Towards a Greener AlphaFold2 Protocol for Antibody-Antigen Modeling: Insights From CAPRI Round 55.

IF 2.8 4区生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Proteins-Structure Function and Bioinformatics Pub Date : 2025-03-03 DOI:10.1002/prot.26820

Büşra Savaş, İrem Yılmazbilek, Atakan Özsan, Ezgi Karaca

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Abstract

In the 55th round of CAPRI, we used enhanced AlphaFold2 (AF2) sampling and data-driven docking. Our AF2 protocol relies on Wallner's massive sampling approach, which combines different AF2 versions and sampling parameters to produce thousands of models per target. For T231 (an antibody-peptide complex) and T232 (PP2A:TIPRL complex), we employed a 50-fold reduced MinnieFold sampling and a custom ranking approach, leading to a top-ranking medium prediction in both cases. For T233 and T234 (two antibody bound MHC I complexes), we followed data-driven docking, which did not lead to an acceptable model. Our post-CAPRI55 analysis showed that if we had used our MinnieFold approach on T233 and T234, we could have submitted a medium-quality model for T233 as well. In the scoring challenge, we utilized the scoring function of FoldX, which was effective in selecting acceptable models for T231 and medium-quality models for T232. Our success, especially in predicting and ranking a medium-quality model for T231 and potentially for T233, underscores the feasibility of green and accurate enhanced AF2 sampling in antibody complex prediction.

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迈向更绿色的AlphaFold2抗体-抗原建模方案：来自CAPRI第55轮的见解。

在第55轮CAPRI中，我们使用了增强型AlphaFold2（AF2）采样和数据驱动对接。我们的 AF2 方案依赖于 Wallner 的大规模采样方法，该方法结合了不同的 AF2 版本和采样参数，可为每个靶标生成数千个模型。对于 T231（抗体-肽复合物）和 T232（PP2A:TIPRL 复合物），我们采用了减少 50 倍的 MinnieFold 采样和自定义排序方法，从而在这两种情况下都获得了最高级别的中等预测结果。对于 T233 和 T234（两个与抗体结合的 MHC I 复合物），我们采用了数据驱动对接法，但没有得出可接受的模型。我们的 CAPRI55 后分析表明，如果我们在 T233 和 T234 上使用 MinnieFold 方法，我们也可以为 T233 提交一个中等质量的模型。在评分挑战中，我们使用了 FoldX 的评分功能，它有效地为 T231 挑选出了可接受的模型，为 T232 挑选出了中等质量的模型。我们的成功，尤其是为 T231 以及可能为 T233 预测中等质量模型并对其进行排名的成功，凸显了在抗体复合物预测中进行绿色、准确的增强型 AF2 采样的可行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proteins-Structure Function and Bioinformatics 生物-生化与分子生物学

CiteScore

5.90

自引率

3.40%

发文量

172

审稿时长

3 months

期刊介绍： PROTEINS : Structure, Function, and Bioinformatics publishes original reports of significant experimental and analytic research in all areas of protein research: structure, function, computation, genetics, and design. The journal encourages reports that present new experimental or computational approaches for interpreting and understanding data from biophysical chemistry, structural studies of proteins and macromolecular assemblies, alterations of protein structure and function engineered through techniques of molecular biology and genetics, functional analyses under physiologic conditions, as well as the interactions of proteins with receptors, nucleic acids, or other specific ligands or substrates. Research in protein and peptide biochemistry directed toward synthesizing or characterizing molecules that simulate aspects of the activity of proteins, or that act as inhibitors of protein function, is also within the scope of PROTEINS. In addition to full-length reports, short communications (usually not more than 4 printed pages) and prediction reports are welcome. Reviews are typically by invitation; authors are encouraged to submit proposed topics for consideration.