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Luna-Cerralbo D, Blasco-Machín I, Adame-Pérez S, Lampaya V, Larraga A, Alejo T, Martínez-Oliván J, Broset E, Bruscolini P. A statistical-physics approach for codon usage optimisation. Comput Struct Biotechnol J 2024; 23:3050-3064. [PMID: 39188969 PMCID: PMC11345917 DOI: 10.1016/j.csbj.2024.07.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/25/2024] [Accepted: 07/25/2024] [Indexed: 08/28/2024] Open
Abstract
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.
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Affiliation(s)
- David Luna-Cerralbo
- Department of Theoretical Physics, Faculty of Science, University of Zaragoza, c/ Pedro Cerbuna s/n, Zaragoza, 50009, Spain
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, c/ Mariano Esquillor s/n, Zaragoza, 50018, Spain
| | - Irene Blasco-Machín
- Certest Pharma, Certest Biotec S.L, Polígono Industrial Río Gallego II, Calle J, 1, San Mateo de Gállego, 50840, Spain
| | - Susana Adame-Pérez
- Certest Pharma, Certest Biotec S.L, Polígono Industrial Río Gallego II, Calle J, 1, San Mateo de Gállego, 50840, Spain
| | - Verónica Lampaya
- Certest Pharma, Certest Biotec S.L, Polígono Industrial Río Gallego II, Calle J, 1, San Mateo de Gállego, 50840, Spain
| | - Ana Larraga
- Certest Pharma, Certest Biotec S.L, Polígono Industrial Río Gallego II, Calle J, 1, San Mateo de Gállego, 50840, Spain
| | - Teresa Alejo
- Certest Pharma, Certest Biotec S.L, Polígono Industrial Río Gallego II, Calle J, 1, San Mateo de Gállego, 50840, Spain
| | - Juan Martínez-Oliván
- Certest Pharma, Certest Biotec S.L, Polígono Industrial Río Gallego II, Calle J, 1, San Mateo de Gállego, 50840, Spain
| | - Esther Broset
- Certest Pharma, Certest Biotec S.L, Polígono Industrial Río Gallego II, Calle J, 1, San Mateo de Gállego, 50840, Spain
| | - Pierpaolo Bruscolini
- Department of Theoretical Physics, Faculty of Science, University of Zaragoza, c/ Pedro Cerbuna s/n, Zaragoza, 50009, Spain
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, c/ Mariano Esquillor s/n, Zaragoza, 50018, Spain
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Paremskaia AI, Kogan AA, Murashkina A, Naumova DA, Satish A, Abramov IS, Feoktistova SG, Mityaeva ON, Deviatkin AA, Volchkov PY. Codon-optimization in gene therapy: promises, prospects and challenges. Front Bioeng Biotechnol 2024; 12:1371596. [PMID: 38605988 PMCID: PMC11007035 DOI: 10.3389/fbioe.2024.1371596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 03/19/2024] [Indexed: 04/13/2024] Open
Abstract
Codon optimization has evolved to enhance protein expression efficiency by exploiting the genetic code's redundancy, allowing for multiple codon options for a single amino acid. Initially observed in E. coli, optimal codon usage correlates with high gene expression, which has propelled applications expanding from basic research to biopharmaceuticals and vaccine development. The method is especially valuable for adjusting immune responses in gene therapies and has the potenial to create tissue-specific therapies. However, challenges persist, such as the risk of unintended effects on protein function and the complexity of evaluating optimization effectiveness. Despite these issues, codon optimization is crucial in advancing gene therapeutics. This study provides a comprehensive review of the current metrics for codon-optimization, and its practical usage in research and clinical applications, in the context of gene therapy.
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Affiliation(s)
- Anastasiia Iu Paremskaia
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, Moscow, Russia
| | - Anna A. Kogan
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, Moscow, Russia
| | - Anastasiia Murashkina
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, Moscow, Russia
| | - Daria A. Naumova
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, Moscow, Russia
| | - Anakha Satish
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, Moscow, Russia
| | - Ivan S. Abramov
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, Moscow, Russia
- The MCSC named after A. S. Loginov, Moscow, Russia
| | - Sofya G. Feoktistova
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, Moscow, Russia
| | - Olga N. Mityaeva
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, Moscow, Russia
| | - Andrei A. Deviatkin
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, Moscow, Russia
| | - Pavel Yu Volchkov
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, Moscow, Russia
- The MCSC named after A. S. Loginov, Moscow, Russia
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Gonzalez-Sanchez B, Vega-Rodríguez MA, Santander-Jiménez S. A multi-objective butterfly optimization algorithm for protein encoding. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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Motion Pattern Optimization and Energy Analysis for Underwater Glider Based on the Multi-Objective Artificial Bee Colony Method. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2021. [DOI: 10.3390/jmse9030327] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Underwater gliders are prevailing in oceanic observation nowadays for their flexible deployment and low cost. However, the limited onboard energy constrains their application, hence the motion pattern optimization and energy analysis are the key to maximizing the range of the glider while maintaining the acceptable navigation preciseness of the glider. In this work, a Multi-Objective Artificial Bee Colony (MOABC) algorithm is used to solve the constrained hybrid non-convex multi-objective optimization problem about range and accuracy of gliders in combination with specific glider dynamics models. The motion parameters Pareto front that balances the navigational index referring to range and preciseness are obtained, relevant gliding profile motion results are simulated simultaneously, and the results are compared with the conventional gliding patterns to examine the quality of the solution. Comparison shows that, with the utilization of the algorithm, glider voyage performance with respect to endurance and preciseness can be effectively improved.
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Şen A, Kargar K, Akgün E, Pınar MÇ. Codon optimization: a mathematical programing approach. Bioinformatics 2020; 36:4012-4020. [DOI: 10.1093/bioinformatics/btaa248] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 11/25/2019] [Accepted: 04/13/2020] [Indexed: 12/11/2022] Open
Abstract
AbstractMotivationSynthesizing proteins in heterologous hosts is an important tool in biotechnology. However, the genetic code is degenerate and the codon usage is biased in many organisms. Synonymous codon changes that are customized for each host organism may have a significant effect on the level of protein expression. This effect can be measured by using metrics, such as codon adaptation index, codon pair bias, relative codon bias and relative codon pair bias. Codon optimization is designing codons that improve one or more of these objectives. Currently available algorithms and software solutions either rely on heuristics without providing optimality guarantees or are very rigid in modeling different objective functions and restrictions.ResultsWe develop an effective mixed integer linear programing (MILP) formulation, which considers multiple objectives. Our numerical study shows that this formulation can be effectively used to generate (Pareto) optimal codon designs even for very long amino acid sequences using a standard commercial solver. We also show that one can obtain designs in the efficient frontier in reasonable solution times and incorporate other complex objectives, such as mRNA secondary structures in codon design using MILP formulations.Availability and implementationhttp://alpersen.bilkent.edu.tr/codonoptimization/CodonOptimization.zip.
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Affiliation(s)
- Alper Şen
- Department of Industrial Engineering, Bilkent University, Ankara 06800, Turkey
| | - Kamyar Kargar
- Department of Industrial Engineering, Bilkent University, Ankara 06800, Turkey
| | - Esma Akgün
- Department of Management Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Mustafa Ç Pınar
- Department of Industrial Engineering, Bilkent University, Ankara 06800, Turkey
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