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Abstract：
The fibroblast growth factor receptor 2 (FGFR2) gene is one of the most extensively studied genes with many known mutations implicated in several human disorders, including oncogenic ones. Most FGFR2 disease-associated gene mutations are missense mutations that result in constitutive activation of the FGFR2 protein and downstream molecular pathways. Many tertiary structures of the FGFR2 kinase domain are publicly available in the wildtype and mutated forms and in the inactive and activated state of the receptor. The current literature suggests a molecular brake inhibiting the ATP-binding A loop from adopting the activated state. Mutations relieve this brake, triggering allosteric changes between active and inactive states. However, the existing analysis relies on static structures and fails to account for the intrinsic structural dynamics. In this study, we utilize experimentally resolved structures of the FGFR2 tyrosine kinase domain and machine learning to capture the intrinsic structural dynamics, correlate it with functional regions and disease types, and enrich it with predicted structures of variants with currently no experimentally resolved structures. Our findings demonstrate the value of machine learning-enabled characterizations of structure dynamics in revealing the impact of mutations on (dys)function and disorder in FGFR2.
摘要：
成纤维细胞生长因子受体2（FGFR2）基因是研究最广泛的基因之一，具有许多已知的突变与几种人类疾病有关。包括致癌的。大多数FGFR2疾病相关基因突变是错义突变，导致FGFR2蛋白和下游分子途径的组成型激活。FGFR2激酶结构域的许多三级结构在野生型和突变形式以及受体的失活和活化状态中是公开可用的。目前的文献表明分子制动抑制ATP结合A环采用活化状态。突变减轻了刹车，触发活性和非活性状态之间的变构变化。然而,现有的分析依赖于静态结构，没有考虑到内在的结构动力学。在这项研究中,我们利用FGFR2酪氨酸激酶域的实验解析结构和机器学习来捕获内在的结构动力学，将其与功能区域和疾病类型相关联，并用预测的变体结构丰富它，目前没有实验解析的结构。我们的发现证明了机器学习的结构动力学特征在揭示突变对FGFR2中（dys）功能和紊乱的影响方面的价值。
