Quantum mechanics for analyzing molecular biophysics
Abstract
Quantum mechanics and molecular biology are two very distinct fields. Quantum mechanics operates effectively in cryogenic temperatures, free from environmental noise. In contrast, many consider the hot, wet, and noisy environment of cells to be unsuitable for observing quantum effects. But at the same time, the bio-molecular interactions involve sub-atomic particles (e.g., protons and electrons) and therefore are fundamentally quantum. That is why, for an in-depth understanding of the basic cellular mechanisms (e.g., DNA replication, protein folding, mutations, etc.) at the molecular level, quantum mechanics is beneficial. This perspective discusses the recent advances in applying quantum mechanics to study molecular biophysics. The primary focus is on how principles of quantum mechanics have been utilized to improve biophysical tools (light microscopy, computing, and biosensing) necessary for investigating biomolecules and their interactions.
Keywords: Quantum mechanics, molecular biophysics, biomolecules, gene, mutations, microscopy, biosensing, quantum computing.
1. Introduction
In 1900, Max Planck's paper on the blackbody radiation problem blazed a new trail in physics. He proposed that radiation might not be continuous but discrete. These discrete packets are "quanta", a term coined by Max Planck. Five years later, Einstein introduced the photoelectric effect and 'photons', the discrete energy packets of light. A major contribution to this emerging field came with the introduction of Heisenberg's uncertainty principle and Schrödinger's wave mechanics theory. In 1935, Schrödinger presented a hypothetical cat experiment as an example of quantum superposition. Einstein later hypothesized that quantum mechanics is incomplete and termed entanglement "a spooky event at a distance".
Although molecular biology and quantum mechanics are seemingly different fields, the latter can still play a significant role in understanding biomolecular interactions. This was first addressed by Schrödinger in his book "What is Life? The Physical Aspect of the Living Cell." He considered genetic mutations to be the result of quantum jumps. A few years later, Watson and Crick proposed that tautomerisation of nucleic acid bases causes spontaneous mutations. Besides, many consider that the hot, noisy, and wet cellular environments are not suitable for quantum phenomena to happen.
Nevertheless, in the last few decades, several applications of quantum mechanics have been proposed in the area of biology. Quantum tunnelling (e.g., DNA mutation, enzyme catalysis, protein dynamics, voltage gated ion channels in non-targeted effects, cell communications in bystander effect), quantum coherence (e.g., photosynthesis, ion channel conductance, energy transfer in fluorescent proteins), entanglement (e.g., navigation of migratory birds), quantum interference (e.g., selectivity of ion channels) and superposition (e.g., ions in ion channel selectivity filter) are useful in describing many foundational biological processes. Quantum mechanics might also play an important role in maintaining the biological clock.
Molecular biophysics is an extremely interdisciplinary field that deals with the study of biomolecular interactions. This perspective explores the recent advances in applying the principles of quantum mechanics to study molecular biophysics. A summary of these prospective application areas is presented in Fig. 1. Additionally, the role of quantum mechanics in the development and improvement of key biophysical tools is also outlined.
2. Probing biomolecular interactions
Biomolecular interactions (e.g., DNA-RNA, DNA-protein, RNA-protein, protein-protein, etc.) are rudimentary building blocks of cellular mechanisms (e.g., cell signaling, transcription, replication, chromatin organization, etc.). Unmasking the physical principles governing these fundamental interactions is crucial to understanding disease progression. For example, spontaneous mutations are one of the main causes of many deadly diseases (e.g., sickle cell disease). Quantum tunnelling of protons (the hydrogen atom), i.e., charge transfer, can explain why spontaneous mutations occur. When pi orbitals of adjacent DNA bases overlap, quantum tunnelling mediates coherent charge transfer between bases. Another quantum phenomenon, chirality-induced spin selectivity, may also contribute to charge transfer across DNA bases.
