Department of Biochemistry and Molecular Biology, 200 First St. SW, Mayo Clinic Rochester, Rochester, MN 55905, United States; Department of Physiology and Biomedical Engineering, 200 First St. SW, Mayo Clinic Rochester, Rochester, MN 55905, United States.
Department of Biochemistry and Molecular Biology, 200 First St. SW, Mayo Clinic Rochester, Rochester, MN 55905, United States.
J Mol Cell Cardiol. 2018 Jun;119:19-27. doi: 10.1016/j.yjmcc.2018.04.006. Epub 2018 Apr 11.
The cardiac muscle sarcomere contains multiple proteins contributing to contraction energy transduction and its regulation during a heartbeat. Inheritable heart disease mutants affect most of them but none more frequently than the ventricular myosin motor and cardiac myosin binding protein c (mybpc3). These co-localizing proteins have mybpc3 playing a regulatory role to the energy transducing motor. Residue substitution and functional domain assignment of each mutation in the protein sequence decides, under the direction of a sensible disease model, phenotype and pathogenicity. The unknown model mechanism is decided here using a method combing neural and Bayes networks. Missense single nucleotide polymorphisms (SNPs) are clues for the disease mechanism summarized in an extensive database collecting mutant sequence location and residue substitution as independent variables that imply the dependent disease phenotype and pathogenicity characteristics in 4 dimensional data points (4ddps). The SNP database contains entries with the majority having one or both dependent data entries unfulfilled. A neural network relating causes (mutant residue location and substitution) and effects (phenotype and pathogenicity) is trained, validated, and optimized using fulfilled 4ddps. It then predicts unfulfilled 4ddps providing the implicit disease model. A discrete Bayes network interprets fulfilled and predicted 4ddps with conditional probabilities for phenotype and pathogenicity given mutation location and residue substitution thus relating the neural network implicit model to explicit features of the motor and mybpc3 sequence and structural domains. Neural/Bayes network forecasting automates disease mechanism modeling by leveraging the world wide human missense SNP database that is in place and expanding.
心肌肌节包含多种蛋白质，它们参与收缩能量的传递，并在心跳过程中对其进行调节。遗传性心脏病突变体影响其中的大多数，但没有一个比心室肌球蛋白马达和心脏肌球蛋白结合蛋白 c（mybpc3）更常见。这些共定位的蛋白质使 mybpc3 对能量传递马达起到调节作用。蛋白质序列中每个突变的残基取代和功能域分配，在合理疾病模型的指导下，决定表型和致病性。在这里，我们使用结合神经网络和贝叶斯网络的方法来确定未知的模型机制。错义单核苷酸多态性（SNP）是疾病机制的线索，这些线索总结在一个广泛的数据库中，该数据库收集了突变序列位置和残基取代作为独立变量，这些变量暗示了 4 维数据点（4ddps）中依赖的疾病表型和致病性特征。SNP 数据库包含大多数情况下有一个或两个依赖数据项未满足的条目。一个将原因（突变残基位置和取代）和结果（表型和致病性）联系起来的神经网络，使用已满足的 4ddps 进行训练、验证和优化。然后，它预测未满足的 4ddps，提供隐含的疾病模型。离散贝叶斯网络通过对表型和致病性的条件概率来解释已满足和预测的 4ddps，这些概率是基于突变位置和残基取代的，从而将神经网络隐含模型与马达和 mybpc3 序列和结构域的显式特征联系起来。神经/贝叶斯网络预测通过利用现有的、不断扩展的全球人类错义 SNP 数据库，实现了疾病机制建模的自动化。
