Fabrication of Defined Polydopamine Nanostructures by DNA Origami‐Templated Polymerization

A versatile, bottom‐up approach allows the controlled fabrication of polydopamine (PD) nanostructures on DNA origami. PD is a biosynthetic polymer that has been investigated as an adhesive and promising surface coating material. However, the control of dopamine polymerization is challenged by the multistage‐mediated reaction mechanism and diverse chemical structures in PD. DNA origami decorated with multiple horseradish peroxidase‐mimicking DNAzyme motifs was used to control the shape and size of PD formation with nanometer resolution. These fabricated PD nanostructures can serve as “supramolecular glue” for controlling DNA origami conformations. Facile liberation of the PD nanostructures from the DNA origami templates has been achieved in acidic medium. This presented DNA origami‐controlled polymerization of a highly crosslinked polymer provides a unique access towards anisotropic PD architectures with distinct shapes that were retained even in the absence of the DNA origami template.     

Enabling the exploration of biochemical pathways

The Biochemical Pathways Wall Chart (http://www.expasy.org/tools/pathways/ref.1) has been converted into a molecule and reaction database. Major features of this database are that each molecule is represented by lists of all atoms and bonds (as connection tables), and in the reactions the reaction centre, the atoms and bonds directly involved in the bond rearrangement process, are marked. The information in the database has been enriched by a set of diverse 3D structure conformations generated by the programs CORINA and ROTATE. The web-based structure and reaction retrieval system C@ROL provides a wide range of search methods to mine this rich database. The database is accessible at http://www2.chemie.uni-erlangen.de/services/biopath/index.html and http://www.mol-net.de/databases/biopath.html .     

Designing novel nicotinic agonists by searching a database of molecular shapes

Summary We introduce an approach by which novel ligands can be designed for a receptor if a pharmacophore geometry has been established and the receptor-bound conformations of other ligands are known. We use the shape-matching method of Kuntz et al. [J. Mol. Biol., 161 (1982) 269–288] to search a database of molecular shapes for those molecules which can fit inside the combined volume of the known ligands and which have interatomic distances compatible with the pharmacophore geometry. Some of these molecules are then modified by interactive modeling techniques to better match the chemical properties of the known ligands. Our shape database (about 5000 candidate molecules) is derived from a subset of the Cambridge Crystallographic Database [Allen et al., Acta Crystallogr., Sect. B,35 (1979) 2331–2339]. We show, as an example, how several novel designs for nicotinic agonists can be derived by this approach, given a pharmacophore model derived from known agonists [Sheridan et al., J. Med. Chem., 29 (1986) 889–906]. This report complements our previous report [DesJarlais et al., J. Med. Chem., in press], which introduced a similar method for designing ligands when the structure of the receptor is known.     

基于分子对接和QSAR方法预测B-Raf II型抑制剂活性

B-Raf激酶在促分裂素原活化蛋白激酶(MAPK)信号转导通路中起着重要作用,已被确定为癌症治疗非常有吸引力的靶标.新型高效B-Raf抑制剂的开发成为癌症治疗的一个热门研究领域.本文以结构多样的B-Raf II型抑制剂为研究对象,联合应用分子对接和定量构效关系(QSAR)模型研究其定量构效关系去探讨抑制活性的起源.两个主题作为研究重点:生物活性构象和描述符.首先对分子对接方法(Glide、Gold、LigandFit、Cdocker和Libdock)进行准确性评价,后将研究的对象分子对接到B-Raf活性位点并获得生物活性构象.基于准确的对接结果,计算得到16个打分评价函数和21个能量描述符,以此构建定量构效关系模型. QSAR结果表明模型具有高度精确的拟合和强的预测能力(模型M1: r² = 0.852, r_(CV)² = 0.790, r_pre² = 0.864;模型M2: r² = 0.738, r_(CV)² = 0.812, r_pre² = 0.8605).同时探讨了对抑制活性有重要影响的描述符,结果表明打分评价函数(G_Score, -ECD, Dock_Score, PMF)与能量描述符(S(hb_ext), DE(int), Emodel)对抑制活性影响非常大.通过虚拟筛选和QSAR模型理论预测,一些新的具有潜在抑制活性的化合物作为B-Raf II型抑制剂被获得.上述信息对于进一步设计新颖高效的B-Raf II型抑制剂提供了有用的指导.     

