posted on 2023-08-29, 15:05authored byAlexander
W. Golinski, Zachary D. Schmitz, Gregory H. Nielsen, Bryce Johnson, Diya Saha, Sandhya Appiah, Benjamin J. Hackel, Stefano Martiniani
Engineered
proteins have emerged as novel diagnostics, therapeutics,
and catalysts. Often, poor protein developabilityquantified
by expression, solubility, and stabilityhinders utility. The
ability to predict protein developability from amino acid sequence
would reduce the experimental burden when selecting candidates. Recent
advances in screening technologies enabled a high-throughput (HT)
developability dataset for 105 of 1020 possible
variants of protein ligand scaffold Gp2. In this work, we evaluate
the ability of neural networks to learn a developability representation
from a HT dataset and transfer this knowledge to predict recombinant
expression beyond observed sequences. The model convolves learned
amino acid properties to predict expression levels 44% closer to the
experimental variance compared to a non-embedded control. Analysis
of learned amino acid embeddings highlights the uniqueness of cysteine,
the importance of hydrophobicity and charge, and the unimportance
of aromaticity, when aiming to improve the developability of small
proteins. We identify clusters of similar sequences with increased
recombinant expression through nonlinear dimensionality reduction
and we explore the inferred expression landscape via nested sampling.
The analysis enables the first direct visualization of the fitness
landscape and highlights the existence of evolutionary bottlenecks
in sequence space giving rise to competing subpopulations of sequences
with different developability. The work advances applied protein engineering
efforts by predicting and interpreting protein scaffold expression
from a limited dataset. Furthermore, our statistical mechanical treatment
of the problem advances foundational efforts to characterize the structure
of the protein fitness landscape and the amino acid characteristics
that influence protein developability.