School of Chemistry and Molecular Bioscience, University of Queensland, Brisbane Queensland 4072, Australia.
Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne Victoria 3004, Australia.
Brief Bioinform. 2023 Nov 22;25(1). doi: 10.1093/bib/bbad428.
Dysfunctions caused by missense mutations in the tumour suppressor p53 have been extensively shown to be a leading driver of many cancers. Unfortunately, it is time-consuming and labour-intensive to experimentally elucidate the effects of all possible missense variants. Recent works presented a comprehensive dataset and machine learning model to predict the functional outcome of mutations in p53. Despite the well-established dataset and precise predictions, this tool was trained on a complicated model with limited predictions on p53 mutations. In this work, we first used computational biophysical tools to investigate the functional consequences of missense mutations in p53, informing a bias of deleterious mutations with destabilizing effects. Combining these insights with experimental assays, we present two interpretable machine learning models leveraging both experimental assays and in silico biophysical measurements to accurately predict the functional consequences on p53 and validate their robustness on clinical data. Our final model based on nine features obtained comparable predictive performance with the state-of-the-art p53 specific method and outperformed other generalized, widely used predictors. Interpreting our models revealed that information on residue p53 activity, polar atom distances and changes in p53 stability were instrumental in the decisions, consistent with a bias of the properties of deleterious mutations. Our predictions have been computed for all possible missense mutations in p53, offering clinical diagnostic utility, which is crucial for patient monitoring and the development of personalized cancer treatment.
肿瘤抑制因子 p53 中的错义突变导致的功能障碍已被广泛证明是许多癌症的主要驱动因素。不幸的是，实验性地阐明所有可能的错义变体的影响既耗时又费力。最近的研究工作提出了一个全面的数据集和机器学习模型，用于预测 p53 突变的功能结果。尽管该工具具有既定的数据集和精确的预测，但它是在一个复杂的模型上进行训练的，对 p53 突变的预测有限。在这项工作中，我们首先使用计算生物物理工具来研究 p53 中的错义突变的功能后果，这些工具提示具有不稳定性的有害突变具有偏向性。我们将这些见解与实验分析结合起来，提出了两种可解释的机器学习模型，利用实验分析和计算生物物理测量来准确预测 p53 的功能后果，并在临床数据上验证其稳健性。我们基于九个特征的最终模型与最先进的 p53 特异性方法具有可比的预测性能，并优于其他通用的、广泛使用的预测器。对我们的模型进行解释表明，残基 p53 活性、极性原子距离和 p53 稳定性变化的信息对决策至关重要，这与有害突变的特性偏向一致。我们已经为 p53 中的所有可能的错义突变计算了预测，这为临床诊断提供了实用价值，对于患者监测和个性化癌症治疗的发展至关重要。
