mRNA codon optimization with quantum computers

Dillion M. Fox; Kim M. Branson; Ross C. Walker

doi:10.1371/journal.pone.0259101

Abstract

Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP-hard combinatorial optimization problem. Each amino acid in the protein sequence can be represented by as many as six codons, and the process of selecting the combination that maximizes probability of expression is termed codon optimization. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. A Quantum Annealer (QA) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The QA is found to be competitive in identifying optimal solutions. The utility of gate-based systems is also evaluated using a simulator resulting in the finding that while current generations of devices lack the hardware requirements, in terms of both qubit count and connectivity, to solve realistic problems, future generation devices may be highly efficient.

Citation: Fox DM, Branson KM, Walker RC (2021) mRNA codon optimization with quantum computers. PLoS ONE 16(10): e0259101. https://doi.org/10.1371/journal.pone.0259101

Editor: Nicholas Chancellor, Durham University, UNITED KINGDOM

Received: March 23, 2021; Accepted: October 12, 2021; Published: October 29, 2021

Copyright: © 2021 Fox et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Protein sequences can be encoded by an enormous multitude of possible nucleotide sequences. The degenerate mapping between amino acids and synonymous codons entails an exponential relationship between the number of potential nucleotide sequences and the length of the polypeptide chain. However, different nucleotide sequences encoding the same protein may exhibit dramatically different outcomes in expression systems [1–4]. Furthermore, recent studies have shown that codon selection can impact downstream processes such as protein folding and function [1–3], which is particularly important for use-cases such as recombinant protein therapies [5].

Codon optimization is a procedure designed to increase gene expression based on a heuristic scoring function [6] with many scoring functions having been proposed [7–10]. Some of the more common scoring functions seek to optimize the fraction of G and C bases [11–15], match the codon usage bias of the host expression system [16–21], and/or attempt to disrupt the formation of mRNA secondary structure [7,17,22]. The vast solution space is most commonly sampled using genetic algorithms (GA) that seek to evolve solutions by introducing synonymous codon mutations and propagating favorable substitutions through generations [12,16,20,23–25]. However, other methods have been proposed [18,26,27]. While classical approaches such as GAs can be highly performant, the fraction of solution space that is sampled in a fixed number of iterations decreases exponentially as the polypeptide chain length grows. Thorough sampling of the solutions space is therefore often intractable with biologically relevant use-cases. In this study we investigate the viability of programming quantum computers to efficiently identify high-quality solutions scored with arbitrary objective functions.

Recent advances in quantum information science and technology have elucidated the potential for quantum devices to outperform classical devices in a narrow range of applications [28,29]. Among these, certain types of combinatorial optimization problems are among the most promising for near-term advantage [30]. The field of quantum computing is developing rapidly in terms of both hardware and algorithms. Each type of quantum computing technology offers a unique set of strengths and weaknesses, and applications are often tailored to compliment the strengths of each device. While there are many physical realizations of quantum technologies, we focus on two markedly different models; quantum annealing (QA) and gate-based quantum computers.

Quantum annealers are most commonly used to solve high-dimensional combinatorial optimization problems [31–33]. For example, a recent study showed that protein design, which requires combinatorial optimization of rotomeric states, can be accelerated with a QA [34]. Current implementations of quantum annealers offer nearly two orders of magnitude more qubits than state of the art gate-based devices, offering the potential to address realistic sized problems. The general class of QA algorithms are classified as metaheuristic methods for solving local optimization problems in multivariate spaces [31]. These approaches are similar to simulated annealing but exploit the phenomena of quantum tunneling, instead of thermal activation, to hop out of local energetic minima [35].

An alternative quantum computing technology, and the technology that was recently used to demonstrate quantum supremacy [28,29], centers on gate-based instructions. Current generations of non-error corrected hardware, termed Noisy Intermediate Scale Quantum (NISQ) [36], can be programmed to solve combinatorial optimization problems using variational methods such as the Quantum Approximate Optimization Algorithm (QAOA) [37]. While gate-based quantum computers to date contain significantly fewer qubits compared to quantum annealers, the qubits in gate-based devices tend to be of higher quality. As the technology matures however there is an expectation that qubit count and connections between qubits will improve substantially. There is also the expectation that error rates will decrease, and general-purpose error correction may become possible. Developing and testing suitable algorithms ahead of the technology development curve is thus a worthwhile endeavor and it is therefore common practice to utilize simulators, running on classical computing hardware, to permit evaluation of quantum algorithmic functions in the absence of real-world hardware to evaluate performance on.

Designing novel algorithms to execute on quantum devices requires deep expert knowledge of the devices, quantum information science, and quantum software stacks. However, there are a few classes of ubiquitous problems that are readily solvable using tools built into many widely available software packages. The Binary Quadratic Model (BQM) is perhaps the most archetypal example and can be found at the core of many familiar problems such as the Ising model [38,39]. The energy of a BQM can be described by a Hamiltonian with the general form (1) where q_i, q_j, and q_k represent the values of the qubits, which can either be {0, 1} or {-1, 1} for binary or spin representations, respectively, h_i are the one-body terms, and J_jk are the two-body interactions. For the Ising model, h represents the physical spins and J represents the energy of the interactions between the spins.

The methods section below shows that the codon optimization problem can be mathematically formulated as a BQM and thus implemented on a variety of competing quantum computing platforms. This representation requires translating the scoring function used in traditional approaches into a quadratic Hamiltonian where the eigenstates represent nucleotide sequences and the eigenenergies represent the scores. Implementing this program on the D-Wave Advantage 1.1 QA shows that it identifies high quality solutions that are competitive with a basic implementation of a genetic algorithm programmed with an equivalent scoring function. Implementing a version of this program for IBM Q devices, while successful, shows that modelling practical systems requires too many qubits to be run on even the most advanced gate-based devices available (e.g. IBM’s 65-qubit Hummingbird device) [40]. However, executing the model on an IBM noisy simulator [41] demonstrates the potential of the algorithm. Finally, we comment on the potential usability of the BQM approach on current and future generations of quantum computers.

Results

Codon optimization was implemented as a BQM on quantum devices with a Hamiltonian designed to optimize GC-content (the fraction of G and C bases), minimize sequentially repeated nucleotides, and optimize codon-usage bias (see Eq (15) in Methods for details). Quantum devices use qubits to store data, which decode digitally to 0’s and 1’s upon measurement, but which also may be in a superposition of 0 and 1 during the calculation. A one-hot encoding scheme is used to encode classical genetic data into a quantum device; every possible codon that can map to the target polypeptide sequence is required to be explicitly represented by a physical qubit. The qubits which return “1” upon measurement represent the codons selected at each position in the polypeptide sequence. Therefore, only 1 qubit (codon) for each position in the polypeptide sequence can be in a “1” state, and the rest must return “0” upon measurement (Fig 1a). This scheme is enforced by constructing a 2-body penalty matrix which adds infinite energy to pairs of codons that map to the same position in the polypeptide sequence (Fig 1b). The final sequence is determined by recording the values of the qubits and concatenating the corresponding codons of the qubits in the “1” state (Fig 1c).

