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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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2
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Gonçalves AAM, Ribeiro AJ, Resende CAA, Couto CAP, Gandra IB, Dos Santos Barcelos IC, da Silva JO, Machado JM, Silva KA, Silva LS, Dos Santos M, da Silva Lopes L, de Faria MT, Pereira SP, Xavier SR, Aragão MM, Candida-Puma MA, de Oliveira ICM, Souza AA, Nogueira LM, da Paz MC, Coelho EAF, Giunchetti RC, de Freitas SM, Chávez-Fumagalli MA, Nagem RAP, Galdino AS. Recombinant multiepitope proteins expressed in Escherichia coli cells and their potential for immunodiagnosis. Microb Cell Fact 2024; 23:145. [PMID: 38778337 PMCID: PMC11110257 DOI: 10.1186/s12934-024-02418-w] [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: 01/31/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024] Open
Abstract
Recombinant multiepitope proteins (RMPs) are a promising alternative for application in diagnostic tests and, given their wide application in the most diverse diseases, this review article aims to survey the use of these antigens for diagnosis, as well as discuss the main points surrounding these antigens. RMPs usually consisting of linear, immunodominant, and phylogenetically conserved epitopes, has been applied in the experimental diagnosis of various human and animal diseases, such as leishmaniasis, brucellosis, cysticercosis, Chagas disease, hepatitis, leptospirosis, leprosy, filariasis, schistosomiasis, dengue, and COVID-19. The synthetic genes for these epitopes are joined to code a single RMP, either with spacers or fused, with different biochemical properties. The epitopes' high density within the RMPs contributes to a high degree of sensitivity and specificity. The RMPs can also sidestep the need for multiple peptide synthesis or multiple recombinant proteins, reducing costs and enhancing the standardization conditions for immunoassays. Methods such as bioinformatics and circular dichroism have been widely applied in the development of new RMPs, helping to guide their construction and better understand their structure. Several RMPs have been expressed, mainly using the Escherichia coli expression system, highlighting the importance of these cells in the biotechnological field. In fact, technological advances in this area, offering a wide range of different strains to be used, make these cells the most widely used expression platform. RMPs have been experimentally used to diagnose a broad range of illnesses in the laboratory, suggesting they could also be useful for accurate diagnoses commercially. On this point, the RMP method offers a tempting substitute for the production of promising antigens used to assemble commercial diagnostic kits.
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Affiliation(s)
- Ana Alice Maia Gonçalves
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Anna Julia Ribeiro
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Carlos Ananias Aparecido Resende
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Carolina Alves Petit Couto
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Isadora Braga Gandra
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Isabelle Caroline Dos Santos Barcelos
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Jonatas Oliveira da Silva
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Juliana Martins Machado
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Kamila Alves Silva
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Líria Souza Silva
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Michelli Dos Santos
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Lucas da Silva Lopes
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Mariana Teixeira de Faria
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Sabrina Paula Pereira
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Sandra Rodrigues Xavier
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Matheus Motta Aragão
- Department of Biochemistry and Immunology, Federal University of Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Mayron Antonio Candida-Puma
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa, 04000, Peru
| | | | - Amanda Araujo Souza
- Biophysics Laboratory, Institute of Biological Sciences, Department of Cell Biology, University of Brasilia, Brasília, 70910-900, Brazil
| | - Lais Moreira Nogueira
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Mariana Campos da Paz
- Bioactives and Nanobiotechnology Laboratory, Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Eduardo Antônio Ferraz Coelho
- Postgraduate Program in Health Sciences, Infectious Diseases and Tropical Medicine, Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, 30130-100, Brazil
| | - Rodolfo Cordeiro Giunchetti
- Laboratory of Biology of Cell Interactions, National Institute of Science and Technology on Tropical Diseases (INCT-DT), Department of Morphology, Federal University of Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Sonia Maria de Freitas
- Biophysics Laboratory, Institute of Biological Sciences, Department of Cell Biology, University of Brasilia, Brasília, 70910-900, Brazil
| | - Miguel Angel Chávez-Fumagalli
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa, 04000, Peru
| | - Ronaldo Alves Pinto Nagem
- Department of Biochemistry and Immunology, Federal University of Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Alexsandro Sobreira Galdino
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil.
