Synonymous Gene Recoding Affects the Accuracy of Protein Quantification Measurements
Accurate measurement of therapeutic protein concentration is critical for ensuring manufacturing consistency. Synonymous gene recoding is often used to improve protein yield, including for coagulation Factor IX (FIX), used to treat Hemophilia B. Although synonymous recoding does not alter primary amino acid sequence, it can affect protein conformation, potentially influencing the accuracy of protein quantification depending on the method used. In this study, we investigated whether synonymous changes in F9 gene, which encodes FIX, impacted reliability of commonly used protein quantitation methods. HEK293- FlpIn cells were used to generate FIX proteins expressed from seven different synonymous F9 sequences. Purified proteins were initially measured using absorbance at 280 nm, followed by quantification using various colorimetric, and fluorometric protein assays. Relative differences among protein quantification methods were inconsistent between wild-type and synonymous F9 variants. Compared with A280 measurements, mean differences across variants ranged from − 33% to − 8% for Bradford and from 14 to 54% for BCA, whereas wider ranges were observed for OPA (− 23% to 133%), CBQCA (14% to 282%), and Lowry (1% to 185%). Bradford and BCA also exhibited lower intra-assay variability, with standard deviations of 6% and 10%, respectively, compared with 12%, 18%, and 33% for OPA, CBQCA, and Lowry assays. These findings indicate that synonymous codon substitutions influence protein quantification outcomes, with Bradford and BCA producing more consistent results and lowest variability, whereas amine-reactive fluorescence-based assays (OPA and CBQCA) showed greater inconsistencies. In conclusion , protein quantification methods may not produce consistent accuracy among proteins translated from synonymously recoded sequences.
Authors
- Wells W. Wu (ORCID: https://orcid.org/0000-0002-6392-2235)
- Anton A. Komar (ORCID: https://orcid.org/0000-0003-4188-0633)
- Upendra Katneni (ORCID: https://orcid.org/0000-0002-8145-8894)
- Chava Kimchi‐Sarfaty (ORCID: https://orcid.org/0000-0002-9355-8585)
- Nayiri M. Kaissarian (ORCID: https://orcid.org/0000-0003-2574-4498)
- Nathan Clement
- Nigam Padhiar
Institutions
- Cleveland State University (US)
- Center for Biologics Evaluation and Research (US)
Publication Details
- Journal
- The AAPS Journal
- Published
- 2026-09-21
- DOI
- https://doi.org/10.1208/s12248-026-01310-2
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00