全人源抗体基因库的构建及抗表皮生长因子受体单链抗体的筛选

目的 从肿瘤患者外周血中构建全人源单链可变区抗体基因库,并采用真核核糖体展示技术高效筛选全人源抗表皮生长因子受体(epidermal growth factor receptor,EGFR)单链抗体.方法 采用PCR技术从52例晚期肿瘤患者外周血中构建大容量的全人源抗体基因库.针对EGFR抗原靶标,应用真核核糖体展示技术对该基因库进行高效富集筛选.通过大肠埃希菌表达体系对回收的基因库进行克隆、表达,并鉴定抗体活性.结果 构建的全人源抗体基因库库容估算为4.3×1013分子.对该基因库进行3轮富集筛选后,获得EGFR富集的单链抗体基因库.对回收基因库进行克隆、表达,并随机鉴定了49个克隆,获得结合力为108～107mol/L的2株全人源抗EGFR单链抗体.结论 利用肿瘤患者外周血,结合核糖体展示技术可以较快获得具有医用价值的全人源单链抗体.     

噬菌体展示技术的概况与应用

噬菌体展示技术是一项独特的基因重组表达技术,亦是一种简便、有效的筛选工具,通过淘选,得到与靶标具有高亲和性和选择性的蛋白质或者多肽。GP Smith于1985年首次实现了利用丝状噬菌体表达外源基因,开始了噬菌体展示技术的研究;抗体噬菌体展示成为第一种也是最广泛使用的体外筛选技术,目前已有多种人抗体通过该技术获得并被批准用于临床治疗;噬菌体随机肽库筛选获得的多肽可以作为分子载体靶向运载药物,也可以直接与靶点分子特异性结合,发挥生物治疗的作用。近年来,该技术在寻找肿瘤特异性靶分子和靶向治疗方面显示了其独特的优势。噬菌体展示衍生产品在疾病的诊断和治疗中发挥着重要作用,并且将在不同的医学技术领域广泛应用,包括生物传感、监测、分子成像、基因治疗、疫苗开发和纳米技术。本文对噬菌体展示技术的概况及其应用进行了综述,探讨了噬菌体展示技术的优势和不足,为实现噬菌体展示技术在不同技术领域的广泛应用奠定一定的基础。     

治疗性抗体药物研究与发展趋势

单克隆抗体技术的问世,使研究和生产治疗性单抗药物成为现实.随着基因工程技术的发展,新型的重组抗体技术也随之而生.人们可以利用DNA重组技术对鼠源抗体进行人源化改造、构建合成或半合成抗体库及噬菌体抗体库,从中筛选获得人源抗体,甚至利用转基因小鼠直接获得人源抗体.抗体药物发展的趋势也从鼠源、人一鼠嵌合、人源化到全人源.近年...     

噬菌体展示库筛选冠状病毒刺突蛋白抗体

目的 筛选特异性结合冠状病毒刺突蛋白（简称S蛋白）抗原的特异性抗体。方法 根据冠状病毒S蛋白家族数据库,合成保守序列,分子克隆连接到pet-22原核表达载体,纯化蛋白免疫小鼠产生相应的多克隆抗体,利用噬菌体展示技术提取免疫后小鼠脾脏制备噬菌体抗体scfv展示库,经过高通量筛选得到对S蛋白抗原具有高亲和力和强特异性的抗体片段。结果 通过合成的冠状病毒S蛋白糖蛋白亚基CoV＿Spike＿S1-S2＿S2制备了抗体scfv噬菌体展示库,筛选出能与冠状病毒S蛋白特异性结合的单克隆抗体scfv片段。结论 构建的小鼠抗Cov-Spike的抗体scfv库可筛选到特异性结合冠状病毒S蛋白抗原的特异性抗体,为进一步筛选、鉴定具有潜在防治冠状病毒感染的抗体系列药物提供了可行的方法。     

核糖体展示小分子抗体的研究进展

核糖体展示技术是由Plückthun实验室[1]在多聚核糖体展示技术[2]的基础上改进而来的一种利用功能性蛋白相互作用进行筛选的新技术,它将正确折叠的蛋白及其mRNA同时结合在核糖体上,形成mRNA-核糖体-蛋白质三聚体,使目的蛋白的基因型和表型联系起来,可用于抗体及蛋白质文库选择、蛋白质体外改造等。运用此技术已成功筛选到一些与靶分子特异结合的高亲和力蛋白质,包括抗体、多肽、酶等,是蛋白质筛选的重要工具。1核糖体展示技术的基本原理及特点核糖体展示技术通过聚合酶链反应(PCR)扩增DNA文库,同时引入T7启动子、核糖体结合位点及茎-…     

