药物发现中常用化合物库特征描述与对比分析

随着计算技术的发展和分子模拟软件的日趋成熟, 虚拟筛选已经在药物发现过程中发挥着越来越重要的作用. 在虚拟筛选过程中, 所使用化合物库的质量对先导化合物发现的成功率起着至关重要的作用. 本文通过对已知药物库、天然产物库、中药原植物化学成分库、筛选常用商业化合物库以及研究者所在实验室建立的化合物库的分析比较, 从化合物库的分子多样性、化学空间和分子骨架等多个方面提取并对比每一种化合物库的特征, 发现了已知药物库与中药原植物化学成分库的特征相似性, 揭示了中药原植物化学成分库作为筛选库的类药性优势, 并且深化了对几种筛选用化合物库特征的认识和理解.     

组合化学、分子库与新药研究

组合化学是进入90 年代以来寻找及优化新药先导化合物的主要研究方法, 其特点是改变了传统的逐一合成、逐一纯化、逐一筛选的模式, 而是以合成和筛选化学库的形式完成寻找及优化药物先导化合物, 极大地加快了药物先导化合物出现的速度。本文就目前有关组合化学研究的基本理论、基本方法、发展趋势、研究成果以及我国应当采取的措施进行了综述。     

多样性导向的“类天然产物”化合物库合成

天然产物是药物发现中先导化合物的重要来源.高通量筛选技术的发展和近年来化学生物学研究的深入,对拓展天然产物与活性相关的＂化学空间＂提出了新要求.用多样性导向合成方法建立骨架多样、构造复杂、立体化学多样性的＂类天然产物＂化合物库进行生物学相关研究,并以此为基础发现药物先导化合物正在成为一种趋势.在此过程中,发展具有立体选择性和区域选择性,能够广泛应用于多种底物的有机化学反应起着关键作用.     

动态组合化学最新进展

动态组合化学是组合化学的一个新兴分支,在药物先导化合物的发现中有广阔的应用前景。在动态组合化学库中,利用靶标分子的诱导结合作用,通过可逆共价反应,能够选择性的筛选到与靶标分子存在强相互作用的优势化合物。本文按照动态组合化学方法简介、动态组合化学中的可逆共价化学、动态组合化学库的分类、动态组合化学库筛选方法的研究进展及动态组合化学在药物先导化合物发现过程中的应用等5个方面对动态组合化学进行了概述。     

分子阵列研究

小分子化合物可以调节生物学过程,是研究活性生物大分子特别是蛋白质以及药物的重要工具,而高通量筛选是发现活性分子的重要方法.分子阵列是近年来新出现的一种高通量筛选技术,上面含有成千上万种组合合成的化合物以及天然产物,可以用于发现新的先导化合物,以及筛选已有的先导化合物.现在分子阵列已经成功应用于蛋白分析和先导化合物的发现等许多领域.本文综述了近年来分子阵列的构建过程、原位合成和非原位合成等各种固定化策略以及荧光免疫检测、表面等离子体共振成像技术等检测手段,并介绍了化学分子印刷阵列方法,最后总结了分子阵列的应用,并对分子阵列在我国中药发展等方面将起到的潜在作用作了展望.     

一种新的De Novo生物活性分子设计方法

由药效团进行虚拟活性结构生成与3D-QSAR模型相结合,筛选出有前途的结构多样性的化合物,并从中寻找活性先导化合物,是一种新的分子设计方法。采用这种方法对抗小麦赤霉病类含氟农药进行了研究,共生成了53个虚拟活性结构,通过3D-QSAR模型筛选出其中10个活性较高的结构,在活性最高的化合物基础上进行了结构修饰,得到了活性更高且毒性较低的理想化合物。研究结果表明这种方法能突破原模型化合物结构模式的局限,可以找到结构新颖的活性先导化合物,是一种非常有前途的分子设计方法,而且具有较高的筛选效率。     