The basis of life is how information is stored in the genes and transferred to subsequent generations. The unique structure (double helix structure, chiral formations, etc.) and composition of DNA might be the reason for its supposed quantum capabilities that can be used as an information storage, propagation, and transfer system. DNA utilizes entanglement to transfer information within its helical structure using spontaneous breakdown symmetry. The structure of DNA works as a quantum computer that translates the code of life. The electron-hole pair of the system is generated because of the quantized molecular vibrational energy. The nucleotide bases behave like a Josephson junction, and their condensation creates entangled states. AT, GC act as superconductors with Cooper-pair spin configurations, resulting in a system of two energy levels that form a qubit. In 2008, a paper by Ioannis G. Karafyllidis proposed a quantum mechanical model to study how information is transferred from DNA. He suggested two Hamiltonian matrices. One was to characterize DNA (the sender), and another encoded protein (the receiver). Codons represented the base states of the sender/DNA Hamiltonian matrix, which were the eigenstates. The eigenvalues of the receiver/protein Hamiltonian matrix were degenerate. The study hypothesized a model to study the central dogma using quantum computers. Intergenerational information transfer, i.e., inheritance of genes, might also be quantum in nature. Superposition and entanglement are central to heredity. Genes store quantum information of inheritance (the gene pool is divided into hard and soft genes; soft genes contribute to the bio-quantum genetics). A mathematical proof is established to support the hypothesis. It is proposed that the fetus performs a search through the bio-quantum set of family genes to select the subset of its parents' genes using a quantum mechanical database search (gene database based on family history).
Protein-DNA interactions are fundamental for the central dogma, as well as identifying key biomarkers involved in disease progression. Genetic machinery (e.g., enzymes during the DNA replication process) uses quantum entanglement of shared hydrogen bonds for efficient molecular recognition. A multilevel quantum unit 'Qdit' is supposed to increase biological data processing and even bioimaging capabilities, leading to finding new biomarkers and better diagnosis. The pi-pi entanglement between DNA base pairs and protein amino acids can increase the speed of the identification of the consensus DNA sequences by a protein. Coin Position Entanglement (CPE) is important for this pi-pi electron entanglement that is necessary for the quantum walk.
The main concern about quantum biology is that the biological environment is not suitable for observing the quantum mechanical properties of biomolecules. However, even at room temperature, entanglement happens between the electron clouds in DNA. DNA acts like a quantum harmonic oscillator, and a nucleic acid base contains information about its neighboring bases. Decoherence is the loss of quantum coherence and can happen at room temperature. To understand the mechanism of simultaneous cutting of DNA double strands by endonucleases, a research work proposes a quantum mechanical model where the enzyme can act as a decoherence shield and establishes a boundary conditions under which zero-point modes of coherent oscillations provide the necessary energy for strand breakage. The entangled electron pair between DNA and enzymes (through dipole-dipole interactions) coordinates at different catalytic sites.
Protein folding is a complex and multi-step process. Quantum folding is supposed to be responsible for contributing to faster protein folding than the classical random walk. Besides, quantum mechanics is implied to play a role in stabilizing the protein-protein and protein-water interactions.
3. Advancement of the biophysical tools
Molecular biophysics often involves studying biomolecules in cells, tissues, and/or living organisms (such as model organisms) to decipher the mechanisms of action of biomolecules, especially those involved in the progression of fatal diseases. Therefore, it is essential to have high-quality techniques (imaging, sensing, computing, etc.) to analyze biological data with high sensitivity and resolution. In the following sections, the application of quantum mechanics to improve the most important biophysical tools is discussed.
3.1. Quantum light microscopy
Light microscopy is an invaluable tool for observing cellular dynamics. Out of all light microscopy methods, fluorescence microscopy is the commonly used technique to study molecular biology, and at the advent of the new millennium, super-resolution microscopy techniques paved the way for high-resolution biological imaging.