Download:

Fig 1.

(a) Example mapping of each possible codon for the amino acid Glycine (gray oval labeled “G”) to qubits. Gray boxes represent qubits labeled q₀…q₃, and the codon assigned to each qubit is shown next to the qubit label. (b) A penalty matrix is constructed to add infinite energy to cases where more than one codon is in the “1” state. In this example, qubit q₂ is in the “1” state and the rest are in the “0” state, which returns an energetic penalty equal to 0. (c) Example mapping of codons for protein sequence GSK… to qubits. One codon is selected for each position in the sequence, highlighted in orange.

https://doi.org/10.1371/journal.pone.0259101.g001

QA accuracy and quality of scores

The goal of the optimization is to find the combination of codons that minimizes the Hamiltonian (or the objective function for the GA). In theory, QA should be able to find the ground state of the input Hamiltonian. However, due to thermal fluctuations and limited quantum processing unit (QPU) time, low energy solutions that are near, but not equal to the ground state are expected. Furthermore, as the size of the problem increases, the probability of annealing to an optimal eigenvalue decreases (see the “BQM challenges and limitations” section in the Supplementary Information for further discussion of technical challenges specific to heavily constrained problems such as codon optimization).

While it is not possible to calculate the true ground state for large problems, comparisons can be made to other approximate methods, e.g., GA approaches, to contextualize the results and performance of the QA approach. Additionally, D-Wave offers hybrid solvers that augment the QA with classical methods to optimize the results. Direct programming of the QA yielded excessively noisy results with high variance in the estimation of the ground state. The hybrid solver was thus selected to more reliably optimize the input Hamiltonian.

Peptides of length 20.

The baseline performance of the QA implementation of codon optimization was evaluated using 63 peptide fragments of length 20 derived from the human severe acute respiratory syndrome coronavirus 2 (2019-nCoV, SARS-CoV-2) spike glycoprotein sequence (UniProtKB–P0DTC2) and compared with a conventional single-threaded GA implementation. See the Genetic Algorithm Validation section in the Supplementary Information for performance metrics. The results of running the quantum annealer and the GA 20 times each are reported in Fig 2. There is an approximately linear relationship between the optimization scores, with an average ratio close to 1:1. The minimum eigenvalue identified by QA matched the minimum score obtained by the GA to machine precision in 77% of the peptide fragments that were considered. In the remaining cases, the GA identified a lower score than QA. The quantum annealer was programmed to run for a total of 6.0 s including preprocessing and communication between the classical and quantum components; it is unknown exactly how much of this time was spent on the QPU. The average execution time for the GA was 1.09 s with a standard deviation of 0.06 s.

Download:

Fig 2. Eigenvalues measured by QA vs GA scores.

Lower scores indicate higher probability of expression. Dashed line represents y = x. Error bars represent the standard deviation of 20 trials and are shown in blue.

https://doi.org/10.1371/journal.pone.0259101.g002

Full-length proteins.

The Leap Hybrid solver is capable of solving codon optimization problems expressed as a BQM with up to ~1,000 amino acids. A selection of full-length sequences (see Test Applications in Methods) was run on both the quantum annealer and the GA (Fig 3). The hybrid solver was given 50 s to perform the optimization task for each sequence, however, it is unknown exactly how much of this time was spent on the QPU. The GA was run for 6000 generations, determined through testing to be the point at which the systems asymptotically converged to a solution (see Genetic Algorithm Validation in the Supplementary Information). The average execution time for the GA was 10.6 minutes with a standard deviation of 0.9 minutes.

Download:

Fig 3. QA vs GA for 10 full-length proteins.

Dashed line represents y = x. Lower scores indicate higher probability of expression. Point labels indicate number of amino acids.

https://doi.org/10.1371/journal.pone.0259101.g003

Relationship between scoring function and scalability on quantum hardware.

The choice of scoring function can have a dramatic impact on the size of the model. For example, the Hamiltonian representing GC-content requires explicit calculation of all off-diagonal elements of the two-body interaction matrix. In other words, all logical qubits must be fully connected. The D-Wave Advantage system is state-of-the-art in terms of number of qubits and connectivity, described by a P₁₆ Pegasus graph [42], and the best minor embedding scheme for a fully connected graph of logical qubits was heuristically found to support a maximum of 180 logical qubits when interfacing directly with the QPU. However, there are two-body Hamiltonians that do not require couplers between all logical qubits. The Hamiltonian penalizing sequentially repeated nucleotides only requires couplers between qubits mapping to neighboring sequence positions. In this case, the task of minor embedding is straightforward and would only require a few additional physical qubits to represent the required logical qubits on the D-Wave Advantage system.

Gate-based simulator results

The gate-based approach was simulated using the IBM Qasm noisy simulator, which can simulate up to 24 fully connected qubits. The BQM implementation was identical to the scheme described by the QA (Fig 1), and the optimization was carried out using QAOA [37]. The codon optimization simulation was run on 313 peptides of length four and one peptide of length three, each requiring between 7–24 logical qubits with full connectivity. In the most resource-intensive scenario, there could be four amino acids in a row that each map to six qubits, and the algorithm is tasked with finding the ground state out of 6⁴ = 1296 possible states using 24 qubits (the maximum supported by the simulator). The task is therefore small enough to compute the exact result with the NumPyMinimumEigensolver exact solver, negating the need to compare the performance to a classical GA.

The simulated scores vs the exact scores are shown in Fig 4a. The quantum algorithm identified the exact solution for 59 peptides, and overall, 84% of the trials yielded valid results. However, there were 51 cases where the quantum algorithm returned results which had an invalid mapping between codons and qubits. In each of the invalid trials there was at least one amino acid position lacking a selected codon. This type of error was more common in cases where more qubits were required to run the simulation (minimum 16 qubits), and therefore the optimization task was more difficult. The success of the calculation was dependent on the random seed used for the noisy simulator; rerunning trials that failed to return valid results with different random seeds changed the outcome and, in each case, a valid result was eventually found. The instability of the simulation and its dependence on the number of qubits required to run the simulation is an artifact not just of the simulated noise but also an inherent consequence of the variational algorithm employed by the simulator, which is not guaranteed to converge every time. See the “BQM challenges and limitations” section in the Supplementary Information for discussion about the exponentially small subset of valid solutions in the total possible solution space as the number of required qubits is increased. These results imply that it may be impossible to effectively optimize polypeptides of biological relevance on NISQ devices. Further studies are required to determine if this behavior could be improved by making use of alternative variational optimization algorithms or whether fundamental improvements in error correction are needed at the hardware level.

