Taba: A Tool to Analyze the Binding Affinity

Evaluation of ligand-binding affinity using the atomic coordinates of a protein-ligand complex is a challenge from the computational point of view. The availability of crystallographic structures of complexes with binding affinity data opens the possibility to create machine-learning models targeted to a specific protein system. Here, we describe a new methodology that combines a mass-spring system approach with supervised machine-learning techniques to predict the binding affinity of protein-ligand complexes. The combination of these techniques allows exploring the scoring function space, generating a model targeted to a protein system of interest. The new model shows superior predictive performance when compared with classical scoring functions implemented in the programs Molegro Virtual Docker, AutoDock4, and AutoDock Vina. We implemented this methodology in a new program named Taba. Taba is implemented in Python and available to download under the GNU license at https://github.com/azevedolab/taba . © 2019 Wiley Periodicals, Inc.     

Emission Control by Molecular Manipulation of Double‐Paddled Binuclear PtII Complexes at the Air‐Water Interface

Molecular functions depend on conformations and motions of the corresponding molecular species. An air–water interface is a suitable asymmetric field for the control of molecular conformations and motions under a small applied force. In this work, double‐paddled binuclear Pt^II complexes containing pyrazole rings linked by alkyl spacers were synthesized and their orientations and emission properties dynamically manipulated at the air–water interface. The complexes emerge from water with concurrent variation of interface orientation of the planes of the Pt^II complexes from perpendicular to parallel during mechanical compression suggesting a unique ‘submarine emission‘. Phosphorescence of the complexes is quenched at the air–water interface prior to monolayer formation with intensities subsequently rapidly increasing during monolayer compression. These results indicate that asymmetric reactions and motions might be controlled by applying mechanical force at the air–water interface.     

Bounds on absorption times of directionally biased random sequences

A sequence of random variables X₀,X₁, … with values in {0, 1, …, n} representing a general finite-state stochastic process with absorbing state 0 is said to be directionally biased towards 0, if, for all j > 0, ϵ_j: = inf_k>0 {j − E[X_k | X_k−1 = j]} > 0. For such sequences, let t be the expected value of the time to absorption at 0. For a fixed set of biases, the least upper bound for this time can be computed with an algorithm requiring O(n²) steps. Simple upper bounds are described. In particular, t ≤ E[b_x0], where b_i = Σ_j≤i 1/¯ϵ_j and ¯ϵ_j = min_l≥j {ϵ_l}. If all ϵ_j ≤ ϵ_{j + 1} (so ¯ϵ_j = ϵ_j) and ϵ_n < 1, this bound for t is the best possible. For certain finite stochastic processes which we term conditionally independent of X₀ = i, b(i) bounds the expected time given X₀ = i. Similar results are given for lower bounds. The results of this paper were designed to be a useful tool for determining rates of convergence of stochastic optimization algorithms. © 1996 John Wiley & Sons, Inc.     

用支持向量机网络实现VBR视频通信量的预测

VBR(Variable Bit Rate)视频信号具有时变性、非线性和突发性等特点,实现该信号通信量的高精度预测是提高信息传输速度和提高网络带宽资源利用效率的重要手段.针对以上问题,本文提出了一种用于VBR视频通信量预测的差分输入支持向量机(SVM:Support Vector Machine)网络模型.该网络模型采用结构风险最小化准则,在最小化经验风险的同时,尽量缩小模型预测误差的上界,从而使网络模型具有更好的推广能力.实验结果表明:支持向量机网络模型的预测误差为0.0018,而梯度径向基函数(Gradient Radial Basis Function:GRBF)神经网络模型的预测误差为0.0029.可以看出,支持向量机网络模型的预测精度要比GRBF网络模型的预测精度高出大约40%.     

采用V.34建议的高速传真保密机的研究与实现

本文研究分析了采用V.34建议的高速传真通信规程,阐述了高速传真保密通信的基本原理,基于此设计实现了门卫式高速传真保密机.     

Niehoff M5拉丝机国产化设计

针对NiehoffM5拉丝机结构特点及用户对铜丝产品质量的要求,对NiehoffM5拉丝机进行国产化设计,即加入退火装置。给出了退火装置的工作原理,设计要点和结构示意图。加入退火装置的NiehoffM5拉丝机生产出的产品的韧性、耐磨性等性能都得到了较大的提高。     

适用于高电压测试的电容器分选机

分选是电容器交给用户前的最后一道质量保证工序,因而它决定了分选机的重要性。FX-120薄膜电容自动分选机主要进行容量不足检测、直流飞弧检测、直流双向耐压测试、损耗及容量测试并自动分选。主要介绍了该分选机的工作原理、基本机构、电气控制、气动控制、软件设计、关键技术及解决方案。     

排序支持的交互数据分类算法及其应用

交互分类是解决数据复杂分类问题的主要手段之一。在现有的大多交互分类系统中,用户能准确识别数据类别,但在有些分类场景中,类别之间的顺序关系更容易被识别,为此,提出一种排序支持的交互数据分类算法。为提升交互分类精度,引入数据的顺序信息,为降低标记难度,提出候选样本推荐策略。另外,提出一种评估分类算法性能的可视化方法,用包含基本车况、交通违法记录、交通事故记录等信息的车辆数据集进行实验验证,将相关车辆分为高危车辆、中危车辆、低危车辆3类,算法的分类结果模型一致度达近98%,验证了方法的有效性。