分子阵列研究进展

小分子化合物可以调节生物学过程,是研究活性生物大分子（特别是蛋白质）以及药物的重要工具,而高通量筛选是发现活性分子的重要方法。分子阵列是近年来新出现的一种高通量筛选技术,上面含有成千上万种组合合成的化合物以及天然产物,可以用于发现新的先导化合物,以及筛选已有的先导化合物。现在分子阵列已经成功应用于蛋白分析、先导化合物的发现等许多领域。本文综述了近年来分子阵列的构建过程,原位合成、非原位合成等各种固定化策略以及荧光免疫检测,表面等离子体共振成像技术等检测手段,并介绍了化学分子印刷阵列方法,最后总结了分子阵列的应用,并对分子阵列在我国中药发展等有方面将起到的潜在作用作了展望。     

ω-芋螺毒素的定量构效关系与虚拟筛选

ω-芋螺毒素属于海洋生物活性多肽,由24-31个氨基酸残基组成.特异性作用于电压敏感的钙离子通道(VGCCs),能够直接开发成药物或作为先导化合物进行新药开发.本文应用新型氨基酸残基结构描述符cscales和遗传偏最小二乘算法,对ω-芋螺毒素进行定量构效关系(QSAR)研究,并设计、构建了容量为2244个化合物的N-型和P/Q-型VGCC拮抗剂虚拟组合多肽库,然后分别采用QSAR模型预测和相似性搜索方法对组合多肽库进行了虚拟筛选.研究结果表明,建立的N-型和P/Q-型VGCC拮抗剂QSAR模型均具有较好的预测能力,交叉验证相关系数(CV-r2)均大于0.89.主成分分析和聚类分析结果表明,虚拟组合多肽库中化合物具有较好的结构多样性和差异性.通过虚拟筛选,得到了具有高预测活性的6个N-型和19个P/Q-型钙离子通道拮抗剂,为进一步的合成和活性评价奠定了理论基础.同时,本文建立的多肽QSAR预测模型和虚拟筛选策略,为其它多肽类化合物的定量构效关系研究和虚拟筛选提供了参考.     

Schrödinger药物虚拟筛选流程模块在大学生物和化学信息学教学中的应用

介绍了Schr?dinger药物虚拟筛选的基本原理和流程,结合大学生物和化学信息学课程的相关教学内容,分别描述了蛋白受体的预处理、类药性五原则、毒药物动力学(ADME)、泛筛选干扰化合物(PAINS)、高通量虚拟筛选、标准精度筛选、高精度筛选和MM/GBSA的打分排序原理和使用方法。该软件可以在大学生物和化学信息学的教学中演示,有助于提高学生对蛋白结构、分子构象、药物虚拟筛选和计算机辅助分子设计的理解,该软件有很好的图形界面,可以给学生直观的体验,大大丰富了大学课堂的教学内容。此外,该软件在药物设计领域里面也有很好的应用价值,大大节约了药物筛选的成本,提高了药物发现的效率。     

ω-芋螺毒素的定量构效关系与虚拟筛选

ω-芋螺毒素属于海洋生物活性多肽, 由24-31 个氨基酸残基组成. 特异性作用于电压敏感的钙离子通道(VGCCs), 能够直接开发成药物或作为先导化合物进行新药开发. 本文应用新型氨基酸残基结构描述符cscales和遗传偏最小二乘算法, 对ω-芋螺毒素进行定量构效关系(QSAR)研究, 并设计、构建了容量为2244 个化合物的N-型和P/Q-型VGCC拮抗剂虚拟组合多肽库, 然后分别采用QSAR模型预测和相似性搜索方法对组合多肽库进行了虚拟筛选. 研究结果表明, 建立的N-型和P/Q-型VGCC拮抗剂QSAR模型均具有较好的预测能力, 交叉验证相关系数(CV-r²)均大于0.89. 主成分分析和聚类分析结果表明, 虚拟组合多肽库中化合物具有较好的结构多样性和差异性. 通过虚拟筛选, 得到了具有高预测活性的6 个N-型和19 个P/Q-型钙离子通道拮抗剂, 为进一步的合成和活性评价奠定了理论基础. 同时, 本文建立的多肽QSAR预测模型和虚拟筛选策略, 为其它多肽类化合物的定量构效关系研究和虚拟筛选提供了参考.     

