Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd, MSN 3030, Kansas City, KS, 66160, USA.
Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd, MSN 3030, Kansas City, KS, 66160, USA.
Arch Biochem Biophys. 2024 Nov;761:110183. doi: 10.1016/j.abb.2024.110183. Epub 2024 Oct 24.
According to evolutionary theory, many naturally-occurring amino acid substitutions are expected to be neutral or near-neutral, with little effect on protein structure or function. Accordingly, most changes observed in human exomes are also expected to be neutral. As such, accurate algorithms for identifying medically-relevant changes must discriminate rare, non-neutral substitutions against a background of neutral substitutions. However, due to historical biases in biochemical experiments, the data available to train and validate prediction algorithms mostly contains non-neutral substitutions, with few examples of neutral substitutions. Thus, available training sets have the opposite composition of the desired test sets. Towards improving a dataset of these critical negative controls, we have concentrated on identifying neutral positions - those positions for which most of the possible 19 amino acid substitutions have little effect on protein structure or function. Here, we used a strategy based on multiple sequence alignments to identify putative neutral positions in human aldolase A, followed by biochemical assays for 147 aldolase substitutions. Results showed that most variants had little effect on either the apparent Michaelis constant for substrate fructose-1,6-bisphosphate or its apparent cooperativity. Thus, these data are useful for training and validating prediction algorithms. In addition, we created a database of these and other biochemically characterized aldolase variants along with aldolase sequences and characteristics derived from sequence and structure analyses. This database is publicly available at https://github.com/liskinsk/Aldolase-variant-and-sequence-database.
根据进化理论，许多自然发生的氨基酸替换预计是中性或近中性的，对蛋白质结构或功能几乎没有影响。因此，在人类外显子组中观察到的大多数变化也预计是中性的。因此，用于识别医学相关变化的准确算法必须在中性替换的背景下区分稀有、非中性替换。然而，由于生化实验的历史偏见，用于训练和验证预测算法的数据大多包含非中性替换，而中性替换的例子很少。因此，可用的训练集与所需的测试集的组成相反。为了改进这些关键阴性对照的数据集，我们专注于识别中性位置 - 即大多数可能的 19 种氨基酸替换对蛋白质结构或功能几乎没有影响的位置。在这里，我们使用基于多序列比对的策略来鉴定人醛缩酶 A 中的假定中性位置，然后对 147 个醛缩酶替换进行生化测定。结果表明，大多数变体对底物果糖-1,6-二磷酸的表观米氏常数或其表观协同性几乎没有影响。因此，这些数据可用于训练和验证预测算法。此外，我们创建了一个数据库，其中包含这些和其他经过生化表征的醛缩酶变体以及来自序列和结构分析的醛缩酶序列和特征。该数据库可在 https://github.com/liskinsk/Aldolase-variant-and-sequence-database 上公开获得。
