1
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Pal S, Bhattacharya M, Dash S, Lee SS, Chakraborty C. Future Potential of Quantum Computing and Simulations in Biological Science. Mol Biotechnol 2024; 66:2201-2218. [PMID: 37717248 DOI: 10.1007/s12033-023-00863-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 08/16/2023] [Indexed: 09/19/2023]
Abstract
The review article presents the recent progress in quantum computing and simulation within the field of biological sciences. The article is designed mainly into two portions: quantum computing and quantum simulation. In the first part, significant aspects of quantum computing was illustrated, such as quantum hardware, quantum RAM and big data, modern quantum processors, qubit, superposition effect in quantum computation, quantum interference, quantum entanglement, and quantum logic gates. Simultaneously, in the second part, vital features of the quantum simulation was illustrated, such as the quantum simulator, algorithms used in quantum simulations, and the use of quantum simulation in biological science. Finally, the review provides exceptional views to future researchers about different aspects of quantum simulation in biological science.
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Affiliation(s)
- Soumen Pal
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha, 756020, India
| | - Snehasish Dash
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do, 24252, Republic of Korea
| | - Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, 700126, India.
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2
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Yoshida Y, Takemori N, Mizukami W. Ab initio extended Hubbard model of short polyenes for efficient quantum computing. J Chem Phys 2024; 161:084303. [PMID: 39193941 DOI: 10.1063/5.0213525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 08/05/2024] [Indexed: 08/29/2024] Open
Abstract
We propose introducing an extended Hubbard Hamiltonian derived via the ab initio downfolding method, which was originally formulated for periodic materials, toward efficient quantum computing of molecular electronic structure calculations. By utilizing this method, the first-principles Hamiltonian of chemical systems can be coarse-grained by eliminating the electronic degrees of freedom in higher energy space and reducing the number of terms of electron repulsion integral from O(N4) to O(N2). Our approach is validated numerically on the vertical excitation energies and excitation characters of ethylene, butadiene, and hexatriene. The dynamical electron correlation is incorporated within the framework of the constrained random phase approximation in advance of quantum computations, and the constructed models capture the trend of experimental and high-level quantum chemical calculation results. As expected, the L1-norm of the fermion-to-qubit mapped model Hamiltonians is significantly lower than that of conventional ab initio Hamiltonians, suggesting improved scalability of quantum computing. Those numerical outcomes and the results of the simulation of excited-state sampling demonstrate that the ab initio extended Hubbard Hamiltonian may hold significant potential for quantum chemical calculations using quantum computers.
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Affiliation(s)
- Yuichiro Yoshida
- Center for Quantum Information and Quantum Biology, Osaka University, 1-2 Machikaneyama, Toyonaka, Osaka 560-0043, Japan
| | - Nayuta Takemori
- Center for Quantum Information and Quantum Biology, Osaka University, 1-2 Machikaneyama, Toyonaka, Osaka 560-0043, Japan
- Center for Emergent Matter Science, RIKEN, Wako, Saitama 351-0198, Japan
| | - Wataru Mizukami
- Center for Quantum Information and Quantum Biology, Osaka University, 1-2 Machikaneyama, Toyonaka, Osaka 560-0043, Japan
- Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan
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3
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Jeyaraman N, Jeyaraman M, Yadav S, Ramasubramanian S, Balaji S. Revolutionizing Healthcare: The Emerging Role of Quantum Computing in Enhancing Medical Technology and Treatment. Cureus 2024; 16:e67486. [PMID: 39310567 PMCID: PMC11416048 DOI: 10.7759/cureus.67486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/22/2024] [Indexed: 09/25/2024] Open
Abstract
The healthcare sector faces complex challenges that call for innovative solutions to improve diagnostic accuracy, treatment efficacy, and data management. Quantum computing, with its unique capabilities, holds the potential to revolutionize various aspects of healthcare. This narrative review critically examines the existing literature on the application of quantum computing in healthcare, focusing on its utility in enhancing diagnostics, data processing, and treatment planning. Quantum computing's ability to handle large, complex datasets more efficiently than classical computers can significantly impact domains such as genomics, medical imaging, and personalized medicine. Quantum algorithms can accelerate the identification of genetic markers associated with diseases, facilitate the analysis of medical images, and optimize treatment plans based on individual genetic profiles. Moreover, quantum cryptography offers a robust security solution for safeguarding sensitive patient data, a critical need as healthcare increasingly relies on digital platforms. Despite the promising outlook, the integration of quantum computing into healthcare faces technical, ethical, and regulatory challenges. The delicate nature of quantum hardware, the need for error correction, and the scalability of quantum systems pose barriers to widespread adoption. Additionally, concerns around patient privacy and data security, as well as the need for updated regulatory frameworks, must be addressed. Ongoing research and collaborative efforts involving researchers, healthcare providers, and technology developers are crucial to overcoming these hurdles and realizing the full potential of quantum computing in transforming healthcare. As quantum computing continues to evolve, its impact on the future of healthcare could be profound, leading to earlier disease detection, more personalized treatments, and improved patient outcomes. For instance, quantum computing has already been applied to enhance drug discovery processes, with companies like D-Wave Systems (Burnaby, Canada) demonstrating faster molecular simulations for pharmaceutical research and IBM's (Armonk, USA) quantum systems being used to model chemical reactions for new drug development.