In recent years, quantum entanglement has been applied to increase the signal-to-noise ratio of microscopy techniques. Traditional microscopy techniques mostly rely on linear optics. But, they are heavily dependent on the use of high-intensity lasers, which create photo damage or alteration of biological samples. Non-linear optics using entangled photons can be used to increase the sensitivity of a microscope at low light levels. SPAD (single-photon avalanche diode array) cameras, quantum-enhanced phase imagers, SPDC (spontaneous parametric down conversion) induced hyperentangled photon pairs, etc., have contributed to the advancement of a quantum-enhanced phase imaging method to be used in studying photosensitive biomedical samples with a higher signal-to-noise ratio (SNR). Spatially and polarization-entangled photon pairs can increase the SNR of quantum imaging of biological organisms (e.g., whole zebrafish and mouse brain). Two β-barium-borate (BBO) non-linear crystals are used to produce hyper-entangled photon pairs by SPDC. Apart from higher SNR, the introduced method, ICE (quantum imaging by coincidence from entanglement), has higher resolvable pixel counts and full quantification of birefringence. Undetected photons using non-linear crystals extract more information from samples, and it is implied that this quantum imaging technique can also be utilized to study biological samples.
Besides improving the signal-to-noise ratio, entangled photon pairs increase the resolution and acquisition speed of microscopy techniques. The quantum interference (photon interference created by parametric down conversion) based method holds the potential to improve the spatial resolution of an imaging technique that uses undetected photon pairs/entangled photon pairs. This method is also applied for biological imaging.
The infrared (IR) region of light spectra is pivotal, as this range can be used for label-free biological imaging. Non-linear interferometry using entangled photons can perform biological imaging under low light illumination in the mid-IR range.
3.2. Quantum computing for biophysics
The emerging field of quantum computing, including both quantum gates and quantum annealing, has several advantages in the field of biology. The primary advantage is the speed of computation and information storage capacity, which is crucial for many biological discoveries. Parallelism, because of the properties of quantum mechanics (e.g., superposition, entanglement), is being proven to be extremely useful for solving complex biological problems. Quantum computing can speed up drug discovery, gene analysis, RNA folding and protein structure prediction, phylogenetic analysis, DNA-transcription factor interaction analysis, etc.
DNA mutations cause significant changes in cell activity. Even a single nucleotide change in the DNA sequence can alter the genomic expression significantly. That is why DNA sequencing is an important technique in disease diagnosis. But the process requires lots of time due to the speed limitation of classical computers. Here comes the advantage of quantum computing. A group of researchers from Japan utilized the speed of quantum computing in sequencing DNA to identify nucleotide bases from single molecules. They used the difference in conduction path over time (because the electron conductance depends on the chemical architecture of the molecule) to identify the bases. Another research utilized FRQI (Flexible Representation of Quantum Images) framework to find differences in gene/amino acid sequences by comparing different phase angles between two sequences. They reported a fast technique that consumed much less memory than classical computers to do the sequence analysis. A study by Nalecz-Charkiewicz et. al. used a hybrid system consisting of a quantum annealer and CPU to find overlapping regions in both actual (e.g., lambda phage, SARS COVID-19, E.coli DNA sequences) and simulated gene sequences using the Pearson correlation coefficient. Similar research on the application of quantum annealers for genome assembly of ϕX174 bacteriophage was reported by A.S. Boev et. al. Quantum annealers are also helpful in studying DNA-transcription factor interactions. An algorithm framework based on Estimating Algorithmic Information Theory (EAIT) using a quantum accelerator can be used for studying protein-protein interactions, genomic data analysis, drug discovery, etc. QuASeR, a de novo algorithm for DNA sequence reconstruction, is used on both gate-based quantum systems and quantum annealers. Reference-guided DNA sequence alignment, an algorithm based on Simon's quantum algorithm, using gate-based quantum computing, is introduced for reducing computational errors in gene analysis. Boolean networks are used to model cellular networks and to study the dynamics of biological pathways. The Boolean network can then be transferred to a quantum circuit to assess all possible variables of a biological network (e.g., cortical area development in mammals) at the same time. A research developed a quantum classifier for application in the field of genomics (e.g., to label normal and diseased samples to study DNA copy number variation). qscGRN is a quantum gene regulatory network method that utilizes qubit entanglement for modelling interactions between genes from single-cell transcriptomics data.