Download:

Fig 4.

(a) QAOA simulated scores vs exact scores. Each point represents a single run of the simulator for each peptide fragment. Lower scores indicate higher quality results. Trials that returned solutions with invalid mapping between codons and amino acids were excluded. Inset shows a histogram of the QAOA score to exact score ratios where it can be seen that the majority of the ratios are close to 1. (b) Number of qubits required to simulate each of the 313 peptide fragments of length 4.

https://doi.org/10.1371/journal.pone.0259101.g004

Discussion

Codon optimization is a classic example of a biological problem with exponential scaling in solution space. While there exist classical machine learning and artificial intelligence methods that improve sampling, quantum technology may offer an alternative or complimentary approach to enhance the ability to identify optimal samples from these distributions. Current generations of quantum hardware are mature enough to test ideas for novel algorithmic approaches to problems in life sciences but are not yet capable of outperforming classical devices. In this study codon optimization is reformulated to be readily implementable on quantum devices and the viability of the method is demonstrated on both adiabatic and gate-based quantum computers.

The D-Wave Systems Advantage 1.1 system was heuristically determined to be capable of solving codon optimization problems programmed directly into the QPU up to approximately 180 codons with scoring functions requiring all logical qubits to be fully connected, which maps to 30 amino acids in the most resource intensive cases. If a scoring function is selected that does not require full connectivity, such as the Hamiltonian describing repeated nucleotides, then nearly all the logical qubits could map directly to physical qubits and the system size could theoretically be scaled to accommodate sequences of up to ~1,000 amino acids. Such an objective function would be appropriate if global sequence properties (e.g., GC-content) are not required in the study.

There is likely significant room for performance improvements in the quantum, hybrid, and classical methods. As quantum annealing hardware matures it will be possible to address questions of scalability for larger sequences and critically assess the potential for quantum technology to surpass classical techniques.

The IBM Experience provides free access to small quantum devices and noisy simulators. The largest quantum device freely available at the time of writing, Melbourne, contains 15 qubits, with each qubit coupled to no more than three others. The BQM presented in this study requires full connectivity between the logical qubits. Melbourne is therefore able to represent a select set of 2-amino acid systems in which each amino acid maps to one or two codons. Meanwhile the IBM Qasm simulator allows up to 24 fully connected simulated qubits, which is generally limited to 4 amino acids. This simulation approach is sufficient to use QAOA to solve small proof of concept problems, but realistically thousands of qubits with high connectivity are required to run biologically relevant sequences. Given IBM’s current public Roadmap for Scaling Quantum Technology [40], devices with this capacity are not expected to be available to the public before 2024. Utilizing the simulator, the QAOA algorithm had variable performance, identifying the true ground state solution in nearly 20% of the trials, and failing to identify a valid solution in 16% of the trials. As noted above the success of the calculation was dependent on the random seed used for the noisy simulator; valid results for each failed run can be obtained by rerunning with different random seeds until a valid result is found. Further investigation on physical devices is required to determine the limit of the accuracy and precision of the method.

There is a vast body of literature discussing codon optimization techniques for protein expression. This study contributes to the field by offering a novel approach to sampling the vast solution space with an emerging technology. The considerations that went into the construction of the Hamiltonian were designed to highlight some of the implementational nuances associated with common scoring functions. For example, the expression for measuring optimality of GC-content requires full connectivity between qubits. However, counting repeated nucleotides between neighbors only requires some qubits to be coupled. Codon usage bias can be factored into the Hamiltonian using one-body terms, but one could imagine a more thorough approach which compares the distribution of codons selected in the sequence compared to the reference distribution, which would require two-body terms. Similarly, implementing the popular “one amino acid-one codon” strategy would require codons from each type of amino acid to be coupled [18,21,43], adding many two-body terms but not as many as optimization of GC-content. Thus, the particular Hamiltonian studied here serves as a demonstration of the efficacy of the method at evaluating a realistically complicated objective function.

Further studies are required to evaluate these samples beyond simple numerical scores to determine the value this approach could add to the field of protein expression using future generations of quantum hardware. Current quantum hardware is subject to high levels of noise and is therefore not competitive with classical techniques in most practical applications. However, quantum hardware and techniques are advancing exponentially in terms of both scale and tolerance to noise. This rapid advancement coupled with the expectation that scaling with problem size is significantly reduced on quantum hardware compared with classical methods implies that future quantum approaches could provide a significant performance advantage for optimization of NP problems in life sciences.

Methods

Codon optimization algorithm

Classical scoring functions can be reinterpreted as a Hamiltonian by separating one- and two-body interaction terms. There is considerable research and ongoing debate of the proper way to score nucleotide sequences for expression [5,8,10–21,24–27,43], as well as arguments against the use of codon optimization in some cases [5]. The purpose of this study is not to contribute to the discussion of whether or not codon optimization is an appropriate tool for any given context or the optimal way in which it should be performed but rather to investigate a novel method of sampling the vast solution space. The optimization task is therefore restricted to three considerations that capture simple countable properties from the sequences themselves. This includes discussion of how to formulate energetic terms in the Hamiltonian that serve to minimize:

Codon usage bias.
The difference between GC-content and a target value.
The number of sequentially repeated nucleotides.

Energetic terms can be combined into one Hamiltonian or used on their own, just like a scoring function in a GA. Furthermore, the Hamiltonian could be extended using any type of scoring function that can be broken down into one- and two- body interaction terms. The following sections outline the mathematical representations of these optimization tasks and show how they naturally map to a Hamiltonian with a form compatible with a BQM.

Two additional constraints are imposed to add energetic penalties to combinations of codons that do not translate to the query sequence. The first constraint adds a small linear shift to the one-body term of each qubit. Shifting the potentials increases the energetic favorability of including more codons in the sequence. Similarly, the other constraint adds a significant energetic penalty to codons mapping to the same position in the amino acid sequence. The combination of these two potentials optimizes the energetic score of valid combinations of codons compared to invalid combinations.

Incorporating codon usage bias.

Codon usage frequency varies by host system [44]. Therefore, the scoring function is tailored to match the expression system. For this study, codon usage frequencies for e. coli are imported from the python-codon-tables 0.1.10 library [45]. Let C_i represent the frequency of finding codon C at position i. The potential is thus designed to return a large penalty for rare codons (where C_i is small) and incur a negligible penalty for codons readily available to the system (where C_i is large). One such function is the logarithm. This function yields the desired behavior of adding large penalties to rare codons and adding small penalties to accessible codons. However, the function is undefined at C_i = 0. If a host system truly does not have access to a given codon, then any sequence containing the codon is not expressible, and the probability of expression would be zero. An infinitesimal value, ε_f, is added to the frequencies to avoid evaluating the logarithm at an undefined point. For a system containing N possible codons, the Hamiltonian is given by: (2) where c_f is a tunable constant, q_i ∈ {0,1} is the value of the qubit, and ζ is a vector containing the values of the log inverse codon usage frequencies. Given the binary q_i values, the Hamiltonian only penalizes codons that are “selected”, represented by qubits with value q_i = 1.

