  Designing novel functional proteins remains a slow and expensive process due
to a variety of protein engineering challenges; in particular, the number of
protein variants that can be experimentally tested in a given assay pales in
comparison to the vastness of the overall sequence space, resulting in low hit
rates and expensive wet lab testing cycles. In this paper, we propose a
few-shot learning approach to novel protein design that aims to accelerate the
expensive wet lab testing cycle and is capable of leveraging a training dataset
that is both small and skewed ($\approx 10^5$ datapoints, $< 1\%$ positive
hits). Our approach is composed of two parts: a semi-supervised transfer
learning approach to generate a discrete fitness landscape for a desired
protein function and a novel evolutionary Monte Carlo Markov Chain sampling
algorithm to more efficiently explore the fitness landscape. We demonstrate the
performance of our approach by experimentally screening predicted high fitness
gene activators, resulting in a dramatically improved hit rate compared to
existing methods. Our method can be easily adapted to other protein engineering
and design problems, particularly where the cost associated with obtaining
labeled data is significantly high. We have provided open source code for our
method at https://
github.com/SuperSecretBioTech/evolutionary_monte_carlo_search.