Protein folding study gained a massive boost from the application of AI in recent years. But still, to understand the complex dynamic protein folding process, we need the computational prowess of quantum computers. A recent study proposed a quantum annealing approach to solve protein lattice problems. Graph Neural Networks (GNNs) are used extensively for predicting protein structures. But they hardly give importance to the information at the edges. XENet - a modified GNN architecture, takes the edge attributes into consideration for predicting protein interactions. XENet is useful for designing large proteins on quantum computers with low qubits. The structure of the P-loop in Zika virus NS3 helicase was predicted using hybrid classical-quantum systems, where quantum algorithms were utilized only at the computationally intensive part (e.g., predicting a coarse-grain model of the lowest energy conformation) and were performed on an IBM quantum computer.
3.3. Quantum biosensing
Quantum systems are fragile. As soon as they interact with the environment, they become decoherent and their superposition states collapse. But this very fragile nature of the quantum systems can be used to sense changes in biological molecules (e.g., structural changes in proteins, neuron action potential, etc.). Quantum optical technologies have been applied to laboratories on chip-based devices for single-molecule biosensing (e.g., protein folding). Entangled photons, squeezed states, N00N states, and NV (nitrogen vacancy) diamonds have been applied for enhanced phase measurements of neuronal action potential and protein concentration measurement. NV centers in diamonds are especially promising for biosensing applications.
In 2021, a mathematical simulation-based study revealed that hybrid quantum biosensors, composed of nitrogen vacancy centers in nanodiamonds, can detect SARS-CoV-2 RNA with increased sensitivity. NV diamond-based NMR (Nuclear Magnetic Resonance) can also be used to detect single proteins for studying their structure and function. Quantum diamond technology-based magnetic imaging detects magnetically labelled IL-6 (Interleukin-6) from COVID-19 patients. NV nanodiamonds sense the presence of ferritin molecules (metalloprotein) by checking the decreasing coherence time. The defects in diamonds can be used to measure the presence of free radicals using the T1 relaxometry technique. First, confocal z-scans of cells are taken, followed by identifying a target particle, and then the T1 relaxometry is performed, which measures the decrease in relaxation time (T1) because of the presence of spin noise coming from free radicals. T1 relaxometry can also be used for measuring free radicals in primary dendritic cells.
4. Conclusion
Biology is still a mystery, mostly. We still do not know the mechanism of action of many diseases. Understanding those diseases at the molecular level is an important criterion for eradicating them. Biological processes work at physiological temperature. Therefore mainly uses the principles of classical physics. Nevertheless, recently, many researchers have presented studies that argue quantum mechanics might be playing a role in maintaining fundamental biomolecular interactions. But many of these studies remain largely theoretical.
To date, quantum mechanics has been used to describe the underlying principles of DNA mechanics, gene inheritance, epigenetics, protein folding, etc. Disruption of quantum properties can be useful to sense subtle changes in cells because of external stimuli, leading to better wearable sensors and improving digital health technology. Many problems in biology, especially genome assembly after sequencing, are part of NP-hard type problems. Quantum computing and quantum algorithms can accelerate this type of biological problem solving (i.e., genome assembly). Therefore, the improvement in quantum computer hardware will accelerate the process of finding solutions to complex and dynamic biological problems.
The field of quantum biophysics is in its infancy. The applications of quantum mechanics in studying molecular biology are limited. The primary constraint may be the lack of collaboration among biologists, physicists, and computer scientists, which is necessary to generate more experimental evidence. Life scientists are heavily dependent on other fields in terms of tools to investigate the biological world. Advances in physics (e.g., super-resolution microscopy), chemistry (e.g., novel biomarkers), and computer science (e.g., AI tools like AlphaFold) have been proven to be extremely essential for biological discoveries. Quantum tools have the potential to start the next revolution in biological science, to improve our lives. But, first, it needs to cross the tunnel of knowledge barrier between physics and biology.
