Abstract
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.