Computational analysis of the binding ability of heterocyclic and conformationally constrained epibatidine analogs in the neuronal nicotinic acetylcholine receptor

One of the most critical issues on the study of ligand–receptor interactions in drug design is the knowledge of the bioactive conformation of the ligand. In this study, we describe a computational approach aimed at estimating the binding ability of epibatidine analogs to interact with the neuronal nicotinic acetylcholine receptor (nAChR) and get insights into the bioactive conformation. The protocol followed consists of a docking analysis and evaluation of pharmacophore parameters of the docked structures. On the basis of the biological data, the results have revealed that the docking analysis is able to predict active ligands, whereas further efforts are needed to develop a suitable and solid pharmacophore model.     

CORCEMA refinement of the bound ligand conformation within the protein binding pocket in reversibly forming weak complexes using STD-NMR intensities

We describe an intensity-restrained optimization procedure for refining approximate structures of ligands within the protein binding pockets using STD-NMR intensity data on reversibly forming weak complexes. In this approach, the global minimum for the bound-ligand conformation is obtained by a hybrid structure refinement method involving CORCEMA calculation of intensities and simulated annealing optimization of torsion angles of the bound ligand using STD-NMR intensities as experimental constraints and the NOE R-factor as the pseudo-energy function to be minimized. This method is illustrated using simulated STD data sets for typical carbohydrate and peptide ligands. Our procedure also allows for the optimization of side chain torsion angles of protein residues within the binding pocket. This procedure is useful in refining and improving initial models based on crystallography or computer docking or other algorithms to generate models for the bound ligand (e.g., a lead compound) within the protein binding pocket compatible with solution STD-NMR data. This method may facilitate structure-based drug design efforts.     

Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands

A novel heuristic using an iterative select-and-purge strategy is proposed. It combines statistical techniques for sampling and classification by rigid molecular docking through an inverse virtual screening scheme. This approach aims to the de novo discovery of short peptides that may act as docking receptors for small target molecules when there are no data available about known association complexes between them. The algorithm performs an unbiased stochastic exploration of the sample space, acting as a binary classifier when analyzing the entire peptides population. It uses a novel and effective criterion for weighting the likelihood of a given peptide to form an association complex with a particular ligand molecule based on amino acid sequences. The exploratory analysis relies on chemical information of peptides composition, sequence patterns, and association free energies (docking scores) in order to converge to those peptides forming the association complexes with higher affinities. Statistical estimations support these results providing an association probability by improving predictions accuracy even in cases where only a fraction of all possible combinations are sampled. False positives/false negatives ratio was also improved with this method. A simple rigid-body docking approach together with the proper information about amino acid sequences was used. The methodology was applied in a retrospective docking study to all 8000 possible tripeptide combinations using the 20 natural amino acids, screened against a training set of 77 different ligands with diverse functional groups. Afterward, all tripeptides were screened against a test set of 82 ligands, also containing different functional groups. Results show that our integrated methodology is capable of finding a representative group of the top-scoring tripeptides. The associated probability of identifying the best receptor or a group of the top-ranked receptors is more than double and about 10 times higher, respectively, when compared to classical random sampling methods.     

Minimum Spanning vs. Principal Trees for Structured Approximations of Multi-Dimensional Datasets

Construction of graph-based approximations for multi-dimensional data point clouds is widely used in a variety of areas. Notable examples of applications of such approximators are cellular trajectory inference in single-cell data analysis, analysis of clinical trajectories from synchronic datasets, and skeletonization of images. Several methods have been proposed to construct such approximating graphs, with some based on computation of minimum spanning trees and some based on principal graphs generalizing principal curves. In this article we propose a methodology to compare and benchmark these two graph-based data approximation approaches, as well as to define their hyperparameters. The main idea is to avoid comparing graphs directly, but at first to induce clustering of the data point cloud from the graph approximation and, secondly, to use well-established methods to compare and score the data cloud partitioning induced by the graphs. In particular, mutual information-based approaches prove to be useful in this context. The induced clustering is based on decomposing a graph into non-branching segments, and then clustering the data point cloud by the nearest segment. Such a method allows efficient comparison of graph-based data approximations of arbitrary topology and complexity. The method is implemented in Python using the standard scikit-learn library which provides high speed and efficiency. As a demonstration of the methodology we analyse and compare graph-based data approximation methods using synthetic as well as real-life single cell datasets.     

