Figure 3

Substitution weight mean and standard deviation values produced by the MR algorithm. We created 1000 subsamples of the training set (the sequences and non-zero activities of variants from sets 1 and 2) by leaving out 5 randomly selected variants from each subsample. A: The MR (matching loss) algorithm was used to calculate substitution weights for each subsample. The mean values from the 1000 subsamples are indicated by horizontal notches. Error bars represent one standard deviation of the 1000 calculated substitution weights. Substitutions are indicated below the graph with the number of occurrences in the training set in parentheses. Each substitution is described by a single weight. Variant 3–4 was designed to include all substitutions with positive mean weight that occur at least 3 times in the training set (red and blue circles). Note that substitution Y194S (green circle) was not selected since it occurred less than 3 times in the training set. Variant 3–9 included all substitutions that occurred at least 3 times and whose mean weight was at least one standard deviation above zero (red circles only). Substitution weights calculated from the entire dataset instead of the mean of 1000 subsamples are shown as purple circles. B: The MR algorithm was used to calculate substitution weights as in A, except that models were tested by expanding each pair in turn into 4 terms and selecting the pair that most improved the model. In this example each substitution is described by a single weight except for the 3 pairs (132,208), (337,355), (267,293) which are modeled by 4 weights each. Re d circles indicate the substitutions selected to design variant 3–14. Note that substitution combination I132V 208K was not selected since it occurred less than 3 times in the training set.