144620 20260217205532.0 doi 10.1016/j.csbj.2024.07.020 sideral 139394 ART-2024-139394 eng Luna-Cerralbo, David Universidad de Zaragoza A statistical-physics approach for codon usage optimisation 2024 Access copy available to the general public Unrestricted The concept of “codon optimisation” involves adjusting the coding sequence of a target protein to account for the inherent codon preferences of a host species and maximise protein expression in that species. However, there is still a lack of consensus on the most effective approach to achieve optimal results. Existing methods typically depend on heuristic combinations of different variables, leaving the user with the final choice of the sequence hit. In this study, we propose a new statistical-physics model for codon optimisation. This model, called the Nearest-Neighbour interaction (NN) model, links the probability of any given codon sequence to the “interactions” between neighbouring codons. We used the model to design codon sequences for different proteins of interest, and we compared our sequences with the predictions of some commercial tools. In order to assess the importance of the pair interactions, we additionally compared the NN model with a simpler method (Ind) that disregards interactions. It was observed that the NN method yielded similar Codon Adaptation Index (CAI) values to those obtained by other commercial algorithms, despite the fact that CAI was not explicitly considered in the algorithm. By utilising both the NN and Ind methods to optimise the reporter protein luciferase, and then analysing the translation performance in human cell lines and in a mouse model, we found that the NN approach yielded the highest protein expression in vivo. Consequently, we propose that the NN model may prove advantageous in biotechnological applications, such as heterologous protein expression or mRNA-based therapies. info:eu-repo/grantAgreement/ES/DGA/E30-20R info:eu-repo/grantAgreement/ES/DGA/IDMF 2021-0009 info:eu-repo/grantAgreement/ES/MICINN/PID2020-113582GB-I00 info:eu-repo/semantics/openAccess by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es 4.1 2024 BIOCHEMISTRY & MOLECULAR BIOLOGY 104 / 320 = 0.325 2024 Q2 T1 1.561 2024 Biochemistry 2024 Q1 Biophysics 2024 Q1 Structural Biology 2024 Q1 Computer Science Applications 2024 Q1 Genetics 2024 Q1 Biotechnology 2024 Q1 9.8 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Blasco-Machín, Irene Adame-Pérez, Susana Lampaya, Verónica Larraga, Ana Alejo, Teresa Martínez-Oliván, Juan Broset, Esther Bruscolini, Pierpaolo Universidad de Zaragoza (orcid)0000-0002-5833-8798 2004 405 Universidad de Zaragoza Dpto. Física Teórica Área Física Teórica 23 (2024), 3050-3064 Comput. struct. biotechnol. j. Computational and Structural Biotechnology Journal 2001-0370 1577146 http://zaguan.unizar.es/record/144620/files/texto_completo.pdf Versión publicada 2791657 http://zaguan.unizar.es/record/144620/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:144620 articulos driver 2026-02-17-20:32:56 ARTICLE