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Affiliation(s)
- Naveen Jeyaraman
- Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai, IND
| | - Madhan Jeyaraman
- Orthopaedics, South Texas Orthopaedic Research Institute, Texas, USA
- Clinical Research Associate, Viriginia Tech India, Dr MGR Educational and Research Institute, Chennai, IND
- Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai, IND
| | - Sankalp Yadav
- Medicine, Shri Madan Lal Khurana Chest Clinic, New Delhi, IND
| | | | - Sangeetha Balaji
- Orthopaedics, Government Medical College, Omandurar Government Estate, Chennai, IND
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4
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Li Y, Cui X, Xiong Z, Liu B, Wang BY, Shu R, Qiao N, Yung MH. Quantum Molecular Docking with a Quantum-Inspired Algorithm. J Chem Theory Comput 2024. [PMID: 39073856 DOI: 10.1021/acs.jctc.4c00141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Molecular docking (MD) is a crucial task in drug design, which predicts the position, orientation, and conformation of the ligand when it is bound to a target protein. It can be interpreted as a combinatorial optimization problem, where quantum annealing (QA) has shown a promising advantage for solving combinatorial optimization. In this work, we propose a novel quantum molecular docking (QMD) approach based on a QA-inspired algorithm. We construct two binary encoding methods to efficiently discretize the degrees of freedom with an exponentially reduced number of bits and propose a smoothing filter to rescale the rugged objective function. We propose a new quantum-inspired algorithm, hopscotch simulated bifurcation (hSB), showing great advantages in optimizing over extremely rugged energy landscapes. This hSB can be applied to any formulation of an objective function under binary variables. An adaptive local continuous search is also introduced for further optimization of the discretized solution from hSB. Concerning the stability of docking, we propose a perturbation detection method to help rank the candidate poses. We demonstrate our approach on a typical data set. QMD has shown advantages over the search-based Autodock Vina and the deep-learning DIFFDOCK in both redocking and self-docking scenarios. These results indicate that quantum-inspired algorithms can be applied to solve practical problems in drug discovery even before quantum hardware become mature.