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Kim YA, Mousavi K, Yazdi A, Zwierzyna M, Cardinali M, Fox D, Peel T, Coller J, Aggarwal K, Maruggi G. Computational design of mRNA vaccines. Vaccine 2024; 42:1831-1840. [PMID: 37479613 DOI: 10.1016/j.vaccine.2023.07.024] [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: 03/31/2023] [Revised: 06/23/2023] [Accepted: 07/10/2023] [Indexed: 07/23/2023]
Abstract
mRNA technology has emerged as a successful vaccine platform that offered a swift response to the COVID-19 pandemic. Accumulating evidence shows that vaccine efficacy, thermostability, and other important properties, are largely impacted by intrinsic properties of the mRNA molecule, such as RNA sequence and structure, both of which can be optimized. Designing mRNA sequence for vaccines presents a combinatorial problem due to an extremely large selection space. For instance, due to the degeneracy of the genetic code, there are over 10632 possible mRNA sequences that could encode the spike protein, the COVID-19 vaccines' target. Moreover, designing different elements of the mRNA sequence simultaneously against multiple objectives such as translational efficiency, reduced reactogenicity, and improved stability requires an efficient and sophisticated optimization strategy. Recently, there has been a growing interest in utilizing computational tools to redesign mRNA sequences to improve vaccine characteristics and expedite discovery timelines. In this review, we explore important biophysical features of mRNA to be considered for vaccine design and discuss how computational approaches can be applied to rapidly design mRNA sequences with desirable characteristics.
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Affiliation(s)
| | | | | | | | | | | | | | - Jeff Coller
- Johns Hopkins University, Baltimore, MD, USA
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Shi Y, Zhen X, Zhang Y, Li Y, Koo S, Saiding Q, Kong N, Liu G, Chen W, Tao W. Chemically Modified Platforms for Better RNA Therapeutics. Chem Rev 2024; 124:929-1033. [PMID: 38284616 DOI: 10.1021/acs.chemrev.3c00611] [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: 01/30/2024]
Abstract
RNA-based therapies have catalyzed a revolutionary transformation in the biomedical landscape, offering unprecedented potential in disease prevention and treatment. However, despite their remarkable achievements, these therapies encounter substantial challenges including low stability, susceptibility to degradation by nucleases, and a prominent negative charge, thereby hindering further development. Chemically modified platforms have emerged as a strategic innovation, focusing on precise alterations either on the RNA moieties or their associated delivery vectors. This comprehensive review delves into these platforms, underscoring their significance in augmenting the performance and translational prospects of RNA-based therapeutics. It encompasses an in-depth analysis of various chemically modified delivery platforms that have been instrumental in propelling RNA therapeutics toward clinical utility. Moreover, the review scrutinizes the rationale behind diverse chemical modification techniques aiming at optimizing the therapeutic efficacy of RNA molecules, thereby facilitating robust disease management. Recent empirical studies corroborating the efficacy enhancement of RNA therapeutics through chemical modifications are highlighted. Conclusively, we offer profound insights into the transformative impact of chemical modifications on RNA drugs and delineates prospective trajectories for their future development and clinical integration.
