Open AccessCodon Optimization Using a Recurrent Neural Network
Author(s) -
Dennis R. Goulet,
Yunhui Yan,
Palak Agrawal,
Andrew B. Waight,
Amanda Nga-Sze Mak,
Yan Zhu
Publication year - 2023
Publication title -
journal of computational biology
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 0.585
H-Index - 95
eISSN - 1557-8666
pISSN - 1066-5277
DOI - 10.1089/cmb.2021.0458
Subject(s) - chinese hamster ovary cell , recurrent neural network , dna , computational biology , sequence (biology) , dna sequencing , codon usage bias , biology , recombinant dna , computer science , artificial neural network , artificial intelligence , genetics , genome , gene , receptor
Codon optimization of a DNA sequence can significantly increase efficiency of protein expression, reducing the cost to manufacture biologic pharmaceuticals. Although directed methods based on such factors as codon usage bias and GC nucleotide content are often used to optimize protein expression, undirected optimization using machine learning could further improve the process by capitalizing on undiscovered patterns that exist within real DNA sequences. To explore this hypothesis, Chinese hamster DNA sequences were used to train a recurrent neural network (RNN) model of codon optimization. The model was used to generate optimized DNA sequence based on an input amino acid sequence for the example receptor programmed death-ligand 1 and for an example monoclonal antibody. When RNN-optimized sequences were transfected transiently or stably into Chinese hamster ovary cells, the resulting protein expression was as high or higher than that produced by DNA sequences optimized by conventional algorithms.