Efficient and sample-specific interpretation of ToF-SIMS data by additional postprocessing of principal component analysis results

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful tool for surface analysis, but fragmentation of molecular species during the SIMS process may lead to complex mass spectra. While the fragmentation pattern is typically characteristic for each compound, industrial samples are engineered materials, and, thus, may contain a mixture of many compounds, which may result in a variety of overlapping peak patterns in ToF-SIMS spectra. Consequently, the process of data evaluation is challenging and time-consuming. Principal component analysis (PCA) can be used to simplify data analysis for complex sample systems. Especially, correlation loadings were observed as an ideal tool to identify relevant signals in PCA results, which induce the separation of different sample groups. This is because correlation loadings show the relevance of signals independent from their intensity in the raw data. In correlation loadings, however, fragmentation patterns are no longer observed and the identification of peaks' sum formulas is challenging. In this study, a new approach is presented, which simplifies peak identification and assignment in ToF-SIMS spectra after PCA is performed. The approach uses a mathematical transformation that projects PCA results, in particular loadings and correlation loadings, in the direction of specific sample groups. The approach does not change PCA results but rather presents them in a new way. This method allows to visualize characteristic spectra for specific sample groups that contain only relevant signals and, additionally, visualize fragmentation patterns. Data analysis is simplified and helps the user to focus on data interpretation rather than processing.     

Using consensus-shape clustering to identify promiscuous ligands and protein targets and to choose the right query for shape-based virtual screening

Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, many authors have discussed how best to choose the initial query compounds and which of their conformations should be used. Furthermore, it is increasingly the case that pharmaceutical companies have multiple ligands for a given target and these may bind in different ways to the same pocket. Conversely, a given ligand can sometimes bind to multiple targets, and this is clearly of great importance when considering drug side-effects. We recently introduced the notion of spherical harmonic-based "consensus shapes" to help deal with these questions. Here, we apply a consensus shape clustering approach to the 40 protein-ligand targets in the DUD data set using PARASURF/PARAFIT. Results from clustering show that in some cases the ligands for a given target are split into two subgroups which could suggest they bind to different subsites of the same target. In other cases, our clustering approach sometimes groups together ligands from different targets, and this suggests that those ligands could bind to the same targets. Hence spherical harmonic-based clustering can rapidly give cross-docking information while avoiding the expense of performing all-against-all docking calculations. We also report on the effect of the query conformation on the performance of shape-based screening of the DUD data set and the potential gain in screening performance by using consensus shapes calculated in different ways. We provide details of our analysis of shape-based screening using both PARASURF/PARAFIT and ROCS, and we compare the results obtained with shape-based and conventional docking approaches using MSSH/SHEF and GOLD. The utility of each type of query is analyzed using commonly reported statistics such as enrichment factors (EF) and receiver-operator-characteristic (ROC) plots as well as other early performance metrics.     

Comparative Modelling of Organic Anion Transporting Polypeptides: Structural Insights and Comparison of Binding Modes

To better understand the functionality of organic anion transporting polypeptides (OATPs) and to design new ligands, reliable structural data of each OATP is needed. In this work, we used a combination of homology model with molecular dynamics simulations to generate a comprehensive structural dataset, that encompasses a diverse set of OATPs but also their relevant conformations. Our OATP models share a conserved transmembrane helix folding harbouring a druggable binding pocket in the shape of an inner pore. Our simulations suggest that the conserved salt bridges at the extracellular region between residues on TM1 and TM7 might influence the entrance of substrates. Interactions between residues on TM1 and TM4 within OATP1 family shown their importance in transport of substrates. Additionally, in transmembrane (TM) 1/2, a known conserved element, interact with two identified motifs in the TM7 and TM11. Our simulations suggest that TM1/2-TM7 interaction influence the inner pocket accessibility, while TM1/2-TM11 salt bridges control the substrate binding stability.