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Affiliation(s)
- Yesi Shi
- Center for Nanomedicine and Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Xueyan Zhen
- Center for Nanomedicine and Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Yiming Zhang
- Center for Nanomedicine and Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Yongjiang Li
- Center for Nanomedicine and Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Seyoung Koo
- Center for Nanomedicine and Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Qimanguli Saiding
- Center for Nanomedicine and Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Na Kong
- Center for Nanomedicine and Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou 310058, China
| | - Gang Liu
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Wei Chen
- Center for Nanomedicine and Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
- Genomics Research Center, Academia Sinica, Taipei 11529, Taiwan
| | - Wei Tao
- Center for Nanomedicine and Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
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5
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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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Mokhtari M, Khoshbakht S, Ziyaei K, Akbari ME, Moravveji SS. New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics. Brief Bioinform 2024; 25:bbae074. [PMID: 38446742 PMCID: PMC10939336 DOI: 10.1093/bib/bbae074] [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: 07/21/2023] [Revised: 11/14/2023] [Accepted: 02/07/2021] [Indexed: 03/08/2024] Open
Abstract
Bioinformatics has revolutionized biology and medicine by using computational methods to analyze and interpret biological data. Quantum mechanics has recently emerged as a promising tool for the analysis of biological systems, leading to the development of quantum bioinformatics. This new field employs the principles of quantum mechanics, quantum algorithms, and quantum computing to solve complex problems in molecular biology, drug design, and protein folding. However, the intersection of bioinformatics, biology, and quantum mechanics presents unique challenges. One significant challenge is the possibility of confusion among scientists between quantum bioinformatics and quantum biology, which have similar goals and concepts. Additionally, the diverse calculations in each field make it difficult to establish boundaries and identify purely quantum effects from other factors that may affect biological processes. This review provides an overview of the concepts of quantum biology and quantum mechanics and their intersection in quantum bioinformatics. We examine the challenges and unique features of this field and propose a classification of quantum bioinformatics to promote interdisciplinary collaboration and accelerate progress. By unlocking the full potential of quantum bioinformatics, this review aims to contribute to our understanding of quantum mechanics in biological systems.
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Affiliation(s)
- Majid Mokhtari
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
| | - Samane Khoshbakht
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
- Duke Molecular Physiology Institute, Duke University School of Medicine-Cardiology, Durham, NC, 27701, USA
| | - Kobra Ziyaei
- Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj, Iran
| | | | - Sayyed Sajjad Moravveji
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
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Parthiban S, Vijeesh T, Gayathri T, Shanmugaraj B, Sharma A, Sathishkumar R. Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals. FRONTIERS IN PLANT SCIENCE 2023; 14:1252166. [PMID: 38034587 PMCID: PMC10684705 DOI: 10.3389/fpls.2023.1252166] [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: 07/03/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023]
Abstract
Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.
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Affiliation(s)
- Subramanian Parthiban
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thandarvalli Vijeesh
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thashanamoorthi Gayathri
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Balamurugan Shanmugaraj
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Ashutosh Sharma
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Queretaro, Mexico
| | - Ramalingam Sathishkumar
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
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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 2023:10.1007/s12033-023-00863-3. [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] [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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Taniguchi M, Ohshiro T, Tada T. Single-Molecule Identification of Nucleotides Using a Quantum Computer. J Phys Chem B 2023; 127:6636-6642. [PMID: 37466988 DOI: 10.1021/acs.jpcb.3c02918] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Genomic information is essential for human health. Due to its large volume, genomic information can be potentially computed using quantum computers, which are rapidly developing. Genome analysis using quantum computers can accelerate the development of personalized medicine, innovative drugs, and novel diagnostics based on genomic information. However, genomic analysis, including nucleotide identification, has not yet been performed using quantum computers. Here, we demonstrate single-molecule identification of nucleotides using a quantum computer. We have designed a quantum gate that explains the single-molecule conductance of adenosine electronically bonded between electrodes. The quantum circuit consists of a reverse and an encoding quantum gate that can strongly distinguish adenosine among the four nucleotides. Our results are the first step toward the realization of genome analysis using quantum computers.
