, and the hydrogen-bond hot spot mediated by Asn140 exhibited a moderate contribution to the binding affinity of the bromodomain inhibitors. This interaction mechanism study may facilitate the rational design of novel small-molecule bromodomain inhibitors.

A selectivity study of sodium-dependent glucose cotransporter 2/sodium-dependent glucose cotransporter 1 inhibitors by molecular modeling.

Sodium-dependent glucose cotransporters (SGLTs) play an important role in glucose reabsorption in the kidney and have been identified as promising targets to treat diabetes. Because of the side effects like glucose and galactose malabsorption by targeting SGLT1, highly selective SGLT2 inhibitors are more promising in the treatment of diabetes. To understand the mechanism of selectivity, we conducted selectivity-based three-dimensional quantitative structure-activity relationship studies to highlight the structure requirements for highly selective SGLT2 inhibitors. The best comparative molecular field analysis and comparative molecular similarity indices analysis models showed the noncross-validated coefficient (r(2) ) of 0.967 and 0.943, respectively. The predicted correlation coefficients (r(2) pred ) of 0.974 and 0.938 validated the reliability and predictability of these models. Besides, homology models of SGLT2 and SGLT1 were also constructed to investigate the selective mechanism from structure-based perspective. Molecular dynamics simulation and binding free energy calculation were performed on the systems of a potent and selective compound interacting with SGLT2 and SGLT1 to compare the different binding modes. The simulation results showed that the stretch of the methylthio group on Met241 had an essential effect on the different binding modes between SGLT1 and SGLT2, which was consistent with the three-dimensional quantitative structure-activity relationship analysis. Hydrogen bond analysis and binding free energy calculation revealed that SGLT2 binding complex was more stable and favorable than SGLT1 complex, which was highly correlated with the experimental results. Our obtained results give useful information for the investigation of the inhibitors' selectivity between SGLT2 and SGLT1 and will help for further development of highly selective SGLT2 inhibitors.

[Analysis of Applying Chinese Medical Clinical Pathway for Treating Attention-deficit Hyperactivity Disorder].

OBJECTIVE: To evaluate the application effect of Chinese medical clinical pathway for treating attention-deficit hyperactivity disorder (ADHD), and to provide evidence for further improving clinical pathways. METHODS: Totally 270 ADHD children patients were recruited and treated at pediatrics clinics of 9 cooperative hospitals from December 2011 to December 2012. The treatment course for all was 3 months. Scores of attention deficit and hyperactivity rating scale, scores of behavior, Conners index of hyperactivity (CIH), and Chinese medical syndrome scores were compared between before and after treatment. The efficacy difference in various sexes, ages, and disease courses were evaluated by judging standards for Chinese medical syndrome and ADHD. RESULTS: Fifteen children patients who entered clinical pathway dropped out, and the rest 255 completed this trial. Compared with before treatment, total scores of attention deficit and hyperactivity rating scale, scores of attention deficit and hyperactivity rating scale, CIH, and Chinese medical syndrome scores obviously decreased (all P < 0.01). The total effective rate in disease efficacy was 87.8% (224/255 cases), and the total effective rate in Chinese medical syndrome curative effect was 87.5% (223/255 cases). The clinical curative effect was not influenced by age, gender, or course of disease when statistically analyzed from judging standards for Chinese medical syndrome or for disease efficacy. CONCLUSION: Intervention by Chinese medical clinical pathway could improve ADHD patients' symptoms, and its efficacy was not influenced by sex, age, or course of disease.