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Affiliation(s)
- Yunting Li
- Central Research Institute, Huawei Technologies, Shenzhen 518129, China
- State Key Laboratory of Surface Physics and Department of Physics, Fudan University, Shanghai 200433, China
| | - Xiaopeng Cui
- Central Research Institute, Huawei Technologies, Shenzhen 518129, China
| | - Zhaoping Xiong
- Laboratory of Health Intelligence, Huawei Cloud Computing Technologies Co., Ltd, Guizhou 550025, China
| | - Bowen Liu
- Central Research Institute, Huawei Technologies, Shenzhen 518129, China
| | - Bi-Ying Wang
- Central Research Institute, Huawei Technologies, Shenzhen 518129, China
| | - Runqiu Shu
- Central Research Institute, Huawei Technologies, Shenzhen 518129, China
| | - Nan Qiao
- Laboratory of Health Intelligence, Huawei Cloud Computing Technologies Co., Ltd, Guizhou 550025, China
| | - Man-Hong Yung
- Central Research Institute, Huawei Technologies, Shenzhen 518129, China
- Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- International Quantum Academy, Shenzhen 518048, China
- Guangdong Provincial Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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5
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Nałęcz-Charkiewicz K, Charkiewicz K, Nowak RM. Quantum computing in bioinformatics: a systematic review mapping. Brief Bioinform 2024; 25:bbae391. [PMID: 39140856 PMCID: PMC11323091 DOI: 10.1093/bib/bbae391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 06/05/2024] [Accepted: 07/26/2024] [Indexed: 08/15/2024] Open
Abstract
The field of quantum computing (QC) is expanding, with efforts being made to apply it to areas previously covered by classical algorithms and methods. Bioinformatics is one such domain that is developing in terms of QC. This article offers a broad mapping review of methods and algorithms of QC in bioinformatics, marking the first of its kind. It presents an overview of the domain and aids researchers in identifying further research directions in the early stages of this field of knowledge. The work presented here shows the current state-of-the-art solutions, focuses on general future directions, and highlights the limitations of current methods. The gathered data includes a comprehensive list of identified methods along with descriptions, classifications, and elaborations of their advantages and disadvantages. Results are presented not just in a descriptive table but also in an aggregated and visual format.
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Affiliation(s)
- Katarzyna Nałęcz-Charkiewicz
- Artificial Intelligence Division, Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
| | | | - Robert M Nowak
- Artificial Intelligence Division, Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
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6
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Li W, Yin Z, Li X, Ma D, Yi S, Zhang Z, Zou C, Bu K, Dai M, Yue J, Chen Y, Zhang X, Zhang S. A hybrid quantum computing pipeline for real world drug discovery. Sci Rep 2024; 14:16942. [PMID: 39043787 PMCID: PMC11266395 DOI: 10.1038/s41598-024-67897-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 07/17/2024] [Indexed: 07/25/2024] Open
Abstract
Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing a hybrid quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.
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Affiliation(s)
- Weitang Li
- Tencent Quantum Lab, Shenzhen, 518057, China
| | - Zhi Yin
- AceMapAI Biotechnology, Suzhou, 215000, China.
- School of Science, Ningbo University of Technology, Ningbo, 315211, China.
| | - Xiaoran Li
- AceMapAI Biotechnology, Suzhou, 215000, China
| | | | - Shuang Yi
- AceMapAI Biotechnology, Suzhou, 215000, China
| | | | - Chenji Zou
- Tencent Quantum Lab, Shenzhen, 518057, China
| | - Kunliang Bu
- Tencent Quantum Lab, Shenzhen, 518057, China
| | - Maochun Dai
- Tencent Quantum Lab, Shenzhen, 518057, China
| | - Jie Yue
- Tencent Quantum Lab, Shenzhen, 518057, China
| | - Yuzong Chen
- AceMapAI Joint Lab, China Pharmaceutical University, Nanjing, 211198, China
| | - Xiaojin Zhang
- AceMapAI Joint Lab, China Pharmaceutical University, Nanjing, 211198, China.
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7
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Schmidt B, Hildebrandt A. From GPUs to AI and quantum: three waves of acceleration in bioinformatics. Drug Discov Today 2024; 29:103990. [PMID: 38663581 DOI: 10.1016/j.drudis.2024.103990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/01/2024]
Abstract
The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel accelerators such as graphics processing units (GPUs). Consequently, the development of bioinformatics methods nowadays often heavily depends on the effective use of these powerful technologies. Furthermore, progress in computational techniques and architectures continues to be highly dynamic, involving novel deep neural network models and artificial intelligence (AI) accelerators, and potentially quantum processing units in the future. These are expected to be disruptive for the life sciences as a whole and for drug discovery in particular. Here, we identify three waves of acceleration and their applications in a bioinformatics context: (i) GPU computing, (ii) AI and (iii) next-generation quantum computers.
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Affiliation(s)
- Bertil Schmidt
- Institut für Informatik, Johannes Gutenberg University, Mainz, Germany.
