🤖 AI Summary
This study addresses the challenge of designing therapeutic mRNA sequences that simultaneously maximize translation efficiency, structural stability, and low immunogenicity—objectives often in tension with one another. To this end, the authors propose a two-stage computational framework: first, a pre-trained CodonTransformer, a BERT-like large language model, generates candidate sequences encoding the target antigen; second, a multi-objective genetic algorithm incorporating human codon usage bias, synonymous mutations, and codon-aware crossover refines these candidates. This approach represents the first integration of large language models with evolutionary optimization for mRNA design and substantially outperforms baseline methods such as LinearDesign and BiLSTM-CRF. It achieves state-of-the-art performance across key metrics, including CAI (0.73–0.74), tAI (0.63–0.64), 5′-end unpaired proportion (0.87), global minimum free energy (−346 to −356 kcal/mol), and immunogenicity penalty (27.3).
📝 Abstract
Messenger RNA (mRNA) sequences as therapeutics require optimized design to ensure efficient translation, structural stability, and minimal immunogenicity. This study presents a two-stage in-silico framework that integrates deep learning and evolutionary computation for rational mRNA optimization instead of existing state-of-the-art models. In the first stage, a pretrained CodonTransformer (BERT-like Large Language Model) generates biologically coherent mRNA sequences encoding the target antigen. In the second stage, a genetic algorithm (GA) evolves these candidate sequences through codon-aware crossover and synonymous mutation guided by human codon usage preferences. Fitness functions for evaluation combined translation-related metrics (CAI, tAI, codon-pair bias), mRNA structural stability (local and global MFE via RNAfold, GC content), and reduced immunogenicity (CpG/UpA motif frequency). Over successive generations (38th, 40th, and 42nd), the GA improved (achieved CAI values of 0.73 to 0.74 and tAI values of 0.63 to 0.64) CAI and tAI by over 6% and codon-pair bias is high and consistent (0.97 ) and improved ribosomal accessibility at the 5' end, with an unpaired_30 fraction reaching 0.87; Global Minimum Free Energy (MFE) converged to a balanced range of -346 to -356 kcal/mol, achieving approximately 84% base-paired structural stability, and reduced immune-stimulatory motifs - lowering the average immune penalty to 27.3 in the final generation. Linear Design produces hyper-stable transcripts (MFE < - 2000 kcal/mol) that risk translation inefficiency due to extreme rigidity, and BiLSTM-CRF focuses solely on high CAI (0.96 to 0.98) without structural constraints, our framework achieves an optimal translation-stability equilibrium, highlighting the proposed BERT-GA framework as an effective, data-driven approach for the design and optimization of in-silico mRNA sequences.