Prediction of strain engineerings that amplify recombinant protein secretion through the machine learning approach MaLPHAS

Abstract

This article presents a discussion of the process of precision fermentation (PF), describing the history of the space, the expected 70% growth over the next 5 years, various applications of precision fermented products, and the markets available to be disrupted by the technology. A range of prokaryotic and eukaryotic host organisms used for PF are described, with the advantages, disadvantages and applications of each. The process of setting up PF and strain engineering is described, as well as various ways that computational analysis and design techniques can be employed to assist PF engineering. The article then describes the design and implementation of a machine learning method, machine learning predictions having amplified secretion (MaLPHAS) to predict strain engineerings, which optimise the secretion of a recombinant protein. This approach showed an in silico cross-validated R ² accuracy on the training data of up to 46.6% and in an in vitro test on a Komagataella phaffii strain, identified one gene engineering out of five predicted, which was shown to double the secretion of a heterologous protein and outperform three of the best-known edits from the literature for improving secretion in K. phaffii.

FIGURE 1

Precision fermentation for recombinant protein production using the Design‐Build‐Test‐Learn (DBTL) cycle

FIGURE 2

An image of the predicted 3D ribbon structure of the fused recombinant protein, generated with the SWISS‐MODEL protein structure modelling server [86]

FIGURE 3

Box and whisker plots showing HiBiT signals of the recombinant protein secreted into the supernatant by engineered strains, normalised to the mean of the parental strain. A signal of 1 corresponds to a protein concentration of 206 mg/L. Measurements were made on 12 colonies of each strain, from left to right: control (the parental strain), three knockouts from the literature known to increase secretion (Δpep4, Δprb1, and Δbgs7) and five knockouts suggested by MaLPHAS (Δcog6, Δypt7, Δgas1, Δglr1, and Δvam6)