昆虫嗅觉相关可溶性蛋白的研究进展

昆虫在长期进化过程中形成了一套高度敏感的嗅觉系统,通过该系统昆虫可以完成寻觅配偶、定位寄主及选择产卵位点等多种行为。在昆虫嗅觉系统中的可溶性蛋白主要有气味结合蛋白(odorant-binding protein, OBP)和化学感受蛋白(chemosensory protein, CSP)。OBP可以特异性结合并运输疏水性的气味分子相应的受体,是昆虫化学识别过程的第一步,具有十分重要的作用。CSP与OBP的结构和功能类似,主要参与化合物的识别和运输,尽管没有直接的证据表明CSP也参与了昆虫的化学感受过程,但已有研究发现,CSP在昆虫嗅觉系统中发挥着重要的作用。本文主要从分子特性、蛋白结构、表达模式、生理功能等方面分别对昆虫的OBP和CSP进行了概述,为深入的研究两者的功能提供理论参考,进而为以昆虫嗅觉系统为靶标的害虫防治提供新的思路。     

昆虫嗅觉相关蛋白的研究进展

嗅觉是昆虫产生行为的重要物质基础,阐明昆虫嗅觉机理有助于调控昆虫行为和进行害虫治理。近年来,许多与嗅觉相关的生物活性分子和相关基因的发现和克隆,对揭示嗅觉机理具有重要作用。作者针对近年来研究较多的气味结合蛋白、化学感受蛋白、气味受体、气味降解酶以及感觉神经元膜蛋白等,就其生化特性、表达部位、分子结构、生理功能等进行了综述。     

鸟类社会行为中的嗅觉通讯研究进展

长期以来, 学者普遍认为大多数鸟类的嗅觉能力较弱或丧失。早期实验未能得到统一而清晰的结果, 进一步扩大了这一认知误区。随着研究手段和技术的发展, 解剖学、电生理学、分子生物学和行为生态学等学科提供了鸟类嗅觉存在的证据。目前, 相关研究在鸟类14目33科中发现了嗅觉通讯的证据。与视觉和听觉一样, 这一感觉通路可能在鸟类的觅食、导航、防御、隐蔽、警戒和交流等社会行为中发挥着重要作用。本文着重于嗅觉通讯在鸟类社会行为中的功能, 通过回顾近十年的相关研究, 综述鸟类嗅觉在物种和个体识别、繁殖行为、亲缘识别、配偶选择与竞争等方面发挥的作用。我们也指出: 研究手段的创新将揭示更多鸟类物种在社会行为中对于嗅觉通讯的运用。此外, 当前研究多聚焦于少数物种和单一层面(如生理、生态和环境等), 使用分子生物学、解剖与生理学、行为学和神经生物学手段的整合研究较为缺乏; 而后者更有可能全面地揭示鸟类嗅觉通讯的复杂机理及其在社会行为中发挥的多种功能。     

艾滋病疫苗11月人体测试

中国科学院院士、中科院上海药物所所长、国家“973”新药创新研究项目首席科学家陈凯先在近日举行的“第八届海内外生命科学论坛”上介绍说,我国的科研人员利用虚拟筛选技术在抗2型糖尿病新药的研究方面取得进展,基于国产超级计算机的大规模虚拟筛选新药系统,目前已捕获数个具有显著胰岛素增敏作用的先导化合物,为有效治疗2型糖尿病展示了诱人前景。     

酵母双杂交筛选华北大黑鳃金龟触角均一化cDNA文库

昆虫错综复杂的嗅觉系统在昆虫寻找寄主、 交配、 产卵以及逃避行为中起到了至关重要的作用。因此, 对昆虫嗅觉感受机理的研究将有助于揭开昆虫感受和识别环境中的气味物质并引起相关行为反应的秘密。为了更多地了解华北大黑鳃金龟Holotrichia oblita Faldermann 嗅觉相关蛋白互作机制, 本实验利用DUALhunter Starter Kits酵母双杂交筛选试剂盒, 构建了华北大黑鳃金龟气味结合蛋白OBP2诱饵载体, 筛选了华北大黑鳃金龟触角酵母双杂交系统均一化的cDNA文库。通过β-半乳糖苷酶活性检测以及GenBank中Blast比对分析, 以OBP2为诱饵鉴定出6个阳性互作物。我们推测其中的一种较强的阳性互作物凝固酶原可能是华北大黑鳃金龟在嗅觉识别过程中的相关蛋白。     