Optimize target GC concentration.

To optimize the GC concentration of a nucleotide sequence, ρ_GC, a cost function Δ must be introduced to minimize the difference between ρ_GC and the target GC concentration, ρ_T. The simplest objective function satisfying this constraint is a quadratic function, (3) where c_GC is a tunable constant. The GC content is calculated by summing the number of G’s and C’s in the sequence of length N and normalizing by the number of nucleotides in the sequence (4) where s_i is an integer representing the number of G’s and C’s in codon i, and q_i represents the value of qubit i. By expanding Eq (3), a form similar to a Binary Quadratic Model (BQM) formulation becomes apparent: (5)

The matrix represented in the double sum needs to be restricted to a sum over the upper triangular elements, consistent with Eq (1). By decomposing the sum into the trace and a term that sums the contributions of the off-diagonal elements, the sum can be restricted to the upper triangular elements. The trace requires a single summation over s_i². Since qubits map to binary values, they are idempotent with themselves and therefore q_i² = q_i.

(6)

Since the matrix is symmetric, all off-diagonal terms are accounted for in an upper triangular form by multiplying by 2. Thus, (7) recapitulates the quadratic cost function (Eq (3)) in a form consistent with a BQM (Eq (1)). See the GC-Content Derivation section in the Supplementary Information for a full derivation.

Minimize sequentially repeated nucleotides.

To minimize the number of repeated nucleotides in a sequence, all codons mapping to sequential positions in the amino acid sequence are compared. Let r(C_i, C_j) represent a quadratic function that returns the maximum number of repeated sequential nucleotides between codons C_i and C_j, shifted to the origin for null cases by subtracting one. For example,

Repeated nucleotide penalties are stored in a matrix R, (8) where the kappa-function returns 1 if the codons map to sequential positions and 0 otherwise.

(9)

S1 Fig in S1 File shows the result of applying this function to the system in Fig 1. The total repeated nucleotide penalty for a given nucleotide sequence is given by (10)

Matrix R is upper triangular, so the pairwise sum can be restricted to upper triangular elements without changing the result, making it compatible with the BQM model (Eq (1)). A tunable constant, c_R, is introduced to weight the contribution in the Hamiltonian, resulting in the form: (11)

Additional constraints to the Hamiltonian.

The energetic terms described above are designed to add penalties to particular sequence properties. As formulated, the ground state of this type of objective function, without constraints, is the state which selects zero codons. However, the Hamiltonian needs to be formulated so that the ground state corresponds to the sequence of codons that both translates to the input polypeptide sequence and minimizes the constraint penalties. To achieve this, a constant factor, ε, is subtracted from the one-body term of each codon, thereby increasing the energetic favorability of introducing codons to the system. This effectively moves the origin so that the ground state must include codons. The absolute value of the constant must exceed the largest value in the one-body vector h (Eq (1)).

To negate the possibility of assigning more than one codon to a given position, a site-specific function is introduced that applies an effectively infinite penalty to pairs of codons assigned to the same position.

(12)

In practice, a specific value must be chosen to represent infinity. This value must be large enough to skew the energy surface such that it serves as an effective barrier in the optimization, however, if it is too large then it will decrease the resolution of the programmable fields in the D-Wave device. Therefore, the value was heuristically set to 50 effective h-units (the solver rescales the input values to program the magnetic fields), which satisfied this balance. S2 Fig in S1 File shows the result of applying Eq (12) to the example system referenced in Fig 1. For a system with N possible codons, the Hamiltonian is modified by adding the following terms: (13)

Implementation of objective function.

The Hamiltonian representing the total “energy” of a nucleotide sequence is computed by summing the contributions of the expressions defined in the previous sections.

(14)

Expanding and rearranging the terms gives the form in Eq (15): (15)

This form is consistent with the BQM formalism (Eq (1)) and can be directly implemented into BQM frameworks.

Algorithm implementations

The current approach to performing calculations on quantum devices requires the interaction terms to be precomputed on classical devices and read into the quantum devices via specialized APIs. The one- and two-body interaction terms from Eq (15) were precomputed in python 3.7 using standard libraries and numpy [46] arrays. The numpy arrays were converted to dictionaries in accordance with the expected input for the quantum device libraries.

The execution of the BQM was carried out using libraries described in the following sections. Each calculation was run 20 times for small peptide fragments (<100 residues), and full-length proteins were only run one time due to QPU resource constraints. Table 1 provides the values of the constants used in the objective function. The eigenstate returned by the quantum devices was converted back to a nucleotide sequence and translated to a polypeptide sequence to verify the validity of the result. Furthermore, the nucleotide sequence was scored using the classical scoring function used by the GA, and the result was compared to the eigenvalue returned by the quantum devices. Eigenvalues and classical scores agreed to 8 decimal places.

Download:

Table 1. Constants used in Eq (15).

https://doi.org/10.1371/journal.pone.0259101.t001

D-Wave Advantage 1.1.

The codon optimization BQM was implemented on the D-Wave Advantage System 1.1 utilizing the Leap Hybrid Solver. This adiabatic quantum device contains more than 5,000 superconducting qubits. Each qubit is connected to 15 others described by a Pegasus P₁₆ graph [42]. The Advantage system was accessed through the D-Wave Leap web interface, which serves as an access point to QPU hardware as well as an integrated developer environment with built-in support for the full D-Wave API.

The program was constructed and executed using python libraries provided by D-Wave systems. The BinaryQuadraticModel class in the dimod 0.9.10 python library was used to construct the model from the classically prepared data and convert it to a data structure compatible with the quantum device. The one- and two-body interaction terms were precomputed, stored in numpy arrays, and passed into the BinaryQuadraticModel instance along with an offset of 0.0 and the dimod.BINARY representation. The model was executed using the LeapHybridSampler classes in the dwave.system python library. The solver was allotted 6.0 s of execution time for small cases (< 100 qubits) and 50.0 s for larger cases (> 100 qubits). The Leap system does not provide a detailed report of how much computation was handled by the QPU. The eigenstate with the lowest associated eigenvalue was chosen to represent the result of the simulation.

Qiskit Qasm simulator.

