The matrix containing the activity data of 32 previously identified HLDs and the activities obtained for all variants in this study (all measured on MicroPEX) was analyzed by PCA in MATLAB (MathWorks, United States) to uncover the relationships among individual HLDs (objects) based on their activities toward the set of halogenated substrates (variables). Two PCA models were constructed to visualize systematic trends in the dataset. The first one was done on the raw data, which ordered the enzymes according to their total activity. The second PCA was carried out on the log-transformed data after adding 1 to each specific activity to avoid taking the logarithm of zero. The resulting values were then divided by the sum of the values for a particular enzyme. These transformed data were used to calculate principal components, and the components explaining the highest variability in the data were then plotted to identify substrate specificity groups.