天然活性先导化合物生物转化研究进展

天然活性先导化合物生物转化是利用生物催化剂(如：酶、微生物、动植物细胞)将加入到生物反应系统中的天然活性先导化合物进行特异性的分子结构修饰以获得高效、低毒新化合物的方法。该方法可以有效地提高已知的天然活性先导化合物的活性、降低毒副作用、改善水溶性和生物利用度,也可以用来生产具有重要应用价值的微量天然活性先导化合物,同时可用于药物代谢机制的研究。国内外学者已经针对甾体、醌类、黄酮类、萜类等化合物开展了天然活性先导化合物生物转化研究,筛选出一批有重要应用价值的生物转化反应类型,但针对天然活性先导化合物生物转化的机制、生物转化过程工程以及生物转化产物活性等方面的研究较少。将现代生命科学技术(如：生物催化剂的定向改造、高通量筛选、组合生物转化、非水相生物转化)引入天然活性先导化合物生物转化研究中,必将推进天然活性先导化合物的快速发展。     

本期重点推介

正气味结合蛋白(odorant binding proteins,OBPs)是昆虫嗅觉机制中的关键物质之一,参与昆虫专一性识别外界环境的第一步生化反应。而昆虫嗅觉对其生理和行为又起着重要作用。研究OBPs的分子构造和功能,对认识昆虫的嗅觉机制并在害虫防治中加以应用都有重要意义。福建农林大学应用生态研究所程小娟、蔡立君和尤民生等在克隆和分析小菜蛾OBP2基因Pxyl OBP2 c DNA序列的基础上,通过实时定量PCR(qRT-PCR)分析Pxyl OBP2在小菜蛾不同发育阶段和不同组织中的表达模式,对Pxyl OBP2进行了原核表达及蛋     

配体门控离子通道受体别构调节研究

配体门控离子通道(LGIC)在中枢神经系统信息处理的过程中起着极其重要的作用,与多种神经性疾病有着密切联系.与受体正位调节作用相比,别构调节效应具有类内源性生理作用、高选择性及不易过度调节的优点,从而避免了一系列不良反应发生.目前,各种LGIC受体超家族均有别构调节剂发现,部分已在临床上得到应用.在未来的研究中,通过建立及完善针对别构调节剂的筛选策略,别构调节剂的发现效率及生物活性将得到极大地提高,更多的药物将会不断涌现.     

农业昆虫气味受体功能研究进展

昆虫主要依靠嗅觉系统寻找食物、发现配偶、控制交配、选择产卵地、逃避天敌等,因此嗅觉系统对昆虫的繁殖和生存至关重要。气味受体(odorant receptor, OR)是嗅觉系统中的关键成分之一,可被信息化合物激活引发特定行为产生。随着测序技术的发展,大量的农业昆虫的基因组和转录组被测序,从测序数据中分析获得了它们的OR家族。现在OR功能研究经常用到外源表达系统和CRISPR/Cas9系统。外源表达系统结合记录系统可用于目标OR表达和配体筛选,CRISPR/Cas9系统可从昆虫染色体上敲除目标OR基因,进而利用电生理技术和行为学实验研究其功能。本文系统总结了鳞翅目、直翅目、半翅目、双翅目、膜翅目和鞘翅目6个目30个种的农业昆虫OR的气味分子反应谱及功能,尤其是鳞翅目农业昆虫。农业昆虫的性信息素往往是由雌虫产生的两种或两种以上成分按一定比例组成的混合物,包括在种间生殖隔离中起作用的行为拮抗剂。每个种因此具有多个性信息素的受体,共同感受这些信息素信息,调控种内和种间性行为。有些OR可调谐至主要针对植物挥发物,包括花香气味,在寄主植物选择和雌虫选择产卵地等行为中起作用。聚集信息素受体可特异地被聚集信息素激活,引起聚集行为;报警信息素受体可特异地被报警信息素激活,引起排斥行为。研究农业昆虫OR的气味分子反应谱和功能可为开发在害虫防控中应用的性诱剂、食诱剂、拒食剂、聚集信息素等奠定理论基础。最后我们对未来的主要研究方向提出了以下建议：(1)开发新的外源OR表达系统;(2)研究在雌虫中特异调谐至针对雄虫产生的性信息素的性信息素受体的功能;(3)探讨OR与气味分子之间特异性相互作用的分子机制。     