一种基于无监督主动学习的苹果品质光谱无损检测模型构建方法

利用光谱技术实现农产品、食品品质无损检测的实质是建立样本光谱信息与样本品质参数之间的机器学习模型。为了获得具有良好泛化性能的机器学习模型,通常需要大量的标记样本,然而,获取样本的光谱信息相对容易,但标注样本品质参数的过程往往涉及到大量的时间和经济成本,并且具有破坏性。主动学习是一种减少训练集有标记样本数量的方法,通过选择最有价值的样本进行标记,而不是随机选择。因此,主动学习能够控制向训练集添加哪些样本,模型不再是被动地接受用于建模的样本。在分类任务中已经提出较多关于主动学习的算法,但回归任务中的研究却相对较少,且现有的用于回归任务的主动学习算法大多是有监督的,即需要少量有标记样本训练初始模型。本文提出了一种基于无监督主动学习方法的训练样本选择策略。该方法首先通过层次凝聚聚类对无标记（标准值）光谱数据集进行多样性划分,获得不同的聚类簇;然后通过局部线性重建算法在每个聚类簇中选择最具代表性的样本构成训练样本集,最后基于训练集构建模型。利用两个年份三个品种苹果的近红外光谱数据,构建了其可溶性固形物含量和硬度的偏最小二乘预测模型,用于验证所提出方法的有效性。实验结果表明：所提出的方法要优于已有的样本选择策略,可以有效地提高模型精度,减少在模型训练中的破坏性理化实验。同时,与随机采样（RS）、Kennard-Stone算法（KS）、光谱-理化值共生距离算法（SPXY）这三种光谱领域常用的样本选择算法相比,该研究所提出的方法表现出了最佳的性能, 基于所提出的无监督主动学习算法选取200个样本作为训练集所建立的可溶性固形物含量预测模型的预测均方根误差相对于其他三种算法降低了2.0%~13.2%,硬度预测模型的预测均方根误差相对降低了1.2%~15.7%。     

Failure tolerance of spike phase synchronization in coupled neural networks

Neuronal synchronization plays an important role in the various functionality of nervous system such as binding, cognition, information processing, and computation. In this paper, we investigated how random and intentional failures in the nodes of a network influence its phase synchronization properties. We considered both artificially constructed networks using models such as preferential attachment, Watts-Strogatz, and Erdo?s-Re?nyi as well as a number of real neuronal networks. The failure strategy was either random or intentional based on properties of the nodes such as degree, clustering coefficient, betweenness centrality, and vulnerability. Hindmarsh-Rose model was considered as the mathematical model for the individual neurons, and the phase synchronization of the spike trains was monitored as a function of the percentage∕number of removed nodes. The numerical simulations were supplemented by considering coupled non-identical Kuramoto oscillators. Failures based on the clustering coefficient, i.e., removing the nodes with high values of the clustering coefficient, had the least effect on the spike synchrony in all of the networks. This was followed by errors where the nodes were removed randomly. However, the behavior of the other three attack strategies was not uniform across the networks, and different strategies were the most influential in different network structure.     

Binding properties of SH3 peptide ligands identified from phage-displayed random peptide libraries

Summary Combinatorial libraries have yielded high-affinity ligands for SH3 domains of a number of different proteins. We have shown that synthetic peptides containing these SH3 ligand sequences serve as specific probes of SH3 domains. Direct binding of the N-terminal biotinylated peptide ligands was conveniently detected in ELISA, filter-blotting, and dot-blotting experiments with the use of streptavidin-conjugated enzymes. In some cases, detection of peptide-SH3 interactions required that the biotinylated peptides first were preconjugated with streptavidin to form a multivalent complex. Interestingly, these nominally tetravalent SH3 peptide ligands cross-react to varying degrees with different SH3 domains. We have used such complexes to screen cDNA expression libraries and have isolated clones that encode both known and novel SH3-domain-containing proteins. Based on the success of this methodology, we propose a general strategy by which ligands of a modular domain-containing protein can be isolated from random peptide libraries and used to screen cDNA expression libraries systematically for novel modular domain-containing proteins.     

Docking accuracy enhanced by QM-derived protein charges

Effects of protein polari sation on docking accuracy were investigated using molecular docking programme AutoDock 4 in which topology-specific empirical Gasteiger charges were replaced with Polarised protein-specific charges (PPC) to represent quantum mechanics- polarised protein. Docking was successfully conducted for 50 diverse protein–ligand complexes. The docking with PPC charges shows a decrease in the root-mean-square deviation (RMSD) values of ligands compared to those from the docking with Gasteiger charges. Ligand binding orientations and their key interactions such as hydrogen bonding interactions in X-ray structures were substantially reproduced in complexes docked using PPC scheme with 98% of the RMSDs of the best docking poses less than 2 Å compared to 74% in the docking with Gasteiger charges. Considerable improvements in docking accuracy were observed by simply altering the atomic partial charges in the scoring function, which reflects the importance of protein polarisation in molecular docking. Further research can be carried out to (1) include polarisation of both ligands and proteins to account for polarisation effects within protein and between protein and ligand, and (2) develop a PPC-based scoring function to increase the docking accuracies for protein–ligand complexes from a larger variety of protein families.     

A new anisotropic mesh adaptation method based upon hierarchical a posteriori error estimates

A new anisotropic mesh adaptation strategy for finite element solution of elliptic differential equations is presented. It generates anisotropic adaptive meshes as quasi-uniform ones in some metric space, with the metric tensor being computed based on hierarchical a posteriori error estimates. A global hierarchical error estimate is employed in this study to obtain reliable directional information of the solution. Instead of solving the global error problem exactly, which is costly in general, we solve it iteratively using the symmetric Gauß–Seidel method. Numerical results show that a few GS iterations are sufficient for obtaining a reasonably good approximation to the error for use in anisotropic mesh adaptation. The new method is compared with several strategies using local error estimators or recovered Hessians. Numerical results are presented for a selection of test examples and a mathematical model for heat conduction in a thermal battery with large orthotropic jumps in the material coefficients.     