应用核糖体展示技术高效筛选人源抗IgE单链抗体

本研究从哮喘患者外周血分离淋巴细胞、提取总mRNA,采用RT-PCR技术分别构建重链可变区VH(variable region of heavy chain)和轻链可变区VL(variable region of light chain)cDNA库,然后通过连接肽(Gly4Ser)3和特定引物将VH和VL cDNA基因组装成人源单链抗体(scFv)核糖体展示模板基因库。应用兔网织红细胞核糖体展示技术,单引物原位反转录技术,经3轮展示富集并回收针对人源IgE蛋白(免疫球蛋白E)的目的单链抗体基因;回收的抗体基因经双酶切后与pET22b(+)载体连接,转化至E.coli Rosseta(DE3)宿主细胞,应用菌落PCR结合Dot blotting技术快速鉴定阳性克隆,抗原ELISA法进一步鉴定阳性克隆。VH和VL基因库得到正确的构建,长度分别为400和710 bp,库容为1013。核糖体展示模板构建正确,长度为1 100 bp。该基因库经过3轮针对IgE蛋白的核糖体展示,目的基因得到了有效富集和回收。经过高效克隆、表达和鉴定,确定1株针对人IgE蛋白显示最强亲和力的阳性克隆菌pET-IgE-6。经测序和序列分析证实该单链抗体为人源抗体,序列未见国内外报道。结果表明,采用患者外周血构建人源抗体基因库,结合核糖体展示技术,可以成功获得高亲和力的人源单链抗体分子。该技术路线为快速获得具有药用价值的人源抗体提供了参考。     

高通量DNA测序技术在抗体新药研发中的应用

自2005年首次报道了一种基于微乳液PCR技术的高通量DNA测序技术 (high-throughput DNA sequencing technology) 以来, 高通量DNA测序平台已经发展为基因组和各种基因文库序列检测的强大工具。大容量的抗体基因库是目前获得抗体新药的基础, 高通量DNA测序技术为从海量的抗体基因库中快速发现功能抗体分子提供了可能。本文就近几年高通量DNA测序技术在抗体基因库的多样性分析, 抗体CDR3区的高通量测序、频率分析、功能基因发现及各种展示技术与高通量DNA测序技术的对接应用等方面进行了综述, 以期为抗体新药的研发提供一条新的技术路线。     

人源抗BLyS单链抗体的筛选及其构建与表达

目的:从人源单链抗体库中筛选抗B细胞刺激因子(BLy S)的单链抗体基因、构建表达载体并实现单链抗体的表达。方法:以哺乳动物细胞展示型人源Sc Fv抗体库为起始文库,通过流式细胞术进行分选,获得荧光强度最强、比例为0.1%的阳性细胞。从候选细胞中提取质粒并转化至DH5α中进行扩增,得到质粒转染293T细胞作为下一轮筛选所需的抗体库。经过依次降低抗原浓度进行了3轮分选得到2个候选的Sc Fv序列。经序列分析,选择其中一种单链抗体基因,利用基因工程技术构建分泌型表达质粒,转染293E细胞并通过镍亲和层析纯化获得B-10 Sc Fv抗体蛋白并通过Fortie Bio Octet QK进行亲和力分析。结果:经过3轮筛选获得了全新的抗BLy S Sc Fv抗体序列,成功构建并表达了anti-Bly S Sc Fv抗体蛋白,该单链抗体与的BLy S亲和常数为3.08 nmol·L-1。结论:从哺乳动物细胞展示型人源Sc Fv抗体库中成功获得了可结合BLy S的全新单链抗体基因序列,该单链抗体与BLy S具有较高的亲和力,这为后续的活性研究以及产品开发奠定了基础。     