The BQM was implemented in qiskit through the IBM Experience web interface. This interface provides access to IBM Q hardware and an integrated developer environment served by Jupyter Notebooks with all fundamental Qiskit 0.16.4 python libraries installed [41]. The IBM Experience provides limited free access to quantum devices, but the available devices were too small (<= 15 qubits) to sample the codon optimization BQM problem, so a noisy simulator hosted by the IBM Experience on classical hardware was used in its place.

The program was constructed and executed using python libraries provided by IBM Qiskit. The core of the implementation builds on the QuadraticProgram base class from the qiskit.optimization library. The codons were appended to the model as binary variables, physically represented by qubits. The objective function was constructed as a minimization with the one- and two-body precomputed terms and an offset of 0.0. The model was simulated using the Aer class with the qasm_simulator backend and FakeVigo noise data, both from the Qiskit library, and the simulator was converted to an executable Quadratic Unconstrained Binary Optimization (QUBO) model with the MinimumEigenOptimizer from the qiskit.optimization.algorithms library. Finally, the combinatorial optimization was carried out with QAOA [37] from the qiskit.aqua.algorithms library. The program was run with default values where possible.

Classic genetic algorithm.

A basic single-threaded genetic algorithm with an objective function mathematically equivalent to Eq (15) was implemented in Python 3.7 using standard libraries and BioPython 1.78 [47]. For polypeptide fragments (<= 20 residues), the simulations were run for 100 iterations (generations), with each generation procreating 50 times. For full-length sequences (> 100 residues), the number of iterations was increased to 6000. The calculations were run on the Leap web interface to provide an accurate execution time comparison to the QA calculations. See the Genetic Algorithm Validation section in the Supplementary Information for performance metrics.

Test applications.

Sample peptide sequences were obtained by splitting the human severe acute respiratory syndrome coronavirus 2 (2019-nCoV, SARS-CoV-2) spike glycoprotein sequence (UniProtKB–P0DTC2) into shorter peptide fragments for simulation on the resource-constrained systems. The sample preparation for the D-Wave system yielded 62 peptides of length 20 and one additional peptide of length 15. The IBM Qasm simulator was simulated with 313 peptides of length 4 and one additional peptide of length 3. Additionally, 10 full-length proteins associated with SARS-CoV-2 studies were scored using the QA and the GA approaches for comparison. See the Sequences section of the Supplementary Information for the full list.

Supporting information

S1 File. Contains additional information and derivations for GC-content derivation, BQM challenges and limitations, Genetic Algorithm Validation, S1-S4 Figs and the protein sequences used.

https://doi.org/10.1371/journal.pone.0259101.s001

(DOCX)

Acknowledgments

We acknowledge the use of IBM Quantum services for this work. The views expressed are those of the authors, and do not reflect the official policy or position of IBM or the IBM Quantum team. We would like to thank our GSK colleagues Eric Manas, Chris MacDermaid and Simon Kelow of GSK for evaluation and suggested refinements, and Andrea Schreij for helping with graphic design.

