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Seq2scFv:一个用于全面分析来自长读测序平台的展示文库的工具包。

Seq2scFv: a toolkit for the comprehensive analysis of display libraries from long-read sequencing platforms.

机构信息

NGS-AI Division, JSR Life Sciences, Epalinges, Switzerland.

出版信息

MAbs. 2024 Jan-Dec;16(1):2408344. doi: 10.1080/19420862.2024.2408344. Epub 2024 Oct 8.

DOI:10.1080/19420862.2024.2408344
PMID:39379324
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11469439/
Abstract

Antibodies have emerged as the leading class of biotherapeutics, yet traditional screening methods face significant time and resource challenges in identifying lead candidates. Integrating high-throughput sequencing with computational approaches marks a pivotal advancement in antibody discovery, expanding the antibody space to explore. In this context, a major breakthrough has been the full-length sequencing of single-chain variable fragments (scFvs) used in display libraries. However, few tools address the task of annotating the paired heavy and light chain variable domains (VH and VL), which is the primary advantage of full-scFv sequencing. To address this methodological gap, we introduce Seq2scFv, a novel open-source toolkit designed for analyzing display libraries from long-read sequencing platforms. Seq2scFv facilitates the identification and thorough characterization of V(D)J recombination in both VH and VL regions. In addition to providing annotated scFvs, translated sequences and numbered chains, Seq2scFv enables linker inference and characterization, sequence encoding with unique identifiers and quantification of identical sequences across selection rounds, thereby simplifying enrichment identification. With its versatile and standalone functionality, we anticipate that the implementation of Seq2scFv tools in antibody discovery pipelines will efficiently expedite the full characterization of display libraries and potentially facilitate the identification of high-affinity antibody candidates.

摘要

抗体已成为主要的生物治疗药物类别,但传统的筛选方法在确定先导候选物方面面临着巨大的时间和资源挑战。将高通量测序与计算方法相结合,标志着抗体发现的重大进展,拓展了抗体的探索空间。在这种情况下,一个重大突破是对展示文库中使用的单链可变片段(scFv)进行全长测序。然而,很少有工具能够完成配对的重链和轻链可变区(VH 和 VL)的注释任务,这是全长 scFv 测序的主要优势。为了解决这个方法学上的差距,我们引入了 Seq2scFv,这是一个专门为分析来自长读测序平台的展示文库而设计的新型开源工具包。Seq2scFv 有助于识别和全面描述 VH 和 VL 区域中的 V(D)J 重组。除了提供注释的 scFv、翻译序列和编号链外,Seq2scFv 还能够推断和描述连接子、用唯一标识符进行序列编码以及量化选择轮次之间的相同序列,从而简化了富集识别。凭借其多功能和独立的功能,我们预计在抗体发现管道中实施 Seq2scFv 工具将有效地加速展示文库的全面表征,并有可能促进高亲和力抗体候选物的鉴定。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e48/11469439/f1a34fabbcf5/KMAB_A_2408344_F0005_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e48/11469439/f544cb0545ad/KMAB_A_2408344_F0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e48/11469439/b8ef57ad855c/KMAB_A_2408344_F0002_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e48/11469439/130a246f8f4b/KMAB_A_2408344_F0003_B.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e48/11469439/f5e445354341/KMAB_A_2408344_F0004_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e48/11469439/f1a34fabbcf5/KMAB_A_2408344_F0005_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e48/11469439/f544cb0545ad/KMAB_A_2408344_F0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e48/11469439/b8ef57ad855c/KMAB_A_2408344_F0002_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e48/11469439/130a246f8f4b/KMAB_A_2408344_F0003_B.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e48/11469439/f5e445354341/KMAB_A_2408344_F0004_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e48/11469439/f1a34fabbcf5/KMAB_A_2408344_F0005_OC.jpg

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本文引用的文献

1
For antibody sequence generative modeling, mixture models may be all you need.对于抗体序列生成建模,混合模型可能就是你所需要的。
Bioinformatics. 2024 May 2;40(5). doi: 10.1093/bioinformatics/btae278.
2
Deep mining of antibody phage-display selections using Oxford Nanopore Technologies and Dual Unique Molecular Identifiers.使用牛津纳米孔技术和双独特分子标识符对抗体噬菌体展示选择进行深度挖掘。
N Biotechnol. 2024 May 25;80:56-68. doi: 10.1016/j.nbt.2024.02.001. Epub 2024 Feb 12.
3
Insights into next generation sequencing guided antibody selection strategies.
下一代测序指导的抗体选择策略的见解。
Sci Rep. 2023 Oct 26;13(1):18370. doi: 10.1038/s41598-023-45538-w.
4
Specific attributes of the V domain influence both the structure and structural variability of CDR-H3 through steric effects.V结构域的特定属性通过空间效应影响CDR-H3的结构和结构变异性。
Front Immunol. 2023 Jul 26;14:1223802. doi: 10.3389/fimmu.2023.1223802. eCollection 2023.
5
Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads.使用 UMI 标记的短读长对高度同源抗体文库中的全长 Fv 进行精确分析。
Nucleic Acids Res. 2023 Jun 23;51(11):e61. doi: 10.1093/nar/gkad235.
6
Assessing developability early in the discovery process for novel biologics.评估新型生物制剂发现过程中的可开发性。
MAbs. 2023 Jan-Dec;15(1):2171248. doi: 10.1080/19420862.2023.2171248.
7
Computational and artificial intelligence-based methods for antibody development.基于计算和人工智能的抗体开发方法。
Trends Pharmacol Sci. 2023 Mar;44(3):175-189. doi: 10.1016/j.tips.2022.12.005. Epub 2023 Jan 18.
8
Observed Antibody Space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences.观察到的抗体空间:一个多样化的数据库,包含经过清理、注释和翻译的未配对和配对抗体序列。
Protein Sci. 2022 Jan;31(1):141-146. doi: 10.1002/pro.4205. Epub 2021 Oct 29.
9
A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding.一套简洁的互补位-表位相互作用词汇表能够实现抗体-抗原结合的可预测性。
Cell Rep. 2021 Mar 16;34(11):108856. doi: 10.1016/j.celrep.2021.108856.
10
Combining phage display with SMRTbell next-generation sequencing for the rapid discovery of functional scFv fragments.将噬菌体展示与 SMRTbell 第二代测序相结合,快速发现功能性 scFv 片段。
MAbs. 2021 Jan-Dec;13(1):1864084. doi: 10.1080/19420862.2020.1864084.