Many protein engineering problems involve finding mutations that produce proteins
with a particular function. Computational active learning is an attractive
approach to discover desired biological activities. Traditional active learning
techniques have been optimized to iteratively improve classifier accuracy, not
to quickly discover biologically significant results. We report here a novel
active learning technique, Most Informative Positive (MIP), which is tailored to
biological problems because it seeks novel and informative positive results. MIP
active learning differs from traditional active learning methods in two ways:
(1) it preferentially seeks Positive (functionally active) examples; and (2) it
may be effectively extended to select gene regions suitable for high throughput
combinatorial mutagenesis. We applied MIP to discover mutations in the tumor
suppressor protein p53 that reactivate mutated p53 found in human cancers. This
is an important biomedical goal because p53 mutants have been
implicated in half of all human cancers, and restoring active p53 in tumors
leads to tumor regression. MIP found Positive (cancer rescue) p53 mutants
in silico using 33% fewer experiments than
traditional non-MIP active learning, with only a minor decrease in classifier
accuracy. Applying MIP to in vivo experimentation yielded
immediate Positive results. Ten different p53 mutations found in human cancers
were paired in silico with all possible single amino acid
rescue mutations, from which MIP was used to select a Positive Region predicted
to be enriched for p53 cancer rescue mutants. In vivo assays
showed that the predicted Positive Region: (1) had significantly more
(p<0.01) new strong cancer rescue mutants than control regions (Negative,
and non-MIP active learning); (2) had slightly more new strong cancer rescue
mutants than an Expert region selected for purely biological considerations; and
(3) rescued for the first time the previously unrescuable p53 cancer mutant
P152L