Molotkov Ivan, Mardis Elaine R, Artomov Mykyta
The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH 43215, USA.
Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43215, USA.
Dis Model Mech. 2024 Dec 1;17(12). doi: 10.1242/dmm.052218. Epub 2024 Dec 16.
Computational tools for predicting variant pathogenicity are widely used to support clinical variant interpretation. Recently, several models, which do not rely on known variant classifications during training, have been developed. These approaches can potentially overcome biases of current clinical databases, such as misclassifications, and can potentially better generalize to novel, unclassified variants. AlphaMissense is one such model, built on the highly successful protein structure prediction model, AlphaFold. AlphaMissense has shown great performance in benchmarks of functional and clinical data, outperforming many supervised models that were trained on similar data. However, like other in silico predictors, AlphaMissense has notable limitations. As a large deep learning model, it lacks interpretability, does not assess the functional impact of variants, and provides pathogenicity scores that are not disease specific. Improving interpretability and precision in computational tools for variant interpretation remains a promising area for advancing clinical genetics.
用于预测变异致病性的计算工具被广泛用于支持临床变异解读。最近，开发了几种在训练过程中不依赖已知变异分类的模型。这些方法有可能克服当前临床数据库的偏差，如错误分类，并且有可能更好地推广到新的、未分类的变异。AlphaMissense就是这样一种模型，它基于非常成功的蛋白质结构预测模型AlphaFold构建。AlphaMissense在功能和临床数据基准测试中表现出色，优于许多在类似数据上训练的监督模型。然而，与其他计算机预测器一样，AlphaMissense也有显著局限性。作为一个大型深度学习模型，它缺乏可解释性，不评估变异的功能影响，并且提供的致病性评分不是疾病特异性的。提高变异解读计算工具的可解释性和精度仍然是推进临床遗传学的一个有前景的领域。