Abstract
Quantum mechanics and molecular biology are two very distinct fields. Quantum mechanics operates effectively in cryogenic temperatures, free from environmental noise. In contrast, many consider the hot, wet, and noisy environment of cells to be unsuitable for observing quantum effects. But at the same time, the bio-molecular interactions involve sub-atomic particles (e.g., protons and electrons) and therefore are fundamentally quantum. That is why, for an in-depth understanding of the basic cellular mechanisms (e.g., DNA replication, protein folding, mutations, etc.) at the molecular level, quantum mechanics is beneficial. This perspective discusses the recent advances in applying quantum mechanics to study molecular biophysics. The primary focus is on how principles of quantum mechanics have been utilized to improve biophysical tools (light microscopy, computing, and biosensing) necessary for investigating biomolecules and their interactions.
Keywords: Quantum mechanics, molecular biophysics, biomolecules, gene, mutations, microscopy, biosensing, quantum computing.
1. Introduction
In 1900, Max Planck's paper on the blackbody radiation problem blazed a new trail in physics. He proposed that radiation might not be continuous but discrete. These discrete packets are "quanta", a term coined by Max Planck. Five years later, Einstein introduced the photoelectric effect and 'photons', the discrete energy packets of light. A major contribution to this emerging field came with the introduction of Heisenberg's uncertainty principle and Schrödinger's wave mechanics theory. In 1935, Schrödinger presented a hypothetical cat experiment as an example of quantum superposition. Einstein later hypothesized that quantum mechanics is incomplete and termed entanglement "a spooky event at a distance".
Although molecular biology and quantum mechanics are seemingly different fields, the latter can still play a significant role in understanding biomolecular interactions. This was first addressed by Schrödinger in his book "What is Life? The Physical Aspect of the Living Cell." He considered genetic mutations to be the result of quantum jumps. A few years later, Watson and Crick proposed that tautomerisation of nucleic acid bases causes spontaneous mutations. Besides, many consider that the hot, noisy, and wet cellular environments are not suitable for quantum phenomena to happen.
Nevertheless, in the last few decades, several applications of quantum mechanics have been proposed in the area of biology. Quantum tunnelling (e.g., DNA mutation, enzyme catalysis, protein dynamics, voltage gated ion channels in non-targeted effects, cell communications in bystander effect), quantum coherence (e.g., photosynthesis, ion channel conductance, energy transfer in fluorescent proteins), entanglement (e.g., navigation of migratory birds), quantum interference (e.g., selectivity of ion channels) and superposition (e.g., ions in ion channel selectivity filter) are useful in describing many foundational biological processes. Quantum mechanics might also play an important role in maintaining the biological clock.
Molecular biophysics is an extremely interdisciplinary field that deals with the study of biomolecular interactions. This perspective explores the recent advances in applying the principles of quantum mechanics to study molecular biophysics. A summary of these prospective application areas is presented in Fig. 1. Additionally, the role of quantum mechanics in the development and improvement of key biophysical tools is also outlined.
2. Probing biomolecular interactions
Biomolecular interactions (e.g., DNA-RNA, DNA-protein, RNA-protein, protein-protein, etc.) are rudimentary building blocks of cellular mechanisms (e.g., cell signaling, transcription, replication, chromatin organization, etc.). Unmasking the physical principles governing these fundamental interactions is crucial to understanding disease progression. For example, spontaneous mutations are one of the main causes of many deadly diseases (e.g., sickle cell disease). Quantum tunnelling of protons (the hydrogen atom), i.e., charge transfer, can explain why spontaneous mutations occur. When pi orbitals of adjacent DNA bases overlap, quantum tunnelling mediates coherent charge transfer between bases. Another quantum phenomenon, chirality-induced spin selectivity, may also contribute to charge transfer across DNA bases.