面向虚拟筛选的GPU加速的分子对接方法

虚拟筛选是在计算机上对化合物分子进行模拟预筛选,找出容易和药物靶标结合的小分子(配体),从而降低实际实验测试次数,提高药物先导化合物的发现效率。常用的分子对接软件可以用于基于结构的虚拟筛选,寻找配体与靶标的最佳的作用模式和结合构象,并通过打分函数来筛选出潜在的配体。现有的对接软件如AutoDock Vina等在分子对接过程中需要耗费大量时间和计算资源,特别是面对大规模分子对接时,过长的筛选时间不能满足应用需求,因此,本文在最高效的QVina2对接软件基础上,提出一种基于GPU的QVina 2并行化方法QVina2-GPU,利用GPU硬件高度并行体系加速分子对接。具体包括增加初始化分子构象数量,以扩展蒙特卡罗的迭代局部搜索中线程的并行规模,增加蒙特卡罗的迭代搜索的广度以减少每次蒙特卡罗迭代搜索深度,并利用Wolfe-Powell准则改进局部搜索算法,提高了对接精度,进一步减少蒙特卡罗迭代搜索深度,最后,在NVIDIA Geforce RTX 3090平台上在公开的配体数据库上验证了QVina2-GPU的性能,实验表明在保证分子对接精度的基础上,我们提出的QVina2-GPU对Qvina2的平均加速比达到5.18倍,最大加速比达到12.28倍。     

Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches

Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein–protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties.     

High-Performance Drug Discovery: Computational Screening by Combining Docking and Molecular Dynamics Simulations

Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, this method is not completely reliable and therefore unsatisfactory. In this study, we used massive molecular dynamics simulations of protein-ligand conformations obtained by molecular docking in order to improve the enrichment performance of molecular docking. Our screening approach employed the molecular mechanics/Poisson-Boltzmann and surface area method to estimate the binding free energies. For the top-ranking 1,000 compounds obtained by docking to a target protein, approximately 6,000 molecular dynamics simulations were performed using multiple docking poses in about a week. As a result, the enrichment performance of the top 100 compounds by our approach was improved by 1.6–4.0 times that of the enrichment performance of molecular dockings. This result indicates that the application of molecular dynamics simulations to virtual screening for lead discovery is both effective and practical. However, further optimization of the computational protocols is required for screening various target proteins.     

Integrating DNA-encoded chemical libraries with virtual combinatorial library screening: Optimizing a PARP10 inhibitor

Two critical steps in drug development are 1) the discovery of molecules that have the desired effects on a target, and 2) the optimization of such molecules into lead compounds with the required potency and pharmacokinetic properties for translation. DNA-encoded chemical libraries (DECLs) can nowadays yield hits with unprecedented ease, and lead-optimization is becoming the limiting step. Here we integrate DECL screening with structure-based computational methods to streamline the development of lead compounds. The presented workflow consists of enumerating a virtual combinatorial library (VCL) derived from a DECL screening hit and using computational binding prediction to identify molecules with enhanced properties relative to the original DECL hit. As proof-of-concept demonstration, we applied this approach to identify an inhibitor of PARP10 that is more potent and druglike than the original DECL screening hit.     

Docking, virtual high throughput screening and in silico fragment-based drug design

The drug discovery process has been profoundly changed recently by the adoption of computational methods helping the design of new drug candidates more rapidly and at lower costs. In silico drug design consists of a collection of tools helping to make rational decisions at the different steps of the drug discovery process, such as the identification of a biomolecular target of therapeutical interest, the selection or the design of new lead compounds and their modification to obtain better affinities, as well as pharmacokinetic and pharmacodynamic properties. Among the different tools available, a particular emphasis is placed in this review on molecular docking, virtual high-throughput screening and fragment-based ligand design.     

Virtual high-throughput in silico screening

In silico methods may benefit drug discovery and development significantly by saving an average of $130 million and 0.8 years per drug. Virtual high-throughput screening (vHTS) applies in silico approaches, such as docking and alignment, to large virtual molecular databases to enrich biologically active compounds in order to yield lead structures. In an industrial environment, the commonly used ligand-based and receptor-based methods outlined here need to be computationally faster to return the utmost benefit. Intelligent database searching using new fast feedback-driven screening methods appears to be particularly rewarding in terms of both cost and time benefits.     