Identification of potential Tpx inhibitors against pathogen-host interactions

Yersinia organisms cause many infectious diseases by invading human cells and delivering their virulence factors via the type three secretion system (T3SS). One alternative strategy in the fight against these pathogenic organisms is to interfere with their T3SS. Previous studies demonstrated that thiol peroxidase, Tpx is functional in the assembly of T3SS and its inhibition by salicylidene acylhydrazides prevents the secretion of pathogenic effectors. In this study, the aim was to identify potential inhibitors of Tpx using an integrated approach starting with high throughput virtual screening and ending with molecular dynamics simulations of selected ligands. Virtual screening of ZINC database of 500,000 compounds via ligand-based and structure-based pharmacophore models retrieved 10,000 hits. The structure-based pharmacophore model was validated using high-throughput virtual screening (HTVS). After multistep docking (SP and XP), common scaffolds were used to find common substructures and the ligand binding poses were optimized using induced fit docking. The stability of the protein–ligand complex was examined with molecular dynamics simulations and the binding free energy of the complex was calculated. As a final outcome eight compounds with different chemotypes were proposed as potential inhibitors for Tpx. The eight ligands identified by a detailed virtual screening protocol can serve as leads in future drug design efforts against the destructive actions of pathogenic bacteria.     

整合药效团及共价对接用于虚拟筛选JAK3抑制剂

大量研究表明JAK3与炎症疾病的发生、发展具有密切的关系,使得JAK3成为一个极具潜力的药物靶点。其中,JAK3共价抑制剂因其选择性高、活性强的特点受到广泛关注。但是,JAK3与其家族的其他成员同源性高,使得开发JAK3选择性抑制剂充满挑战。计算机虚拟筛选方法可以在分子水平对JAK3的结构特征进行针对性筛选,但是传统的共价对接方法效率较低、准确度欠佳,因此本文提出了一种结合药效团和共价对接的虚拟筛选策略。该联用方法从DrugBank数据库成功地筛选出已报道的JAK3临床抑制剂,表明了这种虚拟筛选不仅具有较高的效率,同时具备了较强的筛选准确性,为JAK3共价抑制剂的虚拟筛选提供一定的指导作用。     

Virtual fragment screening: an exploration of various docking and scoring protocols for fragments using Glide

Fragment-based drug discovery approaches allow for a greater coverage of chemical space and generally produce high efficiency ligands. As such, virtual and experimental fragment screening are increasingly being coupled in an effort to identify new leads for specific therapeutic targets. Fragment docking is employed to create target-focussed subset of compounds for testing along side generic fragment libraries. The utility of the program Glide with various scoring schemes for fragment docking is discussed. Fragment docking results for two test cases, prostaglandin D2 synthase and DNA ligase, are presented and compared to experimental screening data. Self-docking, cross-docking, and enrichment studies are performed. For the enrichment runs, experimental data exists indicating that the docking decoys in fact do not inhibit the corresponding enzyme being examined. Results indicate that even for difficult test cases fragment docking can yield enrichments significantly better than random. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Automated Docking and the Search for HIV Protease Inhibitors

Abstract

This article will discuss the motivations, technologies, and future directions of computational automated docking in the context of the structure-based rational design of HIV-1 protease inhibitors. Docking simulations are widely used for screening of compound libraries to identify new drug leads, employing a simple model for rapid testing of thousands of compounds. Docking simulations are also useful for lead enhancement, using more detailed models to analyze the atomic interactions between inhibitors and target macromolecules. Major advances have been reported in the development of empirical force fields, which now allow assessment of relative binding strength and drug specificity, and extensions of automated docking techniques allow de novo drug design.     

Improvement of protein-compound docking scores by using amino-acid sequence similarities of proteins

The low accuracy of predicted docking scores is critical at in silico drug screening. In order to improve the accuracy of docking scores, we approximated the protein-compound binding free energy as a linear combination of the raw docking scores of a target compound with many different protein pockets. The coefficients of the linear combination were estimated by the similarities among proteins, simply by using the amino-acid sequence similarities or identities of the proteins. This method was applied to in silico screening of the active compounds of five target proteins, and it increased the hit ratio by approximately four to five times compared to that given only by the raw docking scores in every case. The hit ratio also became robust against differences of target proteins.     