References

1. Kimchi-Sarfaty C, Oh JM, Kim I-W, Sauna Z, Calcagno AM, Ambudkar S, et al. A “Silent” Polymorphism in the MDR1 Gene Changes Substrate Specificity. Science. 2007;315: 525–529. pmid:17185560
- View Article
- PubMed/NCBI
- Google Scholar
2. Buhr F, Jha S, Thommen M, Mittelstaet J, Kutz F, Schwalbe H, et al. Synonymous Codons Direct Cotranslational Folding toward Different Protein Conformations. Molecular Cell. 2016;61: 341–351. pmid:26849192
- View Article
- PubMed/NCBI
- Google Scholar
3. Kirchner S, Cai Z, Rauscher R, Kastelic N, Anding M, Czech A, et al. Alteration of protein function by a silent polymorphism linked to tRNA abundance. PLoS Biology. 2017;15. pmid:28510592
- View Article
- PubMed/NCBI
- Google Scholar
4. Shabalina SA, Spiridonov NA, Kashina A. Sounds of silence: Synonymous nucleotides as a key to biological regulation and complexity. Nucleic Acids Research. 2013;41: 2073–2094. pmid:23293005
- View Article
- PubMed/NCBI
- Google Scholar
5. Mauro VP, Chappell SA. A critical analysis of codon optimization in human therapeutics Optimizing codon usage for increased protein expression. Trends in Molecular Medicine. 2014;20: 604–613.
- View Article
- Google Scholar
6. Gustafsson C, Govindarajan S, Minshull J. Codon bias and heterologous protein expression. Trends in Biotechnology. 2004;22: 346–353. pmid:15245907
- View Article
- PubMed/NCBI
- Google Scholar
7. Brule CE, Grayhack EJ. Synonymous Codons: Choose Wisely for Expression. Trends in Genetics. 2017;33: 283–297. pmid:28292534
- View Article
- PubMed/NCBI
- Google Scholar
8. Parret AH, Besir H, Meijers R. Critical reflections on synthetic gene design for recombinant protein expression. Current Opinion in Structural Biology. 2016;38: 155–162. pmid:27449695
- View Article
- PubMed/NCBI
- Google Scholar
9. Quax TEF, Claassens NJ, Söll D, van der Oost J. Codon Bias as a Means to Fine-Tune Gene Expression. Molecular Cell. 2015;59: 149–161. pmid:26186290
- View Article
- PubMed/NCBI
- Google Scholar
10. Gustafsson C, Minshull J, Govindarajan S, Ness J, Villalobos A, Welch M. Engineering genes for predictable protein expression. Protein Expression and Purification. 2012;83: 37–46. pmid:22425659
- View Article
- PubMed/NCBI
- Google Scholar
11. Kim CH, Oh Y, Lee TH. Codon optimization for high-level expression of human erythropoietin (EPO) in mammalian cells. Gene. 1997;199: 293–301. pmid:9358069
- View Article
- PubMed/NCBI
- Google Scholar
12. Wang X, Li X, Zhang Z, Shen X, Zhong F. Codon optimization enhances secretory expression of Pseudomonas aeruginosa exotoxin A in E. coli. Protein Expression and Purification. 2010;72: 101–106. pmid:20172029
- View Article
- PubMed/NCBI
- Google Scholar
13. Inouye S, Sahara-Miura Y, Sato JI, Suzuki T. Codon optimization of genes for efficient protein expression in mammalian cells by selection of only preferred human codons This paper is dedicated to the late Professor J. Woodland Hastings (deceased on August 6, 2014). Protein Expression and Purification. 2015;109: 47–54. pmid:25665506
- View Article
- PubMed/NCBI
- Google Scholar
14. Newman ZR, Young JM, Ingolia NT, Barton GM. Differences in codon bias and GC content contribute to the balanced expression of TLR7 and TLR9. Proceedings of the National Academy of Sciences of the United States of America. 2016;113: E1362–E1371. pmid:26903634
- View Article
- PubMed/NCBI
- Google Scholar
15. Barahimipour R, Strenkert D, Neupert J, Schroda M, Merchant SS, Bock R. Dissecting the contributions of GC content and codon usage to gene expression in the model alga Chlamydomonas reinhardtii. Plant Journal. 2015;84: 704–717. pmid:26402748
- View Article
- PubMed/NCBI
- Google Scholar
16. Chung BKS, Lee DY. Computational codon optimization of synthetic gene for protein expression. BMC Systems Biology. 2012;6. pmid:23083100
- View Article
- PubMed/NCBI
- Google Scholar
17. Nieuwkoop T, Claassens NJ, van der Oost J. Improved protein production and codon optimization analyses in Escherichia coli by bicistronic design. Microbial Biotechnology. 2019;12: 173–179. pmid:30484964
- View Article
- PubMed/NCBI
- Google Scholar
18. Puigbò P, Guzmán E, Romeu A, Garcia-Vallvé S. OPTIMIZER: A web server for optimizing the codon usage of DNA sequences. Nucleic Acids Research. 2007;35: 126–131. pmid:17439967
- View Article
- PubMed/NCBI
- Google Scholar
19. Supek F, Vlahoviček K. INCA: Synonymous codon usage analysis and clustering by means of self-organizing map. Bioinformatics. 2004;20: 2329–2330. pmid:15059815
- View Article
- PubMed/NCBI
- Google Scholar
20. Chin JX, Chung BKS, Lee DY. Codon Optimization OnLine (COOL): A web-based multi-objective optimization platform for synthetic gene design. Bioinformatics. 2014;30: 2210–2212. pmid:24728853
- View Article
- PubMed/NCBI
- Google Scholar
21. Rehbein P, Berz J, Kreisel P, Schwalbe H. “CodonWizard”–An intuitive software tool with graphical user interface for customizable codon optimization in protein expression efforts. Protein Expression and Purification. 2019;160: 84–93. pmid:30953700
- View Article
- PubMed/NCBI
- Google Scholar
22. Kudla G, Murray AW, Tollervey D, Plotkin JB. Coding-sequence determinants of gene expression in Escherichia coli. Science. 2008;23: 1–7.
- View Article
- Google Scholar
23. Holland JH. Adaptation in Natural and Artificial Systems. Cambridge, MA: MIT Press; 1992.
24. Villalobos A, Ness JE, Gustafsson C, Minshull J, Govindarajan S. Gene Designer: A synthetic biology tool for constructuring artificial DNA segments. BMC Bioinformatics. 2006;7: 1–8.
- View Article
- Google Scholar
25. Sandhu KS, Pandey S, Maiti S, Pillai B. GASCO: Genetic Algorithm Simulation for Codon Optimization. In Silico Biology. 2008;8: 187–192. pmid:18928205
- View Article
- PubMed/NCBI
- Google Scholar
26. Fu H, Liang Y, Zhong X, Pan ZL, Huang L, Zhang HL, et al. Codon optimization with deep learning to enhance protein expression. Scientific Reports. 2020;10: 1–9.
- View Article
- Google Scholar
27. Lanza AM, Curran KA, Rey LG, Alper HS. A condition-specific codon optimization approach for improved heterologous gene expression in Saccharomyces cerevisiae. BMC Systems Biology. 2014;8: 1–10.
- View Article
- Google Scholar
28. Zhong H-S, Wang H, Deng Y-H, Chen M-C, Peng L-C, Luo Y-H, et al. Quantum computational advantage using photons. Science. 2020;1463: 1460–1463. pmid:33273064
- View Article
- PubMed/NCBI
- Google Scholar
29. Arute F, Arya K, Babbush R, Bacon D, Bardin JC, Barends R, et al. Quantum supremacy using a programmable superconducting processor. Nature. 2019;574: 505–510. pmid:31645734
- View Article
- PubMed/NCBI
- Google Scholar
30. Djidjev H, Chapuis G, Hahn G, Rizk G. Efficient combinatorial optimization using quantum annealing. arXiv. 2018; 1–25.
- View Article
- Google Scholar
31. Albash T, Lidar DA. Adiabatic quantum computation. Reviews of Modern Physics. 2018;90: 15002.
- View Article
- Google Scholar
32. Doll JD, Gomez MA, Stenson C, Finnila AB, Sebenik C. Quantum annealing: A new method for minimizing multidimensional functions. Chemical Physics Letters. 2002;219: 343–348.
- View Article
- Google Scholar
33. Farhi E, Goldstone J, Gutmann S, Lapan J, Lundgren A, Preda D. A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem. Science. 2001;292: 472–476. pmid:11313487
- View Article
- PubMed/NCBI
- Google Scholar
34. Mulligan VK, Melo H, Merritt HI, Slocum S, Weitzner BD, Watkins AM, et al. Designing Peptides on a Quantum Computer. bioRxiv. 2019; 1–20.
- View Article
- Google Scholar
35. Muthukrishnan S, Albash T, Lidar DA. Tunneling and speedup in quantum optimization for permutation-symmetric problems. Physical Review X. 2016;6: 1–23.
- View Article
- Google Scholar
36. Preskill J. Quantum computing in the NISQ era and beyond. Quantum. 2018;2: 1–20.
- View Article
- Google Scholar
37. Farhi E, Goldstone J, Gutmann S. A Quantum Approximate Optimization Algorithm. arXiv. 2014; 1–16.
- View Article
- Google Scholar
38. Kochenberger G, Hao JK, Glover F, Lewis M, Lü Z, Wang H, et al. The unconstrained binary quadratic programming problem: A survey. Journal of Combinatorial Optimization. 2014;28: 58–81.
- View Article
- Google Scholar
39. Date P, Patton R, Schuman C, Potok T. Efficiently embedding QUBO problems on adiabatic quantum computers. Quantum Information Processing. 2019;18: 1–31.
- View Article
- Google Scholar
40. Gambetta JM. IBM’s Roadmap For Scaling Quantum Technology. In: IBM Research Blog [Internet]. 2020. https://www.ibm.com/blogs/research/2020/09/ibm-quantum-roadmap/.
41. Abraham H et al. Qiskit: An Open-source Framework for Quantum Computing. 2019.
- View Article
- Google Scholar
42. Dattani N, Szalay S, Chancellor N. Pegasus: The second connectivity graph for large-scale quantum annealing hardware. arXiv. 2019.
- View Article
- Google Scholar
43. Menzella HG. Comparison of two codon optimization strategies to enhance recombinant protein production in Escherichia coli. Microbial Cell Factories. 2011;10: 11–15.
- View Article
- Google Scholar
44. Grantham R, Gautier C, Gouy M, Mercier R, Pavé A. Codon catalog usage and the genome hypothesis. Nucleic Acids Research. 1980;8: 197.
- View Article
- Google Scholar
45. Nakamura Y, Gojobori T, Ikemura T. Codon usage tabulated from international DNA sequence databases: Status for the year 2000. Nucleic Acids Research. 2000;28: 292. pmid:10592250
- View Article
- PubMed/NCBI
- Google Scholar
46. Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with {NumPy}. Nature. 2020;585: 357–362. pmid:32939066
- View Article
- PubMed/NCBI
- Google Scholar
47. Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, et al. Biopython: Freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009;25: 1422–1423. pmid:19304878
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Kimchi-Sarfaty C, Oh JM, Kim I-W, Sauna Z, Calcagno AM, Ambudkar S, et al. A “Silent” Polymorphism in the MDR1 Gene Changes Substrate Specificity. Science. 2007;315: 525–529. pmid:17185560
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Buhr F, Jha S, Thommen M, Mittelstaet J, Kutz F, Schwalbe H, et al. Synonymous Codons Direct Cotranslational Folding toward Different Protein Conformations. Molecular Cell. 2016;61: 341–351. pmid:26849192
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Kirchner S, Cai Z, Rauscher R, Kastelic N, Anding M, Czech A, et al. Alteration of protein function by a silent polymorphism linked to tRNA abundance. PLoS Biology. 2017;15. pmid:28510592
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Shabalina SA, Spiridonov NA, Kashina A. Sounds of silence: Synonymous nucleotides as a key to biological regulation and complexity. Nucleic Acids Research. 2013;41: 2073–2094. pmid:23293005
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Mauro VP, Chappell SA. A critical analysis of codon optimization in human therapeutics Optimizing codon usage for increased protein expression. Trends in Molecular Medicine. 2014;20: 604–613.
View Article
Google Scholar