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8
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Capone M, Romanelli M, Castaldo D, Parolin G, Bello A, Gil G, Vanzan M. A Vision for the Future of Multiscale Modeling. ACS PHYSICAL CHEMISTRY AU 2024; 4:202-225. [PMID: 38800726 PMCID: PMC11117712 DOI: 10.1021/acsphyschemau.3c00080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/31/2024] [Accepted: 02/01/2024] [Indexed: 05/29/2024]
Abstract
The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.
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Affiliation(s)
- Matteo Capone
- Department
of Physical and Chemical Sciences, University
of L’Aquila, L’Aquila 67010, Italy
| | - Marco Romanelli
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
| | - Davide Castaldo
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
| | - Giovanni Parolin
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
| | - Alessandro Bello
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
- Department
of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Gabriel Gil
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
- Instituto
de Cibernética, Matemática y Física (ICIMAF), La Habana 10400, Cuba
| | - Mirko Vanzan
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
- Department
of Physics, University of Milano, Milano 20133, Italy
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9
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Singh H, Majumder S, Mishra S. Hückel molecular orbital theory on a quantum computer: A scalable system-agnostic variational implementation with compact encoding. J Chem Phys 2024; 160:194106. [PMID: 38767256 DOI: 10.1063/5.0210597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 05/01/2024] [Indexed: 05/22/2024] Open
Abstract
Hückel molecular orbital (HMO) theory provides a semi-empirical treatment of the electronic structure in conjugated π-electronic systems. A scalable system-agnostic execution of HMO theory on a quantum computer is reported here based on a variational quantum deflation (VQD) algorithm for excited state quantum simulation. A compact encoding scheme is proposed here that provides an exponential advantage over the direct mapping and allows for quantum simulation of the HMO model for systems with up to 2n conjugated centers with n qubits. The transformation of the Hückel Hamiltonian to qubit space is achieved by two different strategies: an iterative refinement transformation and the Frobenius-inner-product-based transformation. These methods are tested on a series of linear, cyclic, and hetero-nuclear conjugated π-electronic systems. The molecular orbital energy levels and wavefunctions from the quantum simulation are in excellent agreement with the exact classical results. However, the higher excited states of large systems are found to suffer from error accumulation in the VQD simulation. This is mitigated by formulating a variant of VQD that exploits the symmetry of the Hamiltonian. This strategy has been successfully demonstrated for the quantum simulation of C60 fullerene containing 680 Pauli strings encoded on six qubits. The methods developed in this work are easily adaptable to similar problems of different complexity in other fields of research.
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Affiliation(s)
- Harshdeep Singh
- Center of Computational and Data Sciences, Indian Institute of Technology, Kharagpur, India
| | - Sonjoy Majumder
- Department of Physics, Indian Institute of Technology, Kharagpur, India
| | - Sabyashachi Mishra
- Department of Chemistry, Indian Institute of Technology, Kharagpur, India
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10
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Bowling PE, Dasgupta S, Herbert JM. Eliminating Imaginary Vibrational Frequencies in Quantum-Chemical Cluster Models of Enzymatic Active Sites. J Chem Inf Model 2024; 64:3912-3922. [PMID: 38648614 DOI: 10.1021/acs.jcim.4c00221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
In constructing finite models of enzyme active sites for quantum-chemical calculations, atoms at the periphery of the model must be constrained to prevent unphysical rearrangements during geometry relaxation. A simple fixed-atom or "coordinate-lock" approach is commonly employed but leads to undesirable artifacts in the form of small imaginary frequencies. These preclude evaluation of finite-temperature free-energy corrections, limiting thermochemical calculations to enthalpies only. Full-dimensional vibrational frequency calculations are possible by replacing the fixed-atom constraints with harmonic confining potentials. Here, we compare that approach to an alternative strategy in which fixed-atom contributions to the Hessian are simply omitted. While the latter strategy does eliminate imaginary frequencies, it tends to underestimate both the zero-point energy and the vibrational entropy while introducing artificial rigidity. Harmonic confining potentials eliminate imaginary frequencies and provide a flexible means to construct active-site models that can be used in unconstrained geometry relaxations, affording better convergence of reaction energies and barrier heights with respect to the model size, as compared to models with fixed-atom constraints.