The basis of life is how information is stored in the genes and transferred to subsequent generations. The unique structure (double helix structure, chiral formations, etc.) and composition of DNA might be the reason for its supposed quantum capabilities that can be used as an information storage, propagation, and transfer system. DNA utilizes entanglement to transfer information within its helical structure using spontaneous breakdown symmetry. The structure of DNA works as a quantum computer that translates the code of life. The electron-hole pair of the system is generated because of the quantized molecular vibrational energy. The nucleotide bases behave like a Josephson junction, and their condensation creates entangled states. AT, GC act as superconductors with Cooper-pair spin configurations, resulting in a system of two energy levels that form a qubit. In 2008, a paper by Ioannis G. Karafyllidis proposed a quantum mechanical model to study how information is transferred from DNA. He suggested two Hamiltonian matrices. One was to characterize DNA (the sender), and another encoded protein (the receiver). Codons represented the base states of the sender/DNA Hamiltonian matrix, which were the eigenstates. The eigenvalues of the receiver/protein Hamiltonian matrix were degenerate. The study hypothesized a model to study the central dogma using quantum computers. Intergenerational information transfer, i.e., inheritance of genes, might also be quantum in nature. Superposition and entanglement are central to heredity. Genes store quantum information of inheritance (the gene pool is divided into hard and soft genes; soft genes contribute to the bio-quantum genetics). A mathematical proof is established to support the hypothesis. It is proposed that the fetus performs a search through the bio-quantum set of family genes to select the subset of its parents' genes using a quantum mechanical database search (gene database based on family history).
Protein-DNA interactions are fundamental for the central dogma, as well as identifying key biomarkers involved in disease progression. Genetic machinery (e.g., enzymes during the DNA replication process) uses quantum entanglement of shared hydrogen bonds for efficient molecular recognition. A multilevel quantum unit 'Qdit' is supposed to increase biological data processing and even bioimaging capabilities, leading to finding new biomarkers and better diagnosis. The pi-pi entanglement between DNA base pairs and protein amino acids can increase the speed of the identification of the consensus DNA sequences by a protein. Coin Position Entanglement (CPE) is important for this pi-pi electron entanglement that is necessary for the quantum walk.
The main concern about quantum biology is that the biological environment is not suitable for observing the quantum mechanical properties of biomolecules. However, even at room temperature, entanglement happens between the electron clouds in DNA. DNA acts like a quantum harmonic oscillator, and a nucleic acid base contains information about its neighboring bases. Decoherence is the loss of quantum coherence and can happen at room temperature. To understand the mechanism of simultaneous cutting of DNA double strands by endonucleases, a research work proposes a quantum mechanical model where the enzyme can act as a decoherence shield and establishes a boundary conditions under which zero-point modes of coherent oscillations provide the necessary energy for strand breakage. The entangled electron pair between DNA and enzymes (through dipole-dipole interactions) coordinates at different catalytic sites.
Protein folding is a complex and multi-step process. Quantum folding is supposed to be responsible for contributing to faster protein folding than the classical random walk. Besides, quantum mechanics is implied to play a role in stabilizing the protein-protein and protein-water interactions.
3. Advancement of the biophysical tools
Molecular biophysics often involves studying biomolecules in cells, tissues, and/or living organisms (such as model organisms) to decipher the mechanisms of action of biomolecules, especially those involved in the progression of fatal diseases. Therefore, it is essential to have high-quality techniques (imaging, sensing, computing, etc.) to analyze biological data with high sensitivity and resolution. In the following sections, the application of quantum mechanics to improve the most important biophysical tools is discussed.
3.1. Quantum light microscopy
Light microscopy is an invaluable tool for observing cellular dynamics. Out of all light microscopy methods, fluorescence microscopy is the commonly used technique to study molecular biology, and at the advent of the new millennium, super-resolution microscopy techniques paved the way for high-resolution biological imaging.
