Innovation Center for Biomedical Informatics, Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington DC; School of Systems Biology, George Mason University, Manassas, Virginia.
School of Systems Biology, George Mason University, Manassas, Virginia.
Biophys J. 2021 Jan 19;120(2):189-204. doi: 10.1016/j.bpj.2020.12.002. Epub 2020 Dec 15.
Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-β peptide. The new method successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model.
特定基因中的不同错义突变与不同疾病以及疾病严重程度相关。目前的计算方法预测错义变异的潜在致病性，但无法区分不同的疾病或严重程度表型。我们开发了一种通过将机器学习应用于从分子动力学模拟中提取的特征来克服这一限制的方法，从而能够预测新型遗传变异导致疾病、耐药性或其他特定特征的方式。例如，我们已经将这种新方法应用于与两种不同心律失常以及由淀粉样蛋白-β肽变异引起的两种不同神经退行性疾病相关的钙调蛋白变异。新方法成功预测了由基因突变引起的特定疾病，并比现有方法更准确地对其严重程度进行排序。我们称这种方法为分子动力学表型预测模型。
