Application of germline antibody features to vaccine development, antibody discovery, antibody optimization and disease diagnosis

生殖系 抗体 抗原 生物 可药性 疾病 免疫学 病毒学 计算生物学 遗传学 医学 基因 病理
作者
Yingjie Zhang,Qing Li,Liang Luo,Changfei Duan,Jianzhong Shen,Zhanhui Wang
出处
期刊:Biotechnology Advances [Elsevier BV]
卷期号:65: 108143-108143 被引量:10
标识
DOI:10.1016/j.biotechadv.2023.108143
摘要

Although the efficacy and commercial success of vaccines and therapeutic antibodies have been tremendous, designing and discovering new drug candidates remains a labor-, time- and cost-intensive endeavor with high risks. The main challenges of vaccine development are inducing a strong immune response in broad populations and providing effective prevention against a group of highly variable pathogens. Meanwhile, antibody discovery faces several great obstacles, especially the blindness in antibody screening and the unpredictability of the developability and druggability of antibody drugs. These challenges are largely due to poorly understanding of germline antibodies and the antibody responses to pathogen invasions. Thanks to the recent developments in high-throughput sequencing and structural biology, we have gained insight into the germline immunoglobulin (Ig) genes and germline antibodies and then the germline antibody features associated with antigens and disease manifestation. In this review, we firstly outline the broad associations between germline antibodies and antigens. Moreover, we comprehensively review the recent applications of antigen-specific germline antibody features, physicochemical properties-associated germline antibody features, and disease manifestation-associated germline antibody features on vaccine development, antibody discovery, antibody optimization, and disease diagnosis. Lastly, we discuss the bottlenecks and perspectives of current and potential applications of germline antibody features in the biotechnology field.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
留留发布了新的文献求助30
1秒前
负责纲完成签到,获得积分10
1秒前
2秒前
dada发布了新的文献求助10
3秒前
4秒前
加薪完成签到,获得积分10
4秒前
destiny发布了新的文献求助10
4秒前
累了就休息不是放弃完成签到,获得积分10
4秒前
Odingers发布了新的文献求助10
5秒前
5秒前
Leal发布了新的文献求助10
5秒前
忧郁依霜发布了新的文献求助30
7秒前
xybc完成签到,获得积分10
7秒前
着急的帽子完成签到,获得积分10
8秒前
bbanshan完成签到,获得积分10
9秒前
英姑应助潺潺流水采纳,获得10
9秒前
量子星尘发布了新的文献求助10
9秒前
C_Cppp发布了新的文献求助10
9秒前
美好乐松应助dahai采纳,获得10
10秒前
科研通AI5应助叁丘山采纳,获得30
10秒前
汉堡包应助柒柒球采纳,获得30
11秒前
SciGPT应助von采纳,获得10
12秒前
13秒前
chu完成签到,获得积分10
14秒前
屈绮兰发布了新的文献求助50
14秒前
量子星尘发布了新的文献求助30
17秒前
17秒前
yqwang发布了新的文献求助10
18秒前
Mine完成签到,获得积分10
18秒前
科研通AI2S应助Odingers采纳,获得10
19秒前
20秒前
Leal完成签到,获得积分10
21秒前
成就缘分完成签到,获得积分10
21秒前
24秒前
酷波er应助yqwang采纳,获得10
25秒前
科研通AI5应助玛卡巴卡采纳,获得20
26秒前
研友_8QxN1Z完成签到,获得积分10
28秒前
量子星尘发布了新的文献求助10
28秒前
落叶解三秋完成签到,获得积分10
31秒前
32秒前
高分求助中
Production Logging: Theoretical and Interpretive Elements 2700
An experimental and analytical investigation on the fatigue behaviour of fuselage riveted lap joints: The significance of the rivet squeeze force, and a comparison of 2024-T3 and Glare 3 1000
Neuromuscular and Electrodiagnostic Medicine Board Review 1000
Statistical Methods for the Social Sciences, Global Edition, 6th edition 600
こんなに痛いのにどうして「なんでもない」と医者にいわれてしまうのでしょうか 510
ALUMINUM STANDARDS AND DATA 500
Walter Gilbert: Selected Works 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3664444
求助须知:如何正确求助?哪些是违规求助? 3224488
关于积分的说明 9757694
捐赠科研通 2934379
什么是DOI,文献DOI怎么找? 1606832
邀请新用户注册赠送积分活动 758873
科研通“疑难数据库(出版商)”最低求助积分说明 735012