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Affiliation(s)
- Paige E Bowling
- Biophysics Graduate Program, The Ohio State University, Columbus, Ohio 43210, United States
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States
| | - Saswata Dasgupta
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States
- Department of Chemistry and Biochemistry, University of California-San Diego, La Jolla, California 92093, United States
| | - John M Herbert
- Biophysics Graduate Program, The Ohio State University, Columbus, Ohio 43210, United States
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States
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11
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Ahmadi M, Dutta T, Mukherjee M. Scalable narrow linewidth high power laser for barium ion optical qubits. OPTICS EXPRESS 2024; 32:17879-17892. [PMID: 38858957 DOI: 10.1364/oe.520371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/13/2024] [Indexed: 06/12/2024]
Abstract
The linewidth of a laser plays a pivotal role in ensuring the high fidelity of ion trap quantum processors and optical clocks. As quantum computing endeavors scale up in qubit number, the demand for higher laser power with ultra-narrow linewidth becomes imperative, and leveraging fiber amplifiers emerges as a promising approach to meet these requirements. This study explores the effectiveness of thulium-doped fiber amplifiers (TDFAs) as a viable solution for addressing optical qubit transitions in trapped barium ion qubits. We demonstrate that by performing high-fidelity gates on the qubit while introducing minimal intensity noise, TDFAs do not significantly broaden the linewidth of the seed lasers. We employed a Voigt fitting scheme in conjunction with a delayed self-heterodyne method to accurately measure the linewidth independently, corroborating our findings through quadrupole spectroscopy with trapped barium ions. Our results show linewidth values of 160 ± 15 Hz and 156 ± 16 Hz, respectively, using these two methods, underscoring the reliability of our measurement techniques. The slight variation within the error-bars of the two methods can be attributed to factors such as amplified spontaneous emission in the TDFA or the influence of 1/f noise within the heterodyne setup delay line. These contribute to advancing our understanding of laser linewidth control in the context of ion trap quantum computing as well as stretching the availability of narrow linewidth, high-power tunable lasers beyond the C-band.
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12
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Medina-Franco JL, López-López E. What is the plausibility that all drugs will be designed by computers by the end of the decade? Expert Opin Drug Discov 2024; 19:507-510. [PMID: 38501288 DOI: 10.1080/17460441.2024.2331734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 03/13/2024] [Indexed: 03/20/2024]
Affiliation(s)
- José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico, Mexico
| | - Edgar López-López
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico, Mexico
- Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico, Mexico
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13
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Shang H, Wang F, Fan Y, Ma H, Liu Q, Guo C, Zhou P, Chen Q, Xiao Q, Zheng T, Li B, Zuo F, Liu J, Li Z, Yang J. Large-scale quantum emulating simulations of biomolecules: A pilot exploration of parallel quantum computing. Sci Bull (Beijing) 2024; 69:876-880. [PMID: 38290894 DOI: 10.1016/j.scib.2024.01.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/06/2023] [Accepted: 01/12/2024] [Indexed: 02/01/2024]
Affiliation(s)
- Honghui Shang
- Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China
| | - Fei Wang
- Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
| | - Yi Fan
- Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China
| | - Huan Ma
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230026, China
| | - Qi Liu
- National Supercomputing Center in Wuxi, Wuxi 214072, China
| | - Chu Guo
- Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China
| | - Pengyu Zhou
- Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China
| | - Qi Chen
- National Supercomputing Center in Wuxi, Wuxi 214072, China
| | - Qian Xiao
- School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Tianyu Zheng
- National Supercomputing Center in Wuxi, Wuxi 214072, China
| | - Bin Li
- National Supercomputing Center in Wuxi, Wuxi 214072, China
| | - Fen Zuo
- Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China
| | - Jie Liu
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230026, China.
| | - Zhenyu Li
- Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China
| | - Jinlong Yang
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230026, China.