Noise reduction method for molecular interaction energy: application to in silico drug screening and in silico target protein screening

We developed a new method to improve the accuracy of molecular interaction data using a molecular interaction matrix. This method was applied to enhance the database enrichment of in silico drug screening and in silico target protein screening using a protein-compound affinity matrix calculated by a protein-compound docking software. Our assumption was that the protein-compound binding free energy of a compound could be improved by a linear combination of its docking scores with many different proteins. We proposed two approaches to determine the coefficients of the linear combination. The first approach is based on similarity among the proteins, and the second is a machine-learning approach based on the known active compounds. These methods were applied to in silico screening of the active compounds of several target proteins and in silico target protein screening.     

人类蛋白激酶组的多靶点分子对接系统

蛋白激酶在信号转导、基因转录和蛋白翻译等生物过程起关键性作用,因而与大量人类疾病密切相关。所以,蛋白激酶的抑制剂筛选是抗肿瘤药物开发的热点,正在向基于全激酶组的高通量多靶点筛选模式发展。为了降低大规模实验筛选的成本,提高成功率,本文构建人类蛋白激酶组的多靶点分子对接系统,对抑制剂-激酶组的相互作用进行预测。我们首先利用同源模建方法,对人类激酶组约500个激酶变异体的催化域进行结构建模;接着以催化域结构模型为受体,用已知激酶抑制剂进行分子对接,对抑制剂与各激酶变异体的结合亲和力进行了定量计算。结果显示,本文所建立的多靶点分子对接系统可以准确预测抑制剂与激酶变异体的相互作用,结合自由能的计算值与实验值有很强的相关性。所以,该分子对接系统可用于多靶点激酶抑制剂的计算筛选,为激酶抑制剂开发与抗肿瘤药物设计提供理论依据。     

FWAVina: A novel optimization algorithm for protein-ligand docking based on the fireworks algorithm

Protein-ligand docking is an essential process that has accelerated drug discovery. How to accurately and effectively optimize the predominant position and orientation of ligands in the binding pocket of a target protein is a major challenge. This paper proposed a novel ligand binding pose search method called FWAVina based on the fireworks algorithm, which combined the fireworks algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon local search method adopted in AutoDock Vina to address the pose search problem in docking. The FWA was used as a global optimizer to rapidly search promising poses, and the Broyden-Fletcher-Goldfarb-Shannon method was incorporated into FWAVina to perform an exact local search. FWAVina was developed and tested on the PDBbind and DUD-E datasets. The docking performance of FWAVina was compared with the original Vina program. The results showed that FWAVina achieves a remarkable execution time reduction of more than 50 % than Vina without compromising the prediction accuracies in the docking and virtual screening experiments. In addition, the increase in the number of ligand rotatable bonds has almost no effect on the efficiency of FWAVina. The higher accuracy, faster convergence and improved stability make the FWAVina method a better choice of docking tool for computer-aided drug design. The source code is available at https://github.com/eddyblue/FWAVina/.     

Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors

Programmed cell death has been a fascinating area of research since it throws new challenges and questions in spite of the tremendous ongoing research in this field. Recently, necroptosis, a programmed form of necrotic cell death, has been implicated in many diseases including neurological disorders. Receptor interacting serine/threonine protein kinase 1 (RIPK1) is an important regulatory protein involved in the necroptosis and inhibition of this protein is essential to stop necroptotic process and eventually cell death. Current structure-based virtual screening methods involve a wide range of strategies and recently, considering the multiple protein structures for pharmacophore extraction has been emphasized as a way to improve the outcome. However, using the pharmacophoric information completely during docking is very important. Further, in such methods, using the appropriate protein structures for docking is desirable. If not, potential compound hits, obtained through pharmacophore-based screening, may not have correct ranks and scores after docking. Therefore, a comprehensive integration of different ensemble methods is essential, which may provide better virtual screening results. In this study, dual ensemble screening, a novel computational strategy was used to identify diverse and potent inhibitors against RIPK1. All the pharmacophore features present in the binding site were captured using both the apo and holo protein structures and an ensemble pharmacophore was built by combining these features. This ensemble pharmacophore was employed in pharmacophore-based screening of ZINC database. The compound hits, thus obtained, were subjected to ensemble docking. The leads acquired through docking were further validated through feature evaluation and molecular dynamics simulation.