Shape measurement by a multi-view methodology based on the remote tracking of a 3D optical scanner

Full field optical techniques can be reliably used for 3D measurements of complex shapes by multi-view processes, which require the computation of transformation parameters relating different views into a common reference system. Although, several multi-view approaches have been proposed, the alignment process is still the crucial step of a shape reconstruction.In this paper, a methodology to automatically align 3D views has been developed by integrating a stereo vision system and a full field optical scanner. In particular, the stereo vision system is used to remotely track the optical scanner within a working volume. The tracking system uses stereo images to detect the 3D coordinates of retro-reflective infrared markers rigidly connected to the scanner. Stereo correspondences are established by a robust methodology based on combining the epipolar geometry with an image spatial transformation constraint.The proposed methodology has been validated by experimental tests regarding both the evaluation of the measurement accuracy and the 3D reconstruction of an industrial shape.     

Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands

The Average Information Content Maximization algorithm (AIC-MAX) based on mutual information maximization was recently introduced to select the most discriminatory features. Here, this methodology was applied to select the most significant bits from the Klekota-Roth fingerprint for serotonin receptors ligands as well as to select the most important features for distinguishing ligands with activity for one receptor versus another. The interpretation of selected bits and machine-learning experiments performed using the reduced interpretations outperformed the raw fingerprints and indicated the most important structural features of the analyzed ligands in terms of activity and selectivity. Moreover, the AIC-MAX methodology applied here for serotonin receptor ligands can also be applied to other target classes.     

Clustering of financial time series with application to index and enhanced index tracking portfolio

A stochastic-optimization technique based on time series cluster analysis is described for index tracking and enhanced index tracking problems. Our methodology solves the problem in two steps, i.e., by first selecting a subset of stocks and then setting the weight of each stock as a result of an optimization process (asset allocation). Present formulation takes into account constraints on the number of stocks and on the fraction of capital invested in each of them, whilst not including transaction costs. Computational results based on clustering selection are compared to those of random techniques and show the importance of clustering in noise reduction and robust forecasting applications, in particular for enhanced index tracking.     

球形阵列自适应网格分层定位算法

刚性球形传声器阵列可以在无空间模糊的条件下进行球谐域数据处理,基于球形阵列的可控波束形成器导向响应功率(SRP)算法定位精度高,但是计算量大,计算效率低。通过将球面致密的全局网格搜索替换为分层搜索策略可以有效减小SRP算法的计算量。提出MRE-SRP算法保持球形阵列SRP定位精度的同时降低计算量,首先通过球谐域MUSIC(SH-MUSIC)算法判断入射声源的数量减小搜索区域;其次将相对熵模型引入球谐域SRP(SH-SRP)定位算法中,提取网格分层前后的信息增益,设计自适应网格选择判据,实现分层多分辨率网格的精准再细分,从而降低计算量。实验验证了所提出算法的性能,结果显示在单双声源定位中,该算法可以实现较高的定位精度,精准选择分层网格,计算量减少75%以上。     

Modular and hierarchical structure of social contact networks

Social contact networks exhibit overlapping qualities of communities, hierarchical structure and spatial-correlated nature. We propose a mixing pattern of modular and growing hierarchical structures to reconstruct social contact networks by using an individual’s geospatial distribution information in the real world. The hierarchical structure of social contact networks is defined based on the spatial distance between individuals, and edges among individuals are added in turn from the modular layer to the highest layer. It is a gradual process to construct the hierarchical structure: from the basic modular model up to the global network. The proposed model not only shows hierarchically increasing degree distribution and large clustering coefficients in communities, but also exhibits spatial clustering features of individual distributions. As an evaluation of the method, we reconstruct a hierarchical contact network based on the investigation data of a university. Transmission experiments of influenza H1N1 are carried out on the generated social contact networks, and results show that the constructed network is efficient to reproduce the dynamic process of an outbreak and evaluate interventions. The reproduced spread process exhibits that the spatial clustering of infection is accordant with the clustering of network topology. Moreover, the effect of individual topological character on the spread of influenza is analyzed, and the experiment results indicate that the spread is limited by individual daily contact patterns and local clustering topology rather than individual degree.     

The effect of protein internal cavities on ligand migration and binding in myoglobin

Using Fourier Transform Infrared Spectroscopy at cryogenic temperatures, we have studied carbon monoxide (CO) migration in the interior of sperm whale myoglobin and binding to teh heme iron. The effect of protein internal cavities was examined by comparing the wild-type protein with mutants in which the cavities were blocked by bulky amino acid sidechains. After photodissociation at 3 K, CO ligands reside in the primary docking site (B) from where they can migrate to other sites (C, D) that were identified as the Xe4 and Xe1 cavities. These studies were complemented by flash photolysis experiments at room temperature, which revealed that the protein cavities enable efficient excape of ligands from the protein after dissociation from the heme iron.