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14
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Yoshida Y, Mizukami W, Yoshida N. Solvent Distribution Effects on Quantum Chemical Calculations with Quantum Computers. J Chem Theory Comput 2024; 20:1962-1971. [PMID: 38377035 DOI: 10.1021/acs.jctc.3c01189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
We present a combination of three-dimensional reference interaction site model self-consistent field (3D-RISM-SCF) theory and the variational quantum eigensolver (VQE) to consider the solvent distribution effects within the framework of quantum-classical hybrid computing. The present method, 3D-RISM-VQE, does not include any statistical errors from the solvent configuration sampling owing to the analytical treatment of the statistical solvent distribution. We apply 3D-RISM-VQE to compute the spatial distribution functions of solvent water around a water molecule, the potential and Helmholtz energy curves of NaCl, and to analyze the Helmholtz energy component and related properties of H2O and NH4+. Moreover, we utilize 3D-RISM-VQE to analyze the extent to which solvent effects alter the efficiency of quantum calculations compared with calculations in the gas phase using the L1-norms of molecular electronic Hamiltonians. Our results demonstrate that the efficiency of quantum chemical calculations on a quantum computer in solution is virtually the same as that in the gas phase.
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Affiliation(s)
- Yuichiro Yoshida
- Center for Quantum Information and Quantum Biology, Osaka University, 1-2 Machikaneyama, Toyonaka, Osaka 560-0043, Japan
| | - Wataru Mizukami
- Center for Quantum Information and Quantum Biology, Osaka University, 1-2 Machikaneyama, Toyonaka, Osaka 560-0043, Japan
- Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan
| | - Norio Yoshida
- Department of Chemistry, Graduate School of Science, Kyushu University, 744 Motooka, Nishiku, Fukuoka 819-0395, Japan
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ward, Nagoya 464-8601, Japan
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15
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Gao H, Imamura S, Kasagi A, Yoshida E. Distributed Implementation of Full Configuration Interaction for One Trillion Determinants. J Chem Theory Comput 2024; 20:1185-1192. [PMID: 38314701 PMCID: PMC10867839 DOI: 10.1021/acs.jctc.3c01190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/24/2023] [Accepted: 01/12/2024] [Indexed: 02/07/2024]
Abstract
Full configuration interaction (FCI) can provide an exact molecular ground-state energy within a given basis set and serve as a benchmark for approximate methods in quantum chemical calculations, including the emerging variational quantum eigensolver. However, its exponential computational and memory requirements easily exceed the capability of a single server and limit its applicability to large molecules. In this paper, we present a distributed FCI implementation employing a hybrid parallelization scheme with multithreading and multiprocessing to expand FCI's applicability. We optimize this scheme to minimize the bottlenecks arising from interprocess communications and interthread data management. Our implementation achieves higher scalability than the naive combination of prior works and successfully calculates the exact energy of C3H8/STO-3G with 1.31 trillion determinants, which is the largest FCI calculation to the best of our knowledge. Furthermore, we provide a comprehensive list of FCI results with 136 combinations of molecules and basis sets for future evaluation and development of approximate methods.
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Affiliation(s)
- Hong Gao
- Computing Laboratory, Fujitsu Laboratories, Fujitsu Limited, Kawasaki City, Kanagawa, 211-0053, Japan
| | - Satoshi Imamura
- Computing Laboratory, Fujitsu Laboratories, Fujitsu Limited, Kawasaki City, Kanagawa, 211-0053, Japan
| | - Akihiko Kasagi
- Computing Laboratory, Fujitsu Laboratories, Fujitsu Limited, Kawasaki City, Kanagawa, 211-0053, Japan
| | - Eiji Yoshida
- Computing Laboratory, Fujitsu Laboratories, Fujitsu Limited, Kawasaki City, Kanagawa, 211-0053, Japan
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16
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Pal S, Bhattacharya M, Lee SS, Chakraborty C. Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics. Mol Biotechnol 2024; 66:163-178. [PMID: 37244882 PMCID: PMC10224669 DOI: 10.1007/s12033-023-00765-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/04/2023] [Indexed: 05/29/2023]
Abstract
Modern biological science is trying to solve the fundamental complex problems of molecular biology, which include protein folding, drug discovery, simulation of macromolecular structure, genome assembly, and many more. Currently, quantum computing (QC), a rapidly emerging technology exploiting quantum mechanical phenomena, has developed to address current significant physical, chemical, biological issues, and complex questions. The present review discusses quantum computing technology and its status in solving molecular biology problems, especially in the next-generation computational biology scenario. First, the article explained the basic concept of quantum computing, the functioning of quantum systems where information is stored as qubits, and data storage capacity using quantum gates. Second, the review discussed quantum computing components, such as quantum hardware, quantum processors, and quantum annealing. At the same time, article also discussed quantum algorithms, such as the grover search algorithm and discrete and factorization algorithms. Furthermore, the article discussed the different applications of quantum computing to understand the next-generation biological problems, such as simulation and modeling of biological macromolecules, computational biology problems, data analysis in bioinformatics, protein folding, molecular biology problems, modeling of gene regulatory networks, drug discovery and development, mechano-biology, and RNA folding. Finally, the article represented different probable prospects of quantum computing in molecular biology.