In recent years, quantum entanglement has been applied to increase the signal-to-noise ratio of microscopy techniques. Traditional microscopy techniques mostly rely on linear optics. But, they are heavily dependent on the use of high-intensity lasers, which create photo damage or alteration of biological samples. Non-linear optics using entangled photons can be used to increase the sensitivity of a microscope at low light levels. SPAD (single-photon avalanche diode array) cameras, quantum-enhanced phase imagers, SPDC (spontaneous parametric down conversion) induced hyperentangled photon pairs, etc., have contributed to the advancement of a quantum-enhanced phase imaging method to be used in studying photosensitive biomedical samples with a higher signal-to-noise ratio (SNR). Spatially and polarization-entangled photon pairs can increase the SNR of quantum imaging of biological organisms (e.g., whole zebrafish and mouse brain). Two β-barium-borate (BBO) non-linear crystals are used to produce hyper-entangled photon pairs by SPDC. Apart from higher SNR, the introduced method, ICE (quantum imaging by coincidence from entanglement), has higher resolvable pixel counts and full quantification of birefringence. Undetected photons using non-linear crystals extract more information from samples, and it is implied that this quantum imaging technique can also be utilized to study biological samples.
Besides improving the signal-to-noise ratio, entangled photon pairs increase the resolution and acquisition speed of microscopy techniques. The quantum interference (photon interference created by parametric down conversion) based method holds the potential to improve the spatial resolution of an imaging technique that uses undetected photon pairs/entangled photon pairs. This method is also applied for biological imaging.
The infrared (IR) region of light spectra is pivotal, as this range can be used for label-free biological imaging. Non-linear interferometry using entangled photons can perform biological imaging under low light illumination in the mid-IR range.
3.2. Quantum computing for biophysics
The emerging field of quantum computing, including both quantum gates and quantum annealing, has several advantages in the field of biology. The primary advantage is the speed of computation and information storage capacity, which is crucial for many biological discoveries. Parallelism, because of the properties of quantum mechanics (e.g., superposition, entanglement), is being proven to be extremely useful for solving complex biological problems. Quantum computing can speed up drug discovery, gene analysis, RNA folding and protein structure prediction, phylogenetic analysis, DNA-transcription factor interaction analysis, etc.
DNA mutations cause significant changes in cell activity. Even a single nucleotide change in the DNA sequence can alter the genomic expression significantly. That is why DNA sequencing is an important technique in disease diagnosis. But the process requires lots of time due to the speed limitation of classical computers. Here comes the advantage of quantum computing. A group of researchers from Japan utilized the speed of quantum computing in sequencing DNA to identify nucleotide bases from single molecules. They used the difference in conduction path over time (because the electron conductance depends on the chemical architecture of the molecule) to identify the bases. Another research utilized FRQI (Flexible Representation of Quantum Images) framework to find differences in gene/amino acid sequences by comparing different phase angles between two sequences. They reported a fast technique that consumed much less memory than classical computers to do the sequence analysis. A study by Nalecz-Charkiewicz et. al. used a hybrid system consisting of a quantum annealer and CPU to find overlapping regions in both actual (e.g., lambda phage, SARS COVID-19, E.coli DNA sequences) and simulated gene sequences using the Pearson correlation coefficient. Similar research on the application of quantum annealers for genome assembly of ϕX174 bacteriophage was reported by A.S. Boev et. al. Quantum annealers are also helpful in studying DNA-transcription factor interactions. An algorithm framework based on Estimating Algorithmic Information Theory (EAIT) using a quantum accelerator can be used for studying protein-protein interactions, genomic data analysis, drug discovery, etc. QuASeR, a de novo algorithm for DNA sequence reconstruction, is used on both gate-based quantum systems and quantum annealers. Reference-guided DNA sequence alignment, an algorithm based on Simon's quantum algorithm, using gate-based quantum computing, is introduced for reducing computational errors in gene analysis. Boolean networks are used to model cellular networks and to study the dynamics of biological pathways. The Boolean network can then be transferred to a quantum circuit to assess all possible variables of a biological network (e.g., cortical area development in mammals) at the same time. A research developed a quantum classifier for application in the field of genomics (e.g., to label normal and diseased samples to study DNA copy number variation). qscGRN is a quantum gene regulatory network method that utilizes qubit entanglement for modelling interactions between genes from single-cell transcriptomics data.
