The Australian Centre for Ecogenomics, School of Chemistry and Molecular Bioscience, University of Queensland, Brisbane, Australia.
Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia.
Protein Sci. 2024 Oct;33(10):e5147. doi: 10.1002/pro.5147.
Alzheimer's disease (AD) is one of the most common forms of dementia and neurodegenerative diseases, characterized by the formation of neuritic plaques and neurofibrillary tangles. Many different proteins participate in this complicated pathogenic mechanism, and missense mutations can alter the folding and functions of these proteins, significantly increasing the risk of AD. However, many methods to identify AD-causing variants did not consider the effect of mutations from the perspective of a protein three-dimensional environment. Here, we present a machine learning-based analysis to classify the AD-causing mutations from their benign counterparts in 21 AD-related proteins leveraging both sequence- and structure-based features. Using computational tools to estimate the effect of mutations on protein stability, we first observed a bias of the pathogenic mutations with significant destabilizing effects on family AD-related proteins. Combining this insight, we built a generic predictive model, and improved the performance by tuning the sample weights in the training process. Our final model achieved the performance on area under the receiver operating characteristic curve up to 0.95 in the blind test and 0.70 in an independent clinical validation, outperforming all the state-of-the-art methods. Feature interpretation indicated that the hydrophobic environment and polar interaction contacts were crucial to the decision on pathogenic phenotypes of missense mutations. Finally, we presented a user-friendly web server, AlzDiscovery, for researchers to browse the predicted phenotypes of all possible missense mutations on these 21 AD-related proteins. Our study will be a valuable resource for AD screening and the development of personalized treatment.
阿尔茨海默病（AD）是最常见的痴呆症和神经退行性疾病之一，其特征是神经原纤维缠结和神经纤维缠结的形成。许多不同的蛋白质参与了这一复杂的致病机制，错义突变可以改变这些蛋白质的折叠和功能，显著增加 AD 的风险。然而，许多识别 AD 致病变体的方法并没有从蛋白质三维环境的角度考虑突变的影响。在这里，我们提出了一种基于机器学习的分析方法，利用序列和结构特征来区分 21 种 AD 相关蛋白中的 AD 致病突变与良性突变。利用计算工具来估计突变对蛋白质稳定性的影响，我们首先观察到致病性突变对家族性 AD 相关蛋白有显著的不稳定作用。结合这一见解，我们构建了一个通用的预测模型，并通过在训练过程中调整样本权重来提高性能。我们的最终模型在盲测中的表现达到了接收者操作特征曲线下面积 0.95，在独立的临床验证中达到了 0.70，优于所有最先进的方法。特征解释表明，疏水环境和极性相互作用接触对错义突变致病表型的决策至关重要。最后，我们展示了一个用户友好的网络服务器 AlzDiscovery，供研究人员浏览这 21 种 AD 相关蛋白上所有可能的错义突变的预测表型。我们的研究将为 AD 筛查和个性化治疗的发展提供有价值的资源。