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Affiliation(s)
- Soumen Pal
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha, 756020, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do, 24252, Republic of Korea
| | - Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, 700126, India.
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17
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Prasad VK, Cheng F, Fekl U, Jacobsen HA. Applications of noisy quantum computing and quantum error mitigation to "adamantaneland": a benchmarking study for quantum chemistry. Phys Chem Chem Phys 2024; 26:4071-4082. [PMID: 38225897 DOI: 10.1039/d3cp03523a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
The field of quantum computing has the potential to transform quantum chemistry. The variational quantum eigensolver (VQE) algorithm has allowed quantum computing to be applied to chemical problems in the noisy intermediate-scale quantum (NISQ) era. Applications of VQE have generally focused on predicting absolute energies instead of chemical properties that are relative energy differences and that are most interesting to chemists studying a chemical problem. We address this shortcoming by constructing a molecular benchmark data set in this work containing isomers of C10H16 and carbocationic rearrangements of C10H15+, calculated at a high-level of theory. Using the data set, we compared noiseless VQE simulations to conventionally performed density functional and wavefunction theory-based methods to understand the quality of results. We also investigated the effectiveness of a quantum state tomography-based error mitigation technique in applications of VQE under noise (simulated and real). Our findings reveal that the use of quantum error mitigation is crucial in the NISQ era and advantageous to yield almost noiseless quality results.
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Affiliation(s)
- Viki Kumar Prasad
- The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 Kings College Road, Toronto, Ontario, Canada, M5S 3G4. arno,
- Department of Chemical and Physical Sciences, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, Ontario, Canada, L5L 1C6.
| | - Freeman Cheng
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, Ontario, Canada, M5S 2E4
| | - Ulrich Fekl
- Department of Chemical and Physical Sciences, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, Ontario, Canada, L5L 1C6.
| | - Hans-Arno Jacobsen
- The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 Kings College Road, Toronto, Ontario, Canada, M5S 3G4. arno,
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, Ontario, Canada, M5S 2E4
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18
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Dixon TA, Walker RSK, Pretorius IS. Visioning synthetic futures for yeast research within the context of current global techno-political trends. Yeast 2023; 40:443-456. [PMID: 37653687 DOI: 10.1002/yea.3897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/07/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023] Open
Abstract
Yeast research is entering into a new period of scholarship, with new scientific tools, new questions to ask and new issues to consider. The politics of emerging and critical technology can no longer be separated from the pursuit of basic science in fields, such as synthetic biology and engineering biology. Given the intensifying race for technological leadership, yeast research is likely to attract significant investment from government, and that it offers huge opportunities to the curious minded from a basic research standpoint. This article provides an overview of new directions in yeast research with a focus on Saccharomyces cerevisiae, and places these trends in their geopolitical context. At the highest level, yeast research is situated within the ongoing convergence of the life sciences with the information sciences. This convergent effect is most strongly pronounced in areas of AI-enabled tools for the life sciences, and the creation of synthetic genomes, minimal genomes, pan-genomes, neochromosomes and metagenomes using computer-assisted design tools and methodologies. Synthetic yeast futures encompass basic and applied science questions that will be of intense interest to government and nongovernment funding sources. It is essential for the yeast research community to map and understand the context of their research to ensure their collaborations turn global challenges into research opportunities.
