1
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Ni B, Kaplan DL, Buehler MJ. ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. SCIENCE ADVANCES 2024; 10:eadl4000. [PMID: 38324676 PMCID: PMC10849601 DOI: 10.1126/sciadv.adl4000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/08/2024] [Indexed: 02/09/2024]
Abstract
Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here, we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as the target to enable the discovery of protein materials with superior mechanical properties.
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Affiliation(s)
- Bo Ni
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
| | - David L. Kaplan
- Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
| | - Markus J. Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
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2
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Kim JE, Kang JH, Kwon WH, Lee I, Park SJ, Kim CH, Jeong WJ, Choi JS, Kim K. Self-assembling biomolecules for biosensor applications. Biomater Res 2023; 27:127. [PMID: 38053161 PMCID: PMC10696764 DOI: 10.1186/s40824-023-00466-8] [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: 10/01/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023] Open
Abstract
Molecular self-assembly has received considerable attention in biomedical fields as a simple and effective method for developing biomolecular nanostructures. Self-assembled nanostructures can exhibit high binding affinity and selectivity by displaying multiple ligands/receptors on their surface. In addition, the use of supramolecular structure change upon binding is an intriguing approach to generate binding signal. Therefore, many self-assembled nanostructure-based biosensors have been developed over the past decades, using various biomolecules (e.g., peptides, DNA, RNA, lipids) and their combinations with non-biological substances. In this review, we provide an overview of recent developments in the design and fabrication of self-assembling biomolecules for biosensing. Furthermore, we discuss representative electrochemical biosensing platforms which convert the biochemical reactions of those biomolecules into electrical signals (e.g., voltage, ampere, potential difference, impedance) to contribute to detect targets. This paper also highlights the successful outcomes of self-assembling biomolecules in biosensor applications and discusