Computational Tools and Algorithms for Designing Customized Synthetic Genes

Abstract

Advances in DNA synthesis have enabled the construction of artificial genes, gene circuits, and genomes of bacterial scale. Freedom in de novo design of synthetic constructs provides significant power in studying the impact of mutations in sequence features, and verifying hypotheses on the functional information that is encoded in nucleic and amino acids. To aid this goal, a large number of software tools of variable sophistication have been implemented, enabling the design of synthetic genes for sequence optimization based on rationally defined properties. The first generation of tools dealt predominantly with singular objectives such as codon usage optimization and unique restriction site incorporation. Recent years have seen the emergence of sequence design tools that aim to evolve sequences toward combinations of objectives. The design of optimal protein-coding sequences adhering to multiple objectives is computationally hard, and most tools rely on heuristics to sample the vast sequence design space. In this review, we study some of the algorithmic issues behind gene optimization and the approaches that different tools have adopted to redesign genes and optimize desired coding features. We utilize test cases to demonstrate the efficiency of each approach, as well as identify their strengths and limitations.

Synthetic gene design—The rationale for codon optimization and implications for molecular pharming in plants

“…First, the template gene sequence is retrieved and analyzed to determine the current sequence fitness in each category to be optimized and the user specifies a predefined set of parameter scores to be obtained (the Design of Experiments (DoE)) (Gould et al 2014;Guimaraes et al 2014). The sequence is then synonymously mutated towards improving the fitness of one parameter, while following the user-specified rules (i.e.…”

“…EuGene has two main functions; data gathering and optimization of the gene sequence (Gaspar et al 2012 (Gaspar et al 2012;Gould et al 2014).…”

“…These parameters are similar to other software programs, however, COOL uses a multi-objective optimization algorithm to generate various Pareto-optimal sequences (see Appendix), which can be viewed on a plot (Chin et al 2014;Gould et al 2014). This allows the user to compare best equivalent solutions and the gene sequence that offers the best compromise between design criteria can be chosen (Chin et al 2014).…”

The Need for Integrated Approaches in Metabolic Engineering

“…This notion is an essential motivation for the common use of codon-adjusted transgenes (Gustafsson et al 2004;Gould et al 2014). However, synonymous codon changes pose the potential risk of disrupting important characteristics of a nucleotide sequence, such as mRNA stability and translation rates.…”

High-fidelity PCR enzyme with DNA-binding domain facilitates de novo gene synthesis

“…First, it was shown that purification of oligonucleotides by a high performance liquid chromatography (HPLC) (Andrus and Kuimelis 2001) or polyacrylamide gel electrophoresis (PAGE) (Ellington and Pollard 2001) noticeably reduces the number of mismatches. Also nowadays there are software for the oligonucleotide design: DNAWorks, Gene2Oligo, GeMS and others (Gould et al 2014). But even if well-designed and additionally purified oligonucleotides are used it is not recommended to generate DNA molecules larger than 500 bp by one-step.…”