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Affiliation(s)
- Thomas A Dixon
- School of Social Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Roy S K Walker
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales, Australia
| | - Isak S Pretorius
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales, Australia
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19
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Maynard AD, Dudley SM. Navigating advanced technology transitions: using lessons from nanotechnology. NATURE NANOTECHNOLOGY 2023; 18:1118-1120. [PMID: 37783855 DOI: 10.1038/s41565-023-01481-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Affiliation(s)
- Andrew D Maynard
- School for the Future of Being Human, Arizona State University, Tempe, AZ, USA.
| | - Sean M Dudley
- Knowledge Enterprise, Arizona State University, Tempe, AZ, USA
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20
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Shi M, Zheng X, Zhou Y, Yin Y, Lu Z, Zou Z, Hu Y, Liang Y, Chen T, Yang Y, Jing M, Lei D, Yang P, Li X. Selectivity Mechanism of Pyrrolopyridone Analogues Targeting Bromodomain 2 of Bromodomain-Containing Protein 4 from Molecular Dynamics Simulations. ACS OMEGA 2023; 8:33658-33674. [PMID: 37744850 PMCID: PMC10515184 DOI: 10.1021/acsomega.3c03935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 08/25/2023] [Indexed: 09/26/2023]
Abstract
Bromodomain and extra-terminal domain (BET) proteins play an important role in epigenetic regulation and are linked to several diseases; therefore, they are interesting targets. BET has two bromodomains: bromodomain 1 (BD1) and BD2. Selective targeting of BD1 or BD2 may produce different activities and greater effects than pan-BD inhibitors. However, the selective mechanism of the specific core must be studied at the atomic level. This study determined the effectiveness of pyrrolopyridone analogues to selectively inhibit BD2 using a pan-BD inhibitor (ABBV-075) and a selective-BD2 inhibitor (ABBV-744). Molecular dynamics simulations and calculations of binding free energies were used to systematically study the selectivity of BD2 inhibition by the pyrrolopyridone analogues. Overall, the pyrrolopyridone analogue inhibitors targeting BD2 interacted mainly with the following amino acid pairs between bromodomain-containing protein 4 (BRD4)-BD1 and BRD4-BD2 complexes: I146/V439, N140/N433, D144/H437, P82/P375, V87/V380, D88/D381, and Y139/Y432. The pyrrolopyridone analogues targeting BRD4-BD2 were divided into five regions based on selectivity mechanism. These results suggest that the R3 and R5 regions of pyrrolopyridone analogues can be modified to improve the selectivity between BRD4-BD1 and BRD4-BD2. The selectivity of BD2 inhibition by pyrrolopyridone analogues can be used to design novel BD2 inhibitors based on a pyrrolopyridone core.
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Affiliation(s)
- Mingsong Shi
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
- Innovation
Center of Nursing Research, Nursing Key Laboratory of Sichuan Province,
West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Xueting Zheng
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Yan Zhou
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Yuan Yin
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Zhou Lu
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Zhiyan Zou
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Yan Hu
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Yuanyuan Liang
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Tingting Chen
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Yuhan Yang
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Meng Jing
- Department
of Pathology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of
China, Mianyang 621099, Sichuan, China
| | - Dan Lei
- School
of Life Science and Engineering, Southwest
University of Science and Technology, Mianyang 621010, Sichuan, China
| | - Pei Yang
- Department
of Hepatobiliary Surgery, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of
China, Mianyang 621099, Sichuan, China
| | - Xiaoan Li
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
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21
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Abstract
In attempts to simulate the protonation of proteins, a major challenge is that the number of protonation states grows rapidly as a function (2N) of the number of protonation sites (N). Expression on the free energy of the protonation state as an N-site Ising model ─ using an empirical Generalized-Born model ─ allows a quantum computer to efficiently determine the important states at a given pH value and subsequently reconstruct the pH titration process at all sites. Compared with the exact results painstakingly obtained with classical computers, the results obtained using quantum computers show good agreement for staphylococcal nuclease and excellent agreement for α-lactalbumin. This work illustrates the effectiveness of quantum computers in sampling important physical states, which may be useful in attacking challenging biomolecular problems.
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Affiliation(s)
- Hao Hu
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
- Polaris Quantum Biotech Inc., Suite 205, 201 W Main St., Durham, North Carolina 27701, United States
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22
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