A simple assay for detection of small-molecule redox activity

In addition to selecting molecules of pharmacological interest, high-throughput screening campaigns often generate hits of undesirable mechanism, which cannot be exploited for drug discovery as they lead to obvious problems of specificity and developability. Examples of undesirable mechanisms are target alkylation/acylation and compound aggregation. Both types of "promiscuous" mechanisms have been described in the literature, as have methods for their detection. In addition to these mechanisms, compounds can also inhibit by oxidizing susceptible enzyme targets, such as metalloenzymes and cysteine-using enzymes. However, this redox phenomenon has been documented infrequently, and an easy method for detecting this behavior is missing. In this article, the authors describe direct proof of small-molecule oxidation of a cysteine protease by liquid chromatography/tandem mass spectrometry, develop a simple assay to predict this oxidizing behavior by compounds, and show the utility of this assay by demonstrating its ability to distinguish nuisance redox compounds from well-behaved inhibitors in 3 historical GlaxoSmithKline drug discovery efforts.     

Advances and applications of binding affinity prediction methods in drug discovery

Nowadays, the improvement of R&D productivity is the primary commitment in pharmaceutical research, both in big pharma and smaller biotech companies. To reduce costs, to speed up the discovery process and to increase the chance of success, advanced methods of rational drug design are very helpful, as demonstrated by several successful applications. Among these, computational methods able to predict the binding affinity of small molecules to specific biological targets are of special interest because they can accelerate the discovery of new hit compounds. Here we provide an overview of the most widely used methods in the field of binding affinity prediction, as well as of our own work in developing BEAR, an innovative methodology specifically devised to overtake some limitations in existing approaches. The BEAR method was successfully validated against different biological targets, and proved its efficacy in retrieving active compounds from virtual screening campaigns. The results obtained so far indicate that BEAR may become a leading tool in the drug discovery pipeline. We primarily discuss advantages and drawbacks of each technique and show relevant examples and applications in drug discovery.     

Virtual screening strategies in drug discovery

The identification of novel therapeutic targets and characterization of their 3D structures is increasing at a dramatic rate. Computational screening methods continue to be developed and improved as credible and complementary alternatives to high-throughput biochemical compound screening (HTS). While the majority of drug candidates currently being developed have been found using HTS methods, high-throughput docking and pharmacophore-based searching algorithms are gaining acceptance and becoming a major source of lead molecules in drug discovery. Refinements and optimization of high-throughput docking methods have lead to improvements in reproducing experimental data and in hit rates obtained, validating their use in hit identification. In parallel with virtual screening methods, concomitant developments in cheminformatics including identification, design and manipulation of drug-like small molecule libraries have been achieved. Herein, currently used in silico screening techniques and their utility on a comparative and target dependent basis is discussed.     

Discovery and development of heat shock protein 90 inhibitors

Heat shock protein 90 (Hsp90) is an important target in cancer because of its role in maintaining transformation and has recently become the focus of several drug discovery and development efforts. While compounds with different modes of action are known, the focus of this review is on those classes of compounds which inhibit Hsp90 by binding to the N-terminal ATP pocket. These include natural product inhibitors such as geldanamycin and radicicol and synthetic inhibitors comprised of purines, pyrazoles, isoxazoles and other scaffolds. The synthetic inhibitors have been discovered either by structure-based design, high throughput screening and more recently using fragment-based design and virtual screening techniques. This review will discuss the discovery of these different classes, as well as their development as potential clinical agents.     

Improving performance of docking-based virtual screening by structural filtration

In the current study an innovative method of structural filtration of docked ligand poses is introduced and applied to improve the virtual screening results. The structural filter is defined by a protein-specific set of interactions that are a) structurally conserved in available structures of a particular protein with its bound ligands, and b) that can be viewed as playing the crucial role in protein-ligand binding. The concept was evaluated on a set of 10 diverse proteins, for which the corresponding structural filters were developed and applied to the results of virtual screening obtained with the Lead Finder software. The application of structural filtration resulted in a considerable improvement of the enrichment factor ranging from several folds to hundreds folds depending on the protein target. It appeared that the structural filtration had effectively repaired the deficiencies of the scoring functions that used to overestimate decoy binding, resulting into a considerably lower false positive rate. In addition, the structural filters were also effective in dealing with some deficiencies of the protein structure models that would lead to false negative predictions otherwise. The ability of structural filtration to recover relatively small but specifically bound molecules creates promises for the application of this technology in the fragment-based drug discovery.