哺乳动物细胞表面展示人源HIV特异性全长抗体库的构建

目的构建人源的HIV特异性全长抗体基因库并获得可稳定展示全长抗体的哺乳动物细胞库。方法提取HIV患者外周血淋巴细胞的总RNA,采用RT-PCR的方法扩增抗体重链可变区(VH)基因,插入哺乳动物细胞表达载体pDGB4,转化感受态大肠杆菌TOPO-10,构建抗体基因库,随后将抗体库的质粒DNA稳定转染FCHO细胞,获得可稳定展示全长抗体的细胞库,用流式细胞仪分析全长抗体在FCHO细胞表面的表达。结果本研究以少量外周血淋巴细胞RNA为来源,构建了库容量为7.77×104的全长抗体基因库;将基因库的质粒DNA稳定转染FCHO细胞后,获得了可稳定展示全长抗体的细胞库;经流式细胞仪分析,有67%的细胞表面可表达可被检测到的全长抗体。结论本研究构建了人源的HIV特异性全长抗体基因库并获得可稳定展示全长抗体的哺乳动物细胞库,为治疗HIV感染的特异性抗体的筛选打下了基础。     

人源性肺癌噬菌体展示抗体库的构建

目的应用噬菌体展示技术,构建天然人源抗肺癌噬菌体抗体组合文库,筛选能与肺癌细胞特异结合的抗体。方法用RNA提取试剂盒提取淋巴细胞RNA,以Oligo DT为引物反转录合成cDNA,以半套式PCR扩增轻链和重链可变区抗体基因并重组到原核表达载体中,通过噬菌体外壳蛋白形成融合蛋白,形成噬菌体抗体组合文库。结果电泳显示扩增的mRNA有清晰的28s、18s和5.8s的条带,600~700bp片段者为重组克隆;噬菌体展示抗体库大小在108~107之间;优化电转化条件,最终得到库容为1.2×108cfu,双链重组率为41%的抗体库。结论成功构建人源抗肺癌噬菌体展示文库,为下一步筛选具有肺癌细胞特异亲和力的人源性单克隆抗体奠定了基础。     

Latest technologies for the enhancement of antibody affinity

High affinity antibodies are crucial both for the discovery and validation of biomarkers for human health and disease and as clinical diagnostic and therapeutic reagents. This review describes some of the latest technologies for the design, mutation and selection of high affinity antibodies that provide a paradigm for molecular evolution of a far wider range of proteins including enzymes. Strategies include both in vivo and in vitro methods and embrace the latest concepts for antibody display and selection. Specifically, affinity enhancement can be tailored to the target-binding surface, typically the complementary determining region (CDR) loops in antibodies, whereas enhanced stability, expression or catalytic properties can be affected by selected changes to the core protein scaffold. Together, these technologies provide a rapid and powerful strategy to drive the next generation of protein-based reagents for numerous clinical, environmental and agribusiness applications.     

Computational and artificial intelligence-based methods for antibody development

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.     

Current methods for the generation of human antibodies for the treatment of autoimmune diseases

Autoimmune diseases are a significant area of unmet medical need in the Western World, but human antibodies are an emerging drug class that could address this demand. Some autoimmune diseases, such as rheumatoid arthritis, are currently benefiting from antibody treatment and new and existing technologies for antibody generation could facilitate the production of effective human antibodies as future drug candidates for other autoimmune diseases. Several methods of generating human antibodies for use as therapeutics have been established, the most commonly used being phage display and transgenic mouse technologies and more recently, cell-free display technologies have also emerged. In this review, we explain the principles behind the various methods of antibody generation and highlight some potential benefits of certain approaches in the context of treatment of autoimmune disease.     

Characterization of protein-ligand interaction sites using experimental and computational methods

The ability to identify the sites of a protein that can bind with high affinity to small, drug-like compounds has been an important goal in drug design. Accurate prediction of druggable sites and the identification of small compounds binding in those sites have provided the input for fragment-based combinatorial approaches that allow for a more thorough exploration of the chemical space, and that have the potential to yield molecules that are more lead-like than those found using traditional high-throughput screening. Current progress in experimental and computational methods for identifying and characterizing druggable ligand binding sites on protein targets is reviewed herein, including a discussion of successful nuclear magnetic resonance, X-ray crystallography and tethering technologies. Classical geometric and energy-based computational methods are also discussed, with particular focus on two powerful technologies, that is, computational solvent mapping and grand canonical Monte Carlo simulations (as used by Locus Pharmaceuticals Inc). Both methods can be used to reliably identify druggable sites on proteins and to facilitate the design of novel, low-nanomolar-affinity ligands.     

