Biochemical Activity of 17 Cancer-Associated Variants of DNA Polymerase Kappa Predicted by Electrostatic Properties
Biochemical Activity of 17 Cancer-Associated Variants of DNA Polymerase Kappa Predicted by Electrostatic Properties
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DOI：
10.1021/acs.chemrestox.3c00233
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发表时间：
2023-10-26
影响因子：
4.1
通讯作者：
Beuning，Penny J.
中科院分区：
文献类型：
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作者：
Kankanamge，Lakindu S. Pathira;Mora，Alexandra;Beuning，Penny J.
DNA损伤和修复已被广泛研究与癌症和治疗。Y家族DNA聚合酶可以绕过DNA损伤，这可能是由外部或内部DNA损伤剂，包括一些化疗剂。Y家族聚合酶人pol κ的过表达可导致肿瘤发生和癌症的耐药性。本报告描述了使用计算工具来预测单核苷酸多态性变体对pol κ活性的影响。使用偏序最优似然法（POOL），一种使用来自理论显微滴定曲线拟合（THEMATICS）的输入特征的机器学习方法，用于鉴定最可能参与催化活性的氨基酸残基。μ4值是从POOL和THEMATICS获得的一种度量，用作一种可电离氨基酸及其相邻氨基酸之间偶联程度的量度，然后用于鉴定哪些蛋白质突变可能影响生化活性。生物信息学工具SIFT、PolyPhen-2和FATHMM预测这些变体中的大多数对功能有害。沿着计算和生物信息学预测，我们表征了17种癌症相关DNA pol κ变体的催化活性和稳定性。我们鉴定了pol κ变体R48 I、H105 Y、G147 D、G154 E、V177 L、R298 C、E362 V和R470 C相对于野生型pol κ具有较低的活性; pol κ变体T102A、H142Y、R175Q、E210K、Y221C、N330D、N338S、K353T，和L383 F被鉴定为在催化效率上与WT pol kappa相似。我们观察到，POOL预测可用于预测哪些变体具有降低的活性。来自生物信息学工具如SIFT、PolyPhen-2和FATHMM的预测基于序列比较，因此与POOL互补，但预测生化活性的能力较低。这些生物信息学和计算工具可用于从大数据集中鉴定具有有害影响和改变的生化活性的SNP变体。
DNA damage and repair have been widely studied in relation to cancer and therapeutics. Y-family DNA polymerases can bypass DNA lesions, which may result from external or internal DNA damaging agents, including some chemotherapy agents. Overexpression of the Y-family polymerase human pol kappa can result in tumorigenesis and drug resistance in cancer. This report describes the use of computational tools to predict the effects of single nucleotide polymorphism variants on pol kappa activity. Partial Order Optimum Likelihood (POOL), a machine learning method that uses input features from Theoretical Microscopic Titration Curve Shapes (THEMATICS), was used to identify amino acid residues most likely involved in catalytic activity. The μ4 value, a metric obtained from POOL and THEMATICS that serves as a measure of the degree of coupling between one ionizable amino acid and its neighbors, was then used to identify which protein mutations are likely to impact the biochemical activity. Bioinformatic tools SIFT, PolyPhen-2, and FATHMM predicted most of these variants to be deleterious to function. Along with computational and bioinformatic predictions, we characterized the catalytic activity and stability of 17 cancer-associated DNA pol kappa variants. We identified pol kappa variants R48I, H105Y, G147D, G154E, V177L, R298C, E362V, and R470C as having lower activity relative to wild-type pol kappa; the pol kappa variants T102A, H142Y, R175Q, E210K, Y221C, N330D, N338S, K353T, and L383F were identified as being similar in catalytic efficiency to WT pol kappa. We observed that POOL predictions can be used to predict which variants have decreased activity. Predictions from bioinformatic tools like SIFT, PolyPhen-2, and FATHMM are based on sequence comparisons and therefore are complementary to POOL but are less capable of predicting biochemical activity. These bioinformatic and computational tools can be used to identify SNP variants with deleterious effects and altered biochemical activity from a large data set.