Hallucinating structure-conditioned antibody libraries for target-specific binders.
Hallucinating structure-conditioned antibody libraries for target-specific binders.
复制标题
DOI：
10.3389/fimmu.2022.999034
复制
发表时间：
2022
影响因子：
7.3
通讯作者：
Gray, Jeffrey J.
中科院分区：
文献类型：
--
作者：
Mahajan, Sai Pooja;Ruffolo, Jeffrey A.;Frick, Rahel;Gray, Jeffrey J.
关键词：
抗体被广泛开发并用作治疗癌症、传染病和炎症的疗法。在开发过程中，初始导联通常会进行额外的工程设计以增加其目标亲和力。亲和成熟度的实验方法昂贵、费力且耗时，并且很少允许对相关设计空间进行有效的探索。深度学习（DL）模型正在改变蛋白质工程和设计领域。虽然几种基于dl的蛋白质设计方法已经显示出希望，但抗体设计问题是不同的，需要专门的抗体设计模型。受利用精确结构预测DL模型的幻觉框架的启发，我们提出了FvHallucinator用于设计抗体序列，特别是以抗体结构为条件的CDR环。这种策略产生的靶向CDR文库保留了结合剂的构象，从而保留了与抗原表位的结合方式。在60种抗体的基准集上，FvHallucinator产生类似于天然CDR的序列，并概括了典型CDR簇的困惑。此外，FvHallucinator在VH-VL界面设计氨基酸取代，丰富了人类抗体库和治疗性抗体。我们提出了一个筛选FvHallucinator设计的管道，以获得一个丰富的抗原结合物库。我们将该管道应用于Trastuzumab-HER2复合物的CDR H3，以生成预测可改善原始抗体的结合亲和力和界面特性的硅设计。因此，FvHallucinator管道能够生成廉价、多样化和靶向的抗体文库，这些文库富含用于抗体亲和成熟的结合物。
Antibodies are widely developed and used as therapeutics to treat cancer, infectious disease, and inflammation. During development, initial leads routinely undergo additional engineering to increase their target affinity. Experimental methods for affinity maturation are expensive, laborious, and time-consuming and rarely allow the efficient exploration of the relevant design space. Deep learning (DL) models are transforming the field of protein engineering and design. While several DL-based protein design methods have shown promise, the antibody design problem is distinct, and specialized models for antibody design are desirable. Inspired by hallucination frameworks that leverage accurate structure prediction DL models, we propose the FvHallucinator for designing antibody sequences, especially the CDR loops, conditioned on an antibody structure. Such a strategy generates targeted CDR libraries that retain the conformation of the binder and thereby the mode of binding to the epitope on the antigen. On a benchmark set of 60 antibodies, FvHallucinator generates sequences resembling natural CDRs and recapitulates perplexity of canonical CDR clusters. Furthermore, the FvHallucinator designs amino acid substitutions at the VH-VL interface that are enriched in human antibody repertoires and therapeutic antibodies. We propose a pipeline that screens FvHallucinator designs to obtain a library enriched in binders for an antigen of interest. We apply this pipeline to the CDR H3 of the Trastuzumab-HER2 complex to generate in silico designs predicted to improve upon the binding affinity and interfacial properties of the original antibody. Thus, the FvHallucinator pipeline enables generation of inexpensive, diverse, and targeted antibody libraries enriched in binders for antibody affinity maturation.
登录
查看更多内容
影响因子：
4.6
作者：
Horita S;Nomura Y;Sato Y;Shimamura T;Iwata S;Nomura N
通讯作者：
Nomura N
影响因子：
5.3
作者：
Akbar R;Robert PA;Weber CR;Widrich M;Frank R;Pavlović M;Scheffer L;Chernigovskaya M;Snapkov I;Slabodkin A;Mehta BB;Miho E;Lund-Johansen F;Andersen JT;Hochreiter S;Hobæk Haff I;Klambauer G;Sandve GK;Greiff V
通讯作者：
Greiff V
影响因子：
64.8
作者：
Bhardwaj, Gaurav;Mulligan, Vikram Khipple;Bahl, Christopher D.;Gilmore, Jason M.;Harvey, Peta J.;Cheneval, Olivier;Buchko, Garry W.;Pulavarti, Surya V. S. R. K.;Kaas, Quentin;Eletsky, Alexander;Huang, Po-Ssu;Johnsen, William A.;Greisen, Per Jr;Rocklin, Gabriel J.;Song, Yifan;Linsky, Thomas W.;Watkins, Andrew;Rettie, Stephen A.;Xu, Xianzhong;Carter, Lauren P.;Bonneau, Richard;Olson, James M.;Coutsias, Evangelos;Correnti, Colin E.;Szyperski, Thomas;Craik, David J.;Baker, David
通讯作者：
Baker, David
Biopython：用于计算分子生物学和生物信息学的免费 Python 工具。
DOI：
10.1093/bioinformatics/btp163
发表时间：
2009-06-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Cock PJ;Antao T;Chang JT;Chapman BA;Cox CJ;Dalke A;Friedberg I;Hamelryck T;Kauff F;Wilczynski B;de Hoon MJ
通讯作者：
de Hoon MJ
影响因子：
5.3
作者：
Benschop, Robert J.;Chow, Chi-Kin;Allan, Barrett W.
通讯作者：
Allan, Barrett W.