The National Institute of Biomedical Imaging and Bioengineering awarded Massachusetts Institute of Technology $100,228 on January 30, 2027, under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286) to develop a multi-omic machine-learning framework for stress-aware therapeutic protein biomanufacturing.
The project will generate high-resolution multi-omics datasets including RNA-seq, quantitative proteomics, tRNA abundance, and tRNA modification profiles across bioreactor-relevant stresses in Komagataella phaffii, a major industrial protein production host. MIT will integrate these measurements into structure-aware machine-learning models that incorporate genomic codon distributions, mRNA structure, and position-specific features within encoded protein three-dimensional architecture. The team will experimentally test model predictions by synthesizing and expressing codon-designed variants of FGF2, a commercially and biologically important growth factor, using MIT's automated high-throughput screening capabilities. Iterative model refinement through active learning will yield a generalizable platform for rational, physiology-aware codon design to optimize translation efficiency under specific growth and stress conditions.
Performance occurs in Cambridge, Massachusetts, with an ultimate completion date of August 31, 2028.