Protein folding study gained a massive boost from the application of AI in recent years. But still, to understand the complex dynamic protein folding process, we need the computational prowess of quantum computers. A recent study proposed a quantum annealing approach to solve protein lattice problems. Graph Neural Networks (GNNs) are used extensively for predicting protein structures. But they hardly give importance to the information at the edges. XENet - a modified GNN architecture, takes the edge attributes into consideration for predicting protein interactions. XENet is useful for designing large proteins on quantum computers with low qubits. The structure of the P-loop in Zika virus NS3 helicase was predicted using hybrid classical-quantum systems, where quantum algorithms were utilized only at the computationally intensive part (e.g., predicting a coarse-grain model of the lowest energy conformation) and were performed on an IBM quantum computer.
3.3. Quantum biosensing
Quantum systems are fragile. As soon as they interact with the environment, they become decoherent and their superposition states collapse. But this very fragile nature of the quantum systems can be used to sense changes in biological molecules (e.g., structural changes in proteins, neuron action potential, etc.). Quantum optical technologies have been applied to laboratories on chip-based devices for single-molecule biosensing (e.g., protein folding). Entangled photons, squeezed states, N00N states, and NV (nitrogen vacancy) diamonds have been applied for enhanced phase measurements of neuronal action potential and protein concentration measurement. NV centers in diamonds are especially promising for biosensing applications.
In 2021, a mathematical simulation-based study revealed that hybrid quantum biosensors, composed of nitrogen vacancy centers in nanodiamonds, can detect SARS-CoV-2 RNA with increased sensitivity. NV diamond-based NMR (Nuclear Magnetic Resonance) can also be used to detect single proteins for studying their structure and function. Quantum diamond technology-based magnetic imaging detects magnetically labelled IL-6 (Interleukin-6) from COVID-19 patients. NV nanodiamonds sense the presence of ferritin molecules (metalloprotein) by checking the decreasing coherence time. The defects in diamonds can be used to measure the presence of free radicals using the T1 relaxometry technique. First, confocal z-scans of cells are taken, followed by identifying a target particle, and then the T1 relaxometry is performed, which measures the decrease in relaxation time (T1) because of the presence of spin noise coming from free radicals. T1 relaxometry can also be used for measuring free radicals in primary dendritic cells.
4. Conclusion
Biology is still a mystery, mostly. We still do not know the mechanism of action of many diseases. Understanding those diseases at the molecular level is an important criterion for eradicating them. Biological processes work at physiological temperature. Therefore mainly uses the principles of classical physics. Nevertheless, recently, many researchers have presented studies that argue quantum mechanics might be playing a role in maintaining fundamental biomolecular interactions. But many of these studies remain largely theoretical.
To date, quantum mechanics has been used to describe the underlying principles of DNA mechanics, gene inheritance, epigenetics, protein folding, etc. Disruption of quantum properties can be useful to sense subtle changes in cells because of external stimuli, leading to better wearable sensors and improving digital health technology. Many problems in biology, especially genome assembly after sequencing, are part of NP-hard type problems. Quantum computing and quantum algorithms can accelerate this type of biological problem solving (i.e., genome assembly). Therefore, the improvement in quantum computer hardware will accelerate the process of finding solutions to complex and dynamic biological problems.
The field of quantum biophysics is in its infancy. The applications of quantum mechanics in studying molecular biology are limited. The primary constraint may be the lack of collaboration among biologists, physicists, and computer scientists, which is necessary to generate more experimental evidence. Life scientists are heavily dependent on other fields in terms of tools to investigate the biological world. Advances in physics (e.g., super-resolution microscopy), chemistry (e.g., novel biomarkers), and computer science (e.g., AI tools like AlphaFold) have been proven to be extremely essential for biological discoveries. Quantum tools have the potential to start the next revolution in biological science, to improve our lives. But, first, it needs to cross the tunnel of knowledge barrier between physics and biology.