Molecular modeling of exquisitely selective c-Met inhibitors through 3D-QSAR and molecular dynamics simulations.

c-Met has been considered as an attractive target for developing antitumor agents. The highly selective c-Met inhibitors provide invaluable opportunities for the combination with other therapies safely to achieve the optimal efficacy. In this work, a series of triazolopyrazine c-Met inhibitors with exquisitely selectivity were investigated using a combination of molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR), and molecular dynamics simulation. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models were developed to reveal the structural determinants for c-Met inhibition. Both models were validated to have high reliability and predictability, and contour map analysis suggested feature requirements for different substituents on the scaffold. It is worth noting that an important hydrogen bond rich region was identified in the unique narrow channel, which is distinct from other kinases. Molecular dynamics simulations and binding free energy calculations provided further support that suitable groups in this hydrogen bond rich region made great contributions to the binding of ligands. Moreover, hydrogen bonds with residues of the narrow channel were also indicated to be essential to improve the activity and selectivity. This study will facilitate the discovery and optimization of novel c-Met inhibitors with higher activity and selectivity.

An efficient multistep ligand-based virtual screening approach for GPR40 agonists.

G protein-coupled receptor 40/free fatty acid receptor 1 (GPR40/FFAR1) is a member of the GPCR superfamily, and GPR40 agonists have therapeutic potential for type 2 diabetes. With the crystal structure of GPR40 currently unavailable, various ligand-based virtual screening approaches can be applied to identify novel agonists of GPR40. It is known that each ligand-based method has its own advantages and limitations. To improve the efficiency of individual ligand-based methods, an efficient multistep ligand-based virtual screening approach is presented in this study, including the pharmacophore-based screening, physicochemical property filtering, protein-ligand interaction fingerprint similarity analysis, and 2D-fingerprint structural similarity search. A focused decoy library was generated and used to evaluate the efficiency of this virtual screening protocol. This multistep workflow not only significantly improved the hit rate compared with each individual ligand-based method, but also identified diverse known actives from decoys. This protocol may serve as an efficient virtual screening tool for the targets without crystal structures available to discover novel active compounds.

An integrated virtual screening approach for VEGFR-2 inhibitors.

In recent years, various virtual screening (VS) tools have been developed, and many successful screening campaigns have been showcased. However, whether by conventional molecular docking or pharmacophore screening, the selection of virtual hits is based on the ranking of compounds by scoring functions or fit values, which remains the bottleneck of VS due to insufficient accuracy. As the limitations of individual methods persist, a comprehensive comparison and integration of different methods may provide insights into selecting suitable methods for VS. Here, we evaluated the performance of molecular docking, fingerprint-based 2D similarity and multicomplex pharmacophore in an individual and a combined manner, through a retrospective VS study on VEGFR-2 inhibitors. An integrated two-layer workflow was developed and validated through VS of VEGFR-2 inhibitors against the DUD-E database, which demonstrated improved VS performance through a ligand-based method ECFP_4, followed by molecular docking, and then a strict multicomplex pharmacophore. Through a retrospective comparison with six published papers, this integrated approach outperformed 43 out of 45 methods, indicating a great effectiveness. This kind of integrated VS approach can be extended to other targets for the screening and discovery of inhibitors.

Fragment-based strategy for structural optimization in combination with 3D-QSAR.

Fragment-based drug design has emerged as an important methodology for lead discovery and drug design. Different with other studies focused on fragment library design and active fragment identification, a fragment-based strategy was developed in combination with three-dimensional quantitative structure-activity relationship (3D-QSAR) for structural optimization in this study. Based on a validated scaffold or fragment hit, a series of structural optimization was conducted to convert it to lead compounds, including 3D-QSAR modelling, active site analysis, fragment-based structural optimization and evaluation of new molecules. 3D-QSAR models and active site analysis provided sufficient information for confirming the SAR and pharmacophoric features for fragments. This strategy was evaluated through the structural optimization on a c-Met inhibitor scaffold 5H-benzo[4,5]cyclohepta[1,2-b]pyridin-5-one, which resulted in an c-Met inhibitor with high inhibitory activity. Our study suggested the effectiveness of this fragment-based strategy and the druggability of our newly explored active region. The reliability of this strategy indicated it could also be applied to facilitate lead optimization of other targets.