全AI驱动的人形VHH噬菌体库

Q2 Medicine
Yangyang Zhao, Le Niu, Xuemin Pan, Xingda Ye, Quan Yu, Yupeng Zhu, Yile Chen, Zhiwu Sun, Yunfei Long, Yi Li
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引用次数: 0

摘要

摘要背景和意义VHH是一种小而稳定的片段,由于其体积小、稳定性、多功能性和口服给药潜力,具有很大的治疗潜力。VHH的传统来源是骆驼科动物,但治疗发展通常需要人性化。人类VHH文库对于产生治疗性VHH是非常理想的,但天然人类VH结构域作为独立单元通常是不稳定的。我们开发了一个由人工智能设计的序列组成的人形VHH库,这些序列在稳定性方面与骆驼VHH相似,并且具有如此高的人类含量,因此不再需要人性化。方法在本研究中,我们提出了一种完全人工智能驱动的VHH噬菌体文库从头设计方法。首先,收集了公开的骆驼数据和近100万个私人人类序列。其次,在人类数据上训练一个自回归人工智能模型,在人类和骆驼的混合数据上训练另一个人工智能模型。第三,VHH的CDR1、CDR2、CDR3区均由上述两个AI模型产生。最后,利用人工智能产生的超大量(4E10)VHH序列构建类人VHH噬菌体文库。结果为了验证我们方法的有效性,我们从基于AI的文库中随机合成并表达了26种VHH抗体。同时,先前文献中报道的3种人类VH分子被纳入阳性对照。首先,表达成功率为96.1%,远高于Progen的72%和ESMdesign的66%。其次,平均滴度为59.6mg/L,是阳性对照组平均值的1.5倍。第三,80%从头序列的疏水性与阳性对照组相当。此外,根据我们的专有算法,所有AI序列的免疫原性都低于阳性对照组的平均值。最后,类人VHH噬菌体文库的多样性和天然性也是极好的。结论总之,我们已经开发出一种完全人工智能驱动的解决方案,可以稳定、大规模地同时产生满足多种要求(包括高滴度、低疏水性、低免疫原性和超高表达成功率、高多样性、高天然度)的类人VHH序列。由于VHH是一种强大的治疗片段,我们的方法有可能加速纳米抗体和双特异性抗体药物的开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FULLY AI-DRIVEN HUMANOID VHH PHAGE LIBRARY
Abstract Background & Significance VHHs are small and stable fragments that have great potential as therapeutics due to their small size, stability, versatility, and potential for oral administration. The traditional source of VHHs is camelids, but humanization is usually needed for therapeutic development. A human VHH library is highly desirable for the generation of therapeutic VHHs, but natural human VH domains are usually unstable as standalone units. We developed a humanoid VHH library of AI-designed sequences that both resemble camelid VHHs in terms of stability and have such high human content that humanization is no longer needed. Methods In this study, we present a fully AI-driven approach for the de novo design of a VHH phage library. Firstly, public camelid data and nearly one million private human sequences were collected. Secondly, one autoregressive AI model was trained on human data and another AI model was trained on the mixed data of humans and camels. Thirdly, the CDR1, CDR2, CDR3 regions of VHH were all generated by the mentioned two AI models. Finally, an ultra large quantity (4E10) of VHH sequences generated by AI were utilized to build the Humanoid VHH phage library. Results In order to verify the effectiveness of our method, we randomly synthesized and expressed 26 VHH antibodies from our AI based library. At the same time, 3 human VH molecules reported in previous literature were included as positive controls. First of all, the success rate of expression is 96.1%, which is much higher than 72% of Progen and 66% of ESMdesign. Secondly, the average titer is 59.6mg/L, which is 1.5 times the average value of the positive control group. Thirdly, the hydrophobicity of 80% de novo sequences is comparable to the positive control group. Moreover, the immunogenicity of all AI sequences is less than the average value of the positive control group according to our proprietary algorithms. Finally, the diversity and naturalness of the Humanoid VHH phage library are also excellent. Conclusions In conclusion, we have developed a fully AI-driven solution that could stably and massively generate human-like VHH sequences satisfying multiple requirements (including high titer, low hydrophobicity, low immunogenicity and ultra high success rate of expression, high diversity, high naturalness) simultaneously. As VHH is a powerful therapeutic fragment, our approach has the potential to accelerate nanobody and bispecific antibody drug development.
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来源期刊
Antibody Therapeutics
Antibody Therapeutics Medicine-Immunology and Allergy
CiteScore
8.70
自引率
0.00%
发文量
30
审稿时长
8 weeks
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