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通过AlphaFold、HADDOCK和分子动力学模拟验证了BioPhi驱动的人源化优化在单链抗体片段-CD99结合亲和力方面的可塑性。

Plasticity of BioPhi-driven humanness optimization in ScFv-CD99 binding affinity validated through AlphaFold, HADDOCK, and MD simulations.

作者信息

Sornsuwan Kanokporn, Pamonsupornwichit Thanathat, Juntit On-Anong, Thongkum Weeraya, Takheaw Nuchjira, Kodchakorn Kanchanok, Tayapiwatana Chatchai

机构信息

Office of Research Administration, Chiang Mai University, Chiang Mai 50200, Thailand.

Center of Biomolecular Therapy and Diagnostic, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai 50200, Thailand.

出版信息

Comput Struct Biotechnol J. 2025 Jan 7;27:369-382. doi: 10.1016/j.csbj.2025.01.001. eCollection 2025.

DOI:10.1016/j.csbj.2025.01.001
PMID:39897056
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11786912/
Abstract

BioPhi-guided humanization was utilized to enhance the humanness of a humanized single-chain variable fragment targeting CD99, leading to the development of two variants: HuScFvMT99/3 and HuScFvMT99/3. The HuScFvMT99/3 variant incorporated framework region modifications, leading to modest improvements in humanness, particularly in the VH domain, although the VL domain remained suboptimal. To address this limitation, HuScFvMT99/3 was designed by combining the VL domain of wild-type with the VH domain of HuScFvMT99/3. Molecular dynamics simulations employing AlphaFold2, AlphaFold3, and HADDOCK were performed to evaluate the HuScFv-CD99 peptide complexes. AF2-based simulations demonstrated enhanced binding free energy (ΔG) for both variants compared to HuScFvMT99/3. However, ΔG values obtained from AF3 and HD simulations were inconsistent, with HuScFvMT99/3 exhibiting the weakest binding affinity. While ΔG patterns derived from AlphaFold3 and HADDOCK simulations aligned, amino acid decomposition analysis revealed variations in the interaction coordinates of the predicted complexes. Root-mean-square deviation analysis indicated improved structural stability for HuScFvMT99/3 (0.975 Å) and HuScFvMT99/3 (1.075 Å) relative to HuScFvMT99/3 (1.225 Å). Biolayer interferometry further confirmed that HuScFvMT99/3 exhibited the highest binding affinity (K = 1.35 × 10⁻⁷ M) compared to HuScFvMT99/3 (K = 2.64 × 10⁻⁷ M) and HuScFvMT99/3 (K = 3.95 × 10⁻⁷ M). Supporting evidence was provided by ELISA and flow cytometry experiments. PITHA analysis revealed a high immunogenicity risk for all variants, despite HuScFvMT99/3 displaying improved humanness, a larger complementarity-determining region (CDR) cavity, and a more hydrophobic CDR-H3 loop. These findings highlight the delicate balance between enhancing humanness and preserving the structural and functional integrity critical for therapeutic antibody development.

摘要

利用生物信息学引导的人源化方法增强了靶向CD99的人源化单链可变片段的人源化程度,从而开发出两种变体:HuScFvMT99/3和HuScFvMT99/3。HuScFvMT99/3变体引入了框架区修饰,使人源化程度有适度提高,特别是在VH结构域,尽管VL结构域仍不理想。为解决这一局限性,通过将野生型的VL结构域与HuScFvMT99/3的VH结构域结合来设计HuScFvMT99/3。使用AlphaFold2、AlphaFold3和HADDOCK进行分子动力学模拟以评估HuScFv-CD99肽复合物。基于AF2的模拟表明,与HuScFvMT99/3相比,两种变体的结合自由能(ΔG)均增强。然而,从AF3和HD模拟获得的ΔG值不一致,其中HuScFvMT99/3表现出最弱的结合亲和力。虽然来自AlphaFold3和HADDOCK模拟的ΔG模式一致,但氨基酸分解分析揭示了预测复合物相互作用坐标的差异。均方根偏差分析表明,相对于HuScFvMT99/3(1.225 Å),HuScFvMT99/3(0.975 Å)和HuScFvMT99/3(1.075 Å)的结构稳定性有所提高。生物层干涉术进一步证实,与HuScFvMT99/3(K = 2.64×10⁻⁷ M)和HuScFvMT99/3(K = 3.95×10⁻⁷ M)相比,HuScFvMT99/3表现出最高的结合亲和力(K = 1.35×10⁻⁷ M)。ELISA和流式细胞术实验提供了支持证据。PITHA分析表明,所有变体均具有较高的免疫原性风险,尽管HuScFvMT99/3表现出改善的人源化程度、更大的互补决定区(CDR)腔和更疏水的CDR-H3环。这些发现突出了在增强人源化与保持对治疗性抗体开发至关重要的结构和功能完整性之间的微妙平衡。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/9e35fb569474/gr14.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/9e35fb569474/gr14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/945a664db134/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/221cbb47e78a/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/596dfe2fc22c/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/0fa538975f7a/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/26e7f2c0fb3e/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/ed07cc7ffc0f/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/e5766537947a/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/46706c9f9ad0/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/cbd594669b78/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/9930efb315c8/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/ace73c3ec630/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/f506854eb43a/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/cf72c34b4be4/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/a6f73154584d/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/11786912/9e35fb569474/gr14.jpg

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2
Engineering affinity of humanized ScFv targeting CD147 antibody: A combined approach of mCSM-AB2 and molecular dynamics simulations.靶向 CD147 抗体的人源化 ScFv 的工程亲和力:mCSM-AB2 和分子动力学模拟的联合方法。
J Mol Graph Model. 2024 Dec;133:108884. doi: 10.1016/j.jmgm.2024.108884. Epub 2024 Oct 13.
3
Next-Generation Therapeutic Antibodies for Cancer Treatment: Advancements, Applications, and Challenges.
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Mol Biotechnol. 2024 Sep 2. doi: 10.1007/s12033-024-01270-y.
4
The HADDOCK2.4 web server for integrative modeling of biomolecular complexes.HADDOCK2.4 网页服务器用于生物分子复合物的整合建模。
Nat Protoc. 2024 Nov;19(11):3219-3241. doi: 10.1038/s41596-024-01011-0. Epub 2024 Jun 17.
5
RCSB protein Data Bank: exploring protein 3D similarities via comprehensive structural alignments.RCSB 蛋白质数据库:通过全面的结构比对探索蛋白质 3D 相似性。
Bioinformatics. 2024 Jun 3;40(6). doi: 10.1093/bioinformatics/btae370.
6
Prospects for the computational humanization of antibodies and nanobodies.抗体和纳米抗体计算人源化的前景。
Front Immunol. 2024 May 15;15:1399438. doi: 10.3389/fimmu.2024.1399438. eCollection 2024.
7
Accurate structure prediction of biomolecular interactions with AlphaFold 3.利用 AlphaFold 3 进行生物分子相互作用的精确结构预测。
Nature. 2024 Jun;630(8016):493-500. doi: 10.1038/s41586-024-07487-w. Epub 2024 May 8.
8
Designing stable humanized antibodies.设计稳定的人源化抗体。
Nat Biomed Eng. 2024 Jan;8(1):3-4. doi: 10.1038/s41551-023-01168-1.
9
Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy.评估 AlphaFold 抗体-抗原建模对提高预测准确性的影响。
Protein Sci. 2024 Jan;33(1):e4865. doi: 10.1002/pro.4865.
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Front Immunol. 2023 Oct 24;14:1275999. doi: 10.3389/fimmu.2023.1275999. eCollection 2023.