[18] View Article

[19] Google Scholar

[ref6] 6. Gustafsson C, Govindarajan S, Minshull J. Codon bias and heterologous protein expression. Trends in Biotechnology. 2004;22: 346–353. pmid:15245907
View Article
PubMed/NCBI
Google Scholar

[21] View Article

[22] PubMed/NCBI

[23] Google Scholar

[ref7] 7. Brule CE, Grayhack EJ. Synonymous Codons: Choose Wisely for Expression. Trends in Genetics. 2017;33: 283–297. pmid:28292534
View Article
PubMed/NCBI
Google Scholar

[25] View Article

[26] PubMed/NCBI

[27] Google Scholar

[ref8] 8. Parret AH, Besir H, Meijers R. Critical reflections on synthetic gene design for recombinant protein expression. Current Opinion in Structural Biology. 2016;38: 155–162. pmid:27449695
View Article
PubMed/NCBI
Google Scholar

[29] View Article

[30] PubMed/NCBI

[31] Google Scholar

[ref9] 9. Quax TEF, Claassens NJ, Söll D, van der Oost J. Codon Bias as a Means to Fine-Tune Gene Expression. Molecular Cell. 2015;59: 149–161. pmid:26186290
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref10] 10. Gustafsson C, Minshull J, Govindarajan S, Ness J, Villalobos A, Welch M. Engineering genes for predictable protein expression. Protein Expression and Purification. 2012;83: 37–46. pmid:22425659
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref11] 11. Kim CH, Oh Y, Lee TH. Codon optimization for high-level expression of human erythropoietin (EPO) in mammalian cells. Gene. 1997;199: 293–301. pmid:9358069
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref12] 12. Wang X, Li X, Zhang Z, Shen X, Zhong F. Codon optimization enhances secretory expression of Pseudomonas aeruginosa exotoxin A in E. coli. Protein Expression and Purification. 2010;72: 101–106. pmid:20172029
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref13] 13. Inouye S, Sahara-Miura Y, Sato JI, Suzuki T. Codon optimization of genes for efficient protein expression in mammalian cells by selection of only preferred human codons This paper is dedicated to the late Professor J. Woodland Hastings (deceased on August 6, 2014). Protein Expression and Purification. 2015;109: 47–54. pmid:25665506
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref14] 14. Newman ZR, Young JM, Ingolia NT, Barton GM. Differences in codon bias and GC content contribute to the balanced expression of TLR7 and TLR9. Proceedings of the National Academy of Sciences of the United States of America. 2016;113: E1362–E1371. pmid:26903634
View Article
PubMed/NCBI
Google Scholar

[53] View Article

[54] PubMed/NCBI

[55] Google Scholar

[ref15] 15. Barahimipour R, Strenkert D, Neupert J, Schroda M, Merchant SS, Bock R. Dissecting the contributions of GC content and codon usage to gene expression in the model alga Chlamydomonas reinhardtii. Plant Journal. 2015;84: 704–717. pmid:26402748
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref16] 16. Chung BKS, Lee DY. Computational codon optimization of synthetic gene for protein expression. BMC Systems Biology. 2012;6. pmid:23083100
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref17] 17. Nieuwkoop T, Claassens NJ, van der Oost J. Improved protein production and codon optimization analyses in Escherichia coli by bicistronic design. Microbial Biotechnology. 2019;12: 173–179. pmid:30484964
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref18] 18. Puigbò P, Guzmán E, Romeu A, Garcia-Vallvé S. OPTIMIZER: A web server for optimizing the codon usage of DNA sequences. Nucleic Acids Research. 2007;35: 126–131. pmid:17439967
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref19] 19. Supek F, Vlahoviček K. INCA: Synonymous codon usage analysis and clustering by means of self-organizing map. Bioinformatics. 2004;20: 2329–2330. pmid:15059815
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref20] 20. Chin JX, Chung BKS, Lee DY. Codon Optimization OnLine (COOL): A web-based multi-objective optimization platform for synthetic gene design. Bioinformatics. 2014;30: 2210–2212. pmid:24728853
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref21] 21. Rehbein P, Berz J, Kreisel P, Schwalbe H. “CodonWizard”–An intuitive software tool with graphical user interface for customizable codon optimization in protein expression efforts. Protein Expression and Purification. 2019;160: 84–93. pmid:30953700
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref22] 22. Kudla G, Murray AW, Tollervey D, Plotkin JB. Coding-sequence determinants of gene expression in Escherichia coli. Science. 2008;23: 1–7.
View Article
Google Scholar

[85] View Article

[86] Google Scholar

[ref23] 23. Holland JH. Adaptation in Natural and Artificial Systems. Cambridge, MA: MIT Press; 1992.

