QPSOBT: one codon usage optimization software for protein
QPSOBT is a codon usage optimization software based on the Quantum-behaved Particle
Swarm Optimization (QPSO) algorithm. It can design synthetic genes of multikilobase
sequences for protein heterologous expression rapidly. The program runs on .NET platform.
Compared to the existing codon optimization software and web services, QPSOBT is able to
generate better results when DNA/RNA sequence length is less than 6Kb which is a
commonly-used range, especially when some restriction sites need to be removed. QPSOBT
is freely available (www.sigcib.org/qpsobt.html).
CAI: Codon Adaptation Index
Keywords : Codon usage; Quantum-behaved Particle Swarm; Optimization; protein
It has been suggested by biologists that the synonymous codons are not able to be used
randomly in all organisms, since their usage patterns vary with species and even genes in the
same species so that the balance between natural mutation and selection can be achieved
(Grantham, et al., 1980; Sharp, et al., 1993). As has been demonstrated, natural selection
shapes codon usage in both unicellular and multicellular organisms (Duret and Mouchiroud,
1999). Optimal codons in fast-growing microorganisms, such as Escherichia coli and
Saccharomyces cerevisiae, help to achieve faster translation rates and higher accuracy. In
other organisms that do not show high growing rates ( such as Homo sapines) or that present
small genomes (such as Helicobacter pylori) , codon usage optimization is normally absent,
and thus codon preferences are determined by the characteristic mutational biases seen in that
particular genome (Hershberg and Petrov, 2008).
Genetic engineering has become one of the most powerful tools in modern biochemistry
and biology. Recombinant DNA technology is widely used in both research and industry.
Several hosts, such as Escherichia coli, Pichia pastoris, plants, animals, are used to express
the foreign protein. The desired protein sequence should be reversely translated into a
nucleotide sequence. However, there are enormous possible synthetic sequences that can be
made. Some researchers have proved that codon optimization is vital to establish high
heterologous gene expression. For the purpose of stability maintenance and high expression,
the codon usage of the foreign genes should be optimized (Akashi, 1994; Bulmer, 1991). For
example, Tokuoka investigated expression levels of native and optimized Der f 7 genes in
Aspergillus oryzae. They found codon optimization markedly increased protein and mRNA
production levels (Tokuoka, et al., 2008). Another example involves the study of transgene
expression in Tetrahymena (Ngumbela, et al., 2008). It also emphasizes the importance of
codon optimization which resulted in about ten times more drug resistant transformants than
a cassette containing the non-codon-optimized original neo gene. In addition, the transcript
level of optimized glycoside hydrolase family 45 endoglucanase gene from termite-gut
symbionts in Aspergillus oryzae was 1.8-fold higher than that of native gene (Sasaguri, et al.,
To see this, many optimization methods of codon usage have been proposed (Wu, et al.,
2007). In our previous work, we presented a novel method to optimize codon usage (Cai, et
al., 2008). To facilitate others to employ this method for codon usage optimization, we
programmed a stand-alone software based on the proposed method. The software is based on
the quantum-behaved particle swarm optimization (QPSO) algorithm called QPSOBT. It is
shown that by using QPSOBT, one can find the optimal gene sequence according to the
hosts’ codon usage frequency efficiently.
2. Materials and methods
2.1 The codon usage optimization algorithm
The task of codon usage optimization can be reduced to minimization of the following
objective function of the relative squared error E defined as
where E is the relative squared error between Fri, the frequency of codon i in the reference or
sequence (per 1000), and Fti, the frequency of codon i in the synthetic sequence. The goal of
minimization of E is to find the codon usage frequency in the synthetic sequence as similar as
possible to that in the reference host.
Figure ,1, visualizes the workflow of QPSOBT. It begins with importing a protein or gene
sequence, followed by selection of a target system. The target system can be selected from a
pre-defined database and, alternatively, can be self-defined by users. After that, QPSOBT
runs the QPSO algorithm to search the optimization sequence. The user can set up the
parameters of the algorithm and restrict enzyme sites before running the program; otherwise
QPSOBT runs the algorithm with default setups. The optimized sequence and statistical
results are outputted to the result files when the algorithm terminates.