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Affiliation(s)
| | - Takahito Ohshiro
- SNAKEN, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047 Japan
| | - Tomofumi Tada
- Kyushu University Platform of Inter/Transdisciplinary Energy Research, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
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Lau B, Emani PS, Chapman J, Yao L, Lam T, Merrill P, Warrell J, Gerstein MB, Lam HYK. Insights from incorporating quantum computing into drug design workflows. Bioinformatics 2023; 39:6881079. [PMID: 36477833 PMCID: PMC9825754 DOI: 10.1093/bioinformatics/btac789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 10/14/2022] [Accepted: 12/06/2022] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. RESULTS We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug-design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the coronavirus (SARS-CoV-2) protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD. AVAILABILITY AND IMPLEMENTATION Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | | | - Lijing Yao
- HypaHealth, HypaHub Inc., San Jose, CA 95128, USA
| | - Tarsus Lam
- HypaHealth, HypaHub Inc., San Jose, CA 95128, USA
| | - Paul Merrill
- HypaHealth, HypaHub Inc., San Jose, CA 95128, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
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Engineering Modified mRNA-Based Vaccine against Dengue Virus Using Computational and Reverse Vaccinology Approaches. Int J Mol Sci 2022; 23:ijms232213911. [PMID: 36430387 PMCID: PMC9698390 DOI: 10.3390/ijms232213911] [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] [Received: 10/10/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 11/16/2022] Open
Abstract
Dengue virus belonging to the family Flaviviridae and its four serotypes are responsible for dengue infections, which extend over 60 countries in tropical and subtropical areas of the world including Pakistan. During the ongoing dengue outbreak in Pakistan (2022), over 30,000 cases have been reported, and over 70 lives have been lost. The only commercialized vaccine against DENV, Dengvaxia, cannot be administered as a prophylactic measure to cure this infection due to various complications. Using machine learning and reverse vaccinology approaches, this study was designed to develop a tetravalent modified nucleotide mRNA vaccine using NS1, prM, and EIII sequences of dengue virus from Pakistani isolates. Based on high antigenicity, non-allergenicity, and toxicity profiling, B-cell epitope, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) putative vaccine targets were predicted. Molecular docking confirmed favorable interactions between T-cell epitopes and their respective HLA alleles, while normal mode analysis validated high-affinity interactions of vaccine proteins with immune receptors. In silico immune simulations confirmed adequate immune responses to eliminate the antigen and generate memory. Codon optimization, physicochemical features, nucleotide modifications, and suitable vector availability further ensured better antigen expression and adaptive immune responses. We predict that this vaccine construct may prove to be a good vaccinal candidate against dengue virus in vitro as well.
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RNA folding using quantum computers. PLoS Comput Biol 2022; 18:e1010032. [PMID: 35404931 PMCID: PMC9022793 DOI: 10.1371/journal.pcbi.1010032] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 04/21/2022] [Accepted: 03/18/2022] [Indexed: 11/19/2022] Open
Abstract
The 3-dimensional fold of an RNA molecule is largely determined by patterns of intramolecular hydrogen bonds between bases. Predicting the base pairing network from the sequence, also referred to as RNA secondary structure prediction or RNA folding, is a nondeterministic polynomial-time (NP)-complete computational problem. The structure of the molecule is strongly predictive of its functions and biochemical properties, and therefore the ability to accurately predict the structure is a crucial tool for biochemists. Many methods have been proposed to efficiently sample possible secondary structure patterns. Classic approaches employ dynamic programming, and recent studies have explored approaches inspired by evolutionary and machine learning algorithms. This work demonstrates leveraging quantum computing hardware to predict the secondary structure of RNA. A Hamiltonian written in the form of a Binary Quadratic Model (BQM) is derived to drive the system toward maximizing the number of consecutive base pairs while jointly maximizing the average length of the stems. A Quantum Annealer (QA) is compared to a Replica Exchange Monte Carlo (REMC) algorithm programmed with the same objective function, with the QA being shown to be highly competitive at rapidly identifying low energy solutions. The method proposed in this study was compared to three algorithms from literature and, despite its simplicity, was found to be competitive on a test set containing known structures with pseudoknots. The recent FDA approval of mRNA-based vaccines has increased public interest in synthetically designed RNA molecules. RNA molecules fold into complex secondary structures which determine their molecular properties and in part their efficacy. Determining the folded structure of an RNA molecule is a computationally challenging task with exponential scaling that is intractable to solve exactly, and therefore approximate methods are used. Quantum computing technology offers a new approach to finding approximate solutions to problems with exponential scaling. We formulate a simplistic, yet effective, model of RNA folding that can easily be mapped to quantum computers and we show that currently available quantum computing hardware is competitive with classical methods.
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