Computational Fragment-Based Drug Design: Current Trends,Strategies, and Applications

Fragment-based drug design (FBDD) has become an effective methodology for drug development for decades. Successful applications of this strategy brought both opportunities and challenges to the field of Pharmaceutical Science. Recent progress in the computational fragment-based drug design provide an additional approach for future research in a time- and labor-efficient manner. Combining multiple in silico methodologies, computational FBDD possesses flexibilities on fragment library selection, protein model generation, and fragments/compounds docking mode prediction. These characteristics provide computational FBDD superiority in designing novel and potential compounds for a certain target. The purpose of this review is to discuss the latest advances, ranging from commonly used strategies to novel concepts and technologies in computational fragment-based drug design. Particularly, in this review, specifications and advantages are compared between experimental and computational FBDD, and additionally, limitations and future prospective are discussed and emphasized.     

Advances in phage display technology for drug discovery

Introduction: Over the past decade, several library-based methods have been developed to discover ligands with strong binding affinities for their targets. These methods mimic the natural evolution for screening and identifying ligand–target interactions with specific functional properties. Phage display technology is a well-established method that has been applied to many technological challenges including novel drug discovery.

Areas covered: This review describes the recent advances in the use of phage display technology for discovering novel bioactive compounds. Furthermore, it discusses the application of this technology to produce proteins and peptides as well as minimize the use of antibodies, such as antigen-binding fragment, single-chain fragment variable or single-domain antibody fragments like VHHs.

Expert opinion: Advances in screening, manufacturing and humanization technologies demonstrate that phage display derived products can play a significant role in the diagnosis and treatment of disease. The effects of this technology are inevitable in the development pipeline for bringing therapeutics into the market, and this number is expected to rise significantly in the future as new advances continue to take place in display methods. Furthermore, a widespread application of this methodology is predicted in different medical technological areas, including biosensing, monitoring, molecular imaging, gene therapy, vaccine development and nanotechnology.     

抗体和疫苗在多重耐药菌感染预防和治疗中的应用

多重耐药菌感染为临床抗感染治疗带来了巨大挑战,特异性抗体和疫苗有望弥补抗生素治疗的缺陷,但抗体和疫苗的研发并不成熟,在后抗生素时代,随着科学技术的发展和病原体致病机制研究的深入,抗体和疫苗在临床感染性疾病的预防和治疗中将有极大的应用前景。     

The rapid discovery of engineered antibodies

Antibodies, in particular human antibodies, are a growing class of new agents with potential diagnostic and therapeutic applications. Advanced DNA and protein technologies allow for the generation of recombinant antibodies in vitro from large DNA libraries. Ribosome display is a cell-free protein display technology that selects desirable antibodies through an efficient search for novel diversity from large populations. The ribosome-selected pools can then be screened rapidly to identify individual antibodies possessing the required activity, without involving the need for cloning by Escherichia coli. The combination of ribosome display, selection and cell-free screening provides a rapid discovery tool for the generation, optimization and modification of antibody-combining sites.     

Virtual screening and its integration with modern drug design technologies

Drug discovery is a highly complex and costly process, which demands integrated efforts in several relevant aspects involving innovation, knowledge, information, technologies, expertise, R&D investments and management skills. The shift from traditional to genomics- and proteomics-based drug research has fundamentally transformed key R&D strategies in the pharmaceutical industry addressed to the design of new chemical entities as drug candidates against a variety of biological targets. Therefore, drug discovery has moved toward more rational strategies based on our increasing understanding of the fundamental principles of protein-ligand interactions. The combination of available knowledge of several 3D protein structures with hundreds of thousands of small-molecules have attracted the attention of scientists from all over the world for the application of structure- and ligand-based drug design approaches. In this context, virtual screening technologies have largely enhanced the impact of computational methods applied to chemistry and biology and the goal of applying such methods is to reduce large compound databases and to select a limited number of promising candidates for drug design. This review provides a perspective of the utility of virtual screening in drug design and its integration with other important drug discovery technologies such as high-throughput screening (HTS) and QSAR, highlighting the present challenges, limitations, and future perspectives in medicinal chemistry.     

Host and microbial cells as targets for armed antibodies in the treatment of infectious diseases

There is a renewed interest in antibody-based therapy for infectious diseases. Antibodies armed with different cytotoxic agents such as toxins, drugs or radionuclides can specifically target virally infected host cells or microbes themselves, thus potentially providing new options for the treatment of infectious diseases. The technologies for arming antibodies with different cytotoxic agents are well advanced. Overall, armed antibody therapy provides an exciting new strategy that may potentially be useful against a variety of infectious diseases and tumors caused by microbial agents. This review highlights the use of armed antibodies to target infected host cells or to target the microbes themselves, describing challenges and future directions for both approaches.