  Directed evolution is a versatile technique in protein engineering that
mimics the process of natural selection by iteratively alternating between
mutagenesis and screening in order to search for sequences that optimize a
given property of interest, such as catalytic activity and binding affinity to
a specified target. However, the space of possible proteins is too large to
search exhaustively in the laboratory, and functional proteins are scarce in
the vast sequence space. Machine learning (ML) approaches can accelerate
directed evolution by learning to map protein sequences to functions without
building a detailed model of the underlying physics, chemistry and biological
pathways. Despite the great potentials held by these ML methods, they encounter
severe challenges in identifying the most suitable sequences for a targeted
function. These failures can be attributed to the common practice of adopting a
high-dimensional feature representation for protein sequences and inefficient
search methods. To address these issues, we propose an efficient, experimental
design-oriented closed-loop optimization framework for protein directed
evolution, termed ODBO, which employs a combination of novel low-dimensional
protein encoding strategy and Bayesian optimization enhanced with search space
prescreening via outlier detection. We further design an initial sample
selection strategy to minimize the number of experimental samples for training
ML models. We conduct and report four protein directed evolution experiments
that substantiate the capability of the proposed framework for finding of the
variants with properties of interest. We expect the ODBO framework to greatly
reduce the experimental cost and time cost of directed evolution, and can be
further generalized as a powerful tool for adaptive experimental design in a
broader context.
