利用 AlphaFold 3 辅助拓扑深度学习快速应对病毒的快速进化。

ArXiv Pub Date : 2024-11-19
JunJie Wee, Guo-Wei Wei
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引用次数: 0

摘要

SARS-CoV-2 和其他传染性病毒的快速演变给病毒追踪、诊断、单克隆抗体(mAbs)和疫苗的设计与制造等快速反应带来了巨大挑战,而这些工作既耗时又昂贵。这凸显了对高效计算方法的需求。拓扑深度学习(TDL)等最新研究成果为预测新出现的优势变体提供了强大的工具,但它们需要对病毒表面蛋白和相关的三维(3D)蛋白-蛋白相互作用(PPI)复合物结构进行深度突变扫描(DMS)。我们提出了一种由 AlphaFold 3 (AF3) 辅助的多任务拓扑拉普拉斯(MT-TopLap)策略来满足这一需求。MT-TopLap 将深度学习与拓扑数据分析(TDA)模型(如持久性拉普拉斯(PL))相结合,提取 PPI 的详细拓扑和几何特征,从而增强对病毒突变时 DMS 和结合自由能(BFE)变化的预测。利用 SARS-CoV-2 穗状受体结合域 (RBD) 和人类血管紧张素转换酶-2 (ACE2) 复合物的四个实验 DMS 数据集进行的验证表明,我们的 AF3 辅助 MT-TopLap 策略保持了稳健的性能,与使用实验结构相比,皮尔逊相关系数 (PCC) 平均仅降低 1.1%,均方根误差 (RMSE) 平均增加 9.3%。此外,在使用 SARS-CoV-2 HK.3 变异 DMS 数据集进行测试时,AF3 辅助 MT-TopLap 的 PCC 达到了 0.81,证实了其准确预测 BFE 变化和适应新实验数据的能力,从而展示了其快速有效应对病毒快速进化的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning.

The fast evolution of SARS-CoV-2 and other infectious viruses poses a grand challenge to the rapid response in terms of viral tracking, diagnostics, and design and manufacture of monoclonal antibodies (mAbs) and vaccines, which are both time-consuming and costly. This underscores the need for efficient computational approaches. Recent advancements, like topological deep learning (TDL), have introduced powerful tools for forecasting emerging dominant variants, yet they require deep mutational scanning (DMS) of viral surface proteins and associated three-dimensional (3D) protein-protein interaction (PPI) complex structures. We propose an AlphaFold 3 (AF3)-assisted multi-task topological Laplacian (MT-TopLap) strategy to address this need. MT-TopLap combines deep learning with topological data analysis (TDA) models, such as persistent Laplacians (PL) to extract detailed topological and geometric characteristics of PPIs, thereby enhancing the prediction of DMS and binding free energy (BFE) changes upon virus mutations. Validation with four experimental DMS datasets of SARS-CoV-2 spike receptor-binding domain (RBD) and the human angiotensin-converting enzyme-2 (ACE2) complexes indicates that our AF3 assisted MT-TopLap strategy maintains robust performance, with only an average 1.1% decrease in Pearson correlation coefficients (PCC) and an average 9.3% increase in root mean square errors (RMSE), compared with the use of experimental structures. Additionally, AF3-assisted MT-TopLap achieved a PCC of 0.81 when tested with a SARS-CoV-2 HK.3 variant DMS dataset, confirming its capability to accurately predict BFE changes and adapt to new experimental data, thereby showcasing its potential for rapid and effective response to fast viral evolution.

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