Fig 1 Workflow of QPSOBT
The detailed procedure of the optimization algorithm was described in our previous work
(Cai, et al., 2008).
Microsoft Visual C# was used to develop QPSOBT on the .NET platform. .NET is an
environment not only for the Windows platform, but other operating systems. The .NET
Framework can be downloaded from Microsoft’s website freely.
Figure 2 visualizes the main interface of QPSOBT. The input sequence may be DNA/RNA
or protein sequence, while the output sequence may be DNA or RNA. Several codon usage
tables from the Codon Usage Database (Nakamura, et al., 2000) were pre-set in the
Microsoft Access database. The users can choose the codon usage (i.e. target organism) table
in the list box. There are two methods for self-defining codon usage data: editing the data
directly or calculating through a DNA/RNA sequence (as shown in Fig 3). The high express
genes can be downloaded from a synthetic gene database (Wu, et al., 2007) and can be used
as a reference sequence. The users can also select the avoiding restriction enzymes in the
setup menu item (Fig 4). The optimized sequence will be shown in the text box with a DNA
or RNA format. After the running of the optimization algorithm, the codons frequency and
GC content of the optimized sequence are displayed in the main interface for comparison
with the reference host or sequence. Two output files are used to record statistical results and
the process of optimization. QPSOBT is freely available (www.sigcib.org/qpsobt.html).
Fig 2 Main interface of QPSOBT
Fig 3 Interface of computing codon usage from the self-defined sequence
Fig 4 Interface of selecting avoided restriction enzymes
Among the approaches of codon usage optimization in existing stand-alone softwares
and web servers, the two most frequently referred are the methods based on the “CAI=1”
theory and on codon usage probabilities. By the former method, the protein sequence is
back-translated using the “one amino acid-one codon” approach according to the “CAI=1”
theory (Pesole, et al., 1988). During back-translation, the same amino acids are only encoded
by the most commonly-used synonymous codon in the reference set. There are two main
shortcomings in this method. One is that the method can only optimize highly-expressed
genes due to its measurement based on codon usage bias. The “CAI=1” method cannot
consider tRNAs, ribosomal RNAs, and other non-coding RNAs. The other shortcoming is
that the methods may result in low efficiency for organisms with low translation bias
(Willenbrock, et al., 2006).
The codon-usage-probability-based approach is currently widely used in applications
such as DNAWorks (Hoover and Lubkowski, 2002), GeMs (Jayaraj, et al., 2005), SGD (Wu,
et al., 2006), Gene Designer (Villalobos, et al., 2006), GeneDesign (Richardson, et al., 2006),
and OPTIMIZER (Puigbo, et al., 2007). When the process of optimization in the application
is executed, a random number distributed uniformly within [0, 100] is generated, and a
specific triplet is picked out using a roulette selection (a Monte Carlo method (Matousek,
2009)) according to the random number and the probability distribution. The process is
executed across all the codons in the sequence to fulfill optimization of the codon usage,
followed by removing restriction sites from the resulting sequence to get a modified one.
According to the large number law and central limitation law, it can be inferred that with
more samples, the higher precision it will acquire. Our previous work had proved it could
only generate a good result when protein lengths were more than 2000 (Cai, et al., 2008).
The QPSO algorithm employed in QPSOBT was proposed in our previous work (Jun, et
al., 2004; Jun, et al., 2004; Jun, et al., 2005). The algorithm is a global convergent and has a
stronger global search ability than its predecessor - the Particle Swarm Optimization (PSO)
algorithm. Our previous experiment results show that it can efficiently find out the optimal
gene sequence according to hosts’ codon usage frequency. QPSOBT provides the most basic
functions for optimization process which makes it easily understood and used. Users can use
other more professional bioinformatics tool to analyze and operate the optimized sequences.
This work was supported by the innovation team-building project of Jiangnan University,
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