[ref24] 24. Villalobos A, Ness JE, Gustafsson C, Minshull J, Govindarajan S. Gene Designer: A synthetic biology tool for constructuring artificial DNA segments. BMC Bioinformatics. 2006;7: 1–8.
View Article
Google Scholar

[89] View Article

[90] Google Scholar

[ref25] 25. Sandhu KS, Pandey S, Maiti S, Pillai B. GASCO: Genetic Algorithm Simulation for Codon Optimization. In Silico Biology. 2008;8: 187–192. pmid:18928205
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref26] 26. Fu H, Liang Y, Zhong X, Pan ZL, Huang L, Zhang HL, et al. Codon optimization with deep learning to enhance protein expression. Scientific Reports. 2020;10: 1–9.
View Article
Google Scholar

[96] View Article

[97] Google Scholar

[ref27] 27. Lanza AM, Curran KA, Rey LG, Alper HS. A condition-specific codon optimization approach for improved heterologous gene expression in Saccharomyces cerevisiae. BMC Systems Biology. 2014;8: 1–10.
View Article
Google Scholar

[99] View Article

[100] Google Scholar

[ref28] 28. Zhong H-S, Wang H, Deng Y-H, Chen M-C, Peng L-C, Luo Y-H, et al. Quantum computational advantage using photons. Science. 2020;1463: 1460–1463. pmid:33273064
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref29] 29. Arute F, Arya K, Babbush R, Bacon D, Bardin JC, Barends R, et al. Quantum supremacy using a programmable superconducting processor. Nature. 2019;574: 505–510. pmid:31645734
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref30] 30. Djidjev H, Chapuis G, Hahn G, Rizk G. Efficient combinatorial optimization using quantum annealing. arXiv. 2018; 1–25.
View Article
Google Scholar

[110] View Article

[111] Google Scholar

[ref31] 31. Albash T, Lidar DA. Adiabatic quantum computation. Reviews of Modern Physics. 2018;90: 15002.
View Article
Google Scholar

[113] View Article

[114] Google Scholar

[ref32] 32. Doll JD, Gomez MA, Stenson C, Finnila AB, Sebenik C. Quantum annealing: A new method for minimizing multidimensional functions. Chemical Physics Letters. 2002;219: 343–348.
View Article
Google Scholar

[116] View Article

[117] Google Scholar

[ref33] 33. Farhi E, Goldstone J, Gutmann S, Lapan J, Lundgren A, Preda D. A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem. Science. 2001;292: 472–476. pmid:11313487
View Article
PubMed/NCBI
Google Scholar

[119] View Article

[120] PubMed/NCBI

[121] Google Scholar

[ref34] 34. Mulligan VK, Melo H, Merritt HI, Slocum S, Weitzner BD, Watkins AM, et al. Designing Peptides on a Quantum Computer. bioRxiv. 2019; 1–20.
View Article
Google Scholar

[123] View Article

[124] Google Scholar

[ref35] 35. Muthukrishnan S, Albash T, Lidar DA. Tunneling and speedup in quantum optimization for permutation-symmetric problems. Physical Review X. 2016;6: 1–23.
View Article
Google Scholar

[126] View Article

[127] Google Scholar

[ref36] 36. Preskill J. Quantum computing in the NISQ era and beyond. Quantum. 2018;2: 1–20.
View Article
Google Scholar

[129] View Article

[130] Google Scholar

[ref37] 37. Farhi E, Goldstone J, Gutmann S. A Quantum Approximate Optimization Algorithm. arXiv. 2014; 1–16.
View Article
Google Scholar

[132] View Article

[133] Google Scholar

[ref38] 38. Kochenberger G, Hao JK, Glover F, Lewis M, Lü Z, Wang H, et al. The unconstrained binary quadratic programming problem: A survey. Journal of Combinatorial Optimization. 2014;28: 58–81.
View Article
Google Scholar

[135] View Article

[136] Google Scholar

[ref39] 39. Date P, Patton R, Schuman C, Potok T. Efficiently embedding QUBO problems on adiabatic quantum computers. Quantum Information Processing. 2019;18: 1–31.
View Article
Google Scholar

[138] View Article

[139] Google Scholar

[ref40] 40. Gambetta JM. IBM’s Roadmap For Scaling Quantum Technology. In: IBM Research Blog [Internet]. 2020. https://www.ibm.com/blogs/research/2020/09/ibm-quantum-roadmap/.

[ref41] 41. Abraham H et al. Qiskit: An Open-source Framework for Quantum Computing. 2019.
View Article
Google Scholar

[142] View Article

[143] Google Scholar

[ref42] 42. Dattani N, Szalay S, Chancellor N. Pegasus: The second connectivity graph for large-scale quantum annealing hardware. arXiv. 2019.
View Article
Google Scholar

[145] View Article

[146] Google Scholar

[ref43] 43. Menzella HG. Comparison of two codon optimization strategies to enhance recombinant protein production in Escherichia coli. Microbial Cell Factories. 2011;10: 11–15.
View Article
Google Scholar

[148] View Article

[149] Google Scholar

[ref44] 44. Grantham R, Gautier C, Gouy M, Mercier R, Pavé A. Codon catalog usage and the genome hypothesis. Nucleic Acids Research. 1980;8: 197.
View Article
Google Scholar

[151] View Article

[152] Google Scholar

[ref45] 45. Nakamura Y, Gojobori T, Ikemura T. Codon usage tabulated from international DNA sequence databases: Status for the year 2000. Nucleic Acids Research. 2000;28: 292. pmid:10592250
View Article
PubMed/NCBI
Google Scholar

[154] View Article

[155] PubMed/NCBI

[156] Google Scholar

[ref46] 46. Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with {NumPy}. Nature. 2020;585: 357–362. pmid:32939066
View Article
PubMed/NCBI
Google Scholar

[158] View Article

[159] PubMed/NCBI

[160] Google Scholar

[ref47] 47. Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, et al. Biopython: Freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009;25: 1422–1423. pmid:19304878
View Article
PubMed/NCBI
Google Scholar

[162] View Article

[163] PubMed/NCBI

[164] Google Scholar

Figures

Abstract

Introduction

Results

QA accuracy and quality of scores

Peptides of length 20.

Full-length proteins.

Relationship between scoring function and scalability on quantum hardware.

Gate-based simulator results

Discussion

Methods

Codon optimization algorithm

Incorporating codon usage bias.

Optimize target GC concentration.

Minimize sequentially repeated nucleotides.

Additional constraints to the Hamiltonian.

Implementation of objective function.

Algorithm implementations

D-Wave Advantage 1.1.

Qiskit Qasm simulator.

Classic genetic algorithm.

Test applications.

Supporting information

S1 File. Contains additional information and derivations for GC-content derivation, BQM challenges and limitations, Genetic Algorithm Validation, S1-S4 Figs and the protein sequences used.

Acknowledgments

References