  Deep learning has been widely used for protein engineering. However, it is
limited by the lack of sufficient experimental data to train an accurate model
for predicting the functional fitness of high-order mutants. Here, we develop
SESNet, a supervised deep-learning model to predict the fitness for protein
mutants by leveraging both sequence and structure information, and exploiting
attention mechanism. Our model integrates local evolutionary context from
homologous sequences, the global evolutionary context encoding rich semantic
from the universal protein sequence space and the structure information
accounting for the microenvironment around each residue in a protein. We show
that SESNet outperforms state-of-the-art models for predicting the
sequence-function relationship on 26 deep mutational scanning datasets. More
importantly, we propose a data augmentation strategy by leveraging the data
from unsupervised models to pre-train our model. After that, our model can
achieve strikingly high accuracy in prediction of the fitness of protein
mutants, especially for the higher order variants (> 4 mutation sites), when
finetuned by using only a small number of experimental mutation data (<50). The
strategy proposed is of great practical value as the required experimental
effort, i.e., producing a few tens of experimental mutation data on a given
protein, is generally affordable by an ordinary biochemical group and can be
applied on almost any protein.
