000144620 001__ 144620 000144620 005__ 20240829125213.0 000144620 0247_ $$2doi$$a10.1016/j.csbj.2024.07.020 000144620 0248_ $$2sideral$$a139394 000144620 037__ $$aART-2024-139394 000144620 041__ $$aeng 000144620 100__ $$aLuna-Cerralbo, David$$uUniversidad de Zaragoza 000144620 245__ $$aA statistical-physics approach for codon usage optimisation 000144620 260__ $$c2024 000144620 5060_ $$aAccess copy available to the general public$$fUnrestricted 000144620 5203_ $$aThe concept of “codon optimisation” involves adjusting the coding sequence of a target protein to account for the inherent codon preferences of a host species and maximise protein expression in that species. However, there is still a lack of consensus on the most effective approach to achieve optimal results. Existing methods typically depend on heuristic combinations of different variables, leaving the user with the final choice of the sequence hit. In this study, we propose a new statistical-physics model for codon optimisation. This model, called the Nearest-Neighbour interaction (NN) model, links the probability of any given codon sequence to the “interactions” between neighbouring codons. We used the model to design codon sequences for different proteins of interest, and we compared our sequences with the predictions of some commercial tools. In order to assess the importance of the pair interactions, we additionally compared the NN model with a simpler method (Ind) that disregards interactions. It was observed that the NN method yielded similar Codon Adaptation Index (CAI) values to those obtained by other commercial algorithms, despite the fact that CAI was not explicitly considered in the algorithm. By utilising both the NN and Ind methods to optimise the reporter protein luciferase, and then analysing the translation performance in human cell lines and in a mouse model, we found that the NN approach yielded the highest protein expression in vivo. Consequently, we propose that the NN model may prove advantageous in biotechnological applications, such as heterologous protein expression or mRNA-based therapies. 000144620 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E30-20R$$9info:eu-repo/grantAgreement/ES/DGA/IDMF 2021-0009$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-113582GB-I00 000144620 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000144620 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000144620 700__ $$aBlasco-Machín, Irene 000144620 700__ $$aAdame-Pérez, Susana 000144620 700__ $$aLampaya, Verónica 000144620 700__ $$aLarraga, Ana 000144620 700__ $$aAlejo, Teresa 000144620 700__ $$aMartínez-Oliván, Juan 000144620 700__ $$aBroset, Esther 000144620 700__ $$0(orcid)0000-0002-5833-8798$$aBruscolini, Pierpaolo$$uUniversidad de Zaragoza 000144620 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica 000144620 773__ $$g23 (2024), 3050-3064$$pComput. struct. biotechnol. j.$$tComputational and Structural Biotechnology Journal$$x2001-0370 000144620 8564_ $$s1577146$$uhttps://zaguan.unizar.es/record/144620/files/texto_completo.pdf$$yVersión publicada 000144620 8564_ $$s2791657$$uhttps://zaguan.unizar.es/record/144620/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000144620 909CO $$ooai:zaguan.unizar.es:144620$$particulos$$pdriver 000144620 951__ $$a2024-08-29-10:45:55 000144620 980__ $$aARTICLE