Transfer learning towards predicting viral missense mutations: A case study on SARS-CoV-2.

IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Computational and structural biotechnology journal Pub Date : 2025-04-22 eCollection Date: 2025-01-01 DOI:10.1016/j.csbj.2025.04.029
Shaylyn Govender, Emily Morgan, Rabelani Ramahala, Kevin Lobb, Nigel T Bishop, Özlem Tastan Bishop
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引用次数: 0

Abstract

Understanding viral evolution and predicting future mutations are crucial for overcoming drug resistance and developing long-lasting treatments. Previously, we established machine learning (ML) models using dynamic residue network (DRN) metric data and leveraging a vast amount of existing mutation data from the SARS-CoV-2 main protease (Mpro). Here, we sought to assess the generalizability and robustness of the current models across other SARS-CoV-2 proteins. To achieve this, for the first time, we employed a transfer learning (TL) approach, allowing us to determine the extent to which Mpro trained models could be applied to other SARS-CoV-2 proteins. The TL results were highly promising, with artificial neural network (ANN) and random forest (RF) correlation coefficients for Mpro closely matching those of NSP10, NSP16, and PLpro. The ANN |R| value for Mpro was 0.564, while NSP10, NSP16, and PLpro had values of 0.533, 0.527, and 0.464, respectively. Similarly, the RF |R| value for Mpro was 0.673, compared to 0.457, 0.460, and 0.437 for NSP10, NSP16, and PLpro, respectively. Interestingly, we did not observe a strong correlation for the spike (S) protein monomer and its domains. The low p-values that are associated with the correlation |R| values show that the linear correlations between predicted and actual mutation frequencies are statistically significant. This indicates that TL may generalize well across structurally related viral proteins using DRN-derived ML model from Mpro. Overall, we aim to develop a universal ML model for predicting missense mutation frequencies in viral proteins, and this study lays the foundation for that goal.

迁移学习预测病毒错义突变:以SARS-CoV-2为例
了解病毒的进化和预测未来的突变对于克服耐药性和开发持久的治疗方法至关重要。在此之前,我们利用动态残基网络(DRN)度量数据和大量现有的SARS-CoV-2主蛋白酶(Mpro)突变数据建立了机器学习(ML)模型。在这里,我们试图评估当前模型在其他SARS-CoV-2蛋白中的普遍性和稳健性。为了实现这一目标,我们首次采用了迁移学习(TL)方法,使我们能够确定Mpro训练的模型在多大程度上可以应用于其他SARS-CoV-2蛋白。TL结果显示,Mpro的人工神经网络(ANN)和随机森林(RF)相关系数与NSP10、NSP16和PLpro接近。Mpro的ANN |R|值为0.564,而NSP10、NSP16和PLpro的ANN |R|值分别为0.533、0.527和0.464。同样,Mpro的RF |R|值为0.673,而NSP10、NSP16和PLpro的RF |R|值分别为0.457、0.460和0.437。有趣的是,我们没有观察到刺突(S)蛋白单体与其结构域有很强的相关性。与相关|R|值相关的低p值表明,预测突变频率与实际突变频率之间的线性相关性具有统计学意义。这表明利用Mpro的drn衍生的ML模型,TL可以很好地推广到结构相关的病毒蛋白上。总的来说,我们的目标是开发一个通用的ML模型来预测病毒蛋白的错义突变频率,而这项研究为这一目标奠定了基础。
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
Computational and structural biotechnology journal
Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
9.30
自引率
3.30%
发文量
540
审稿时长
6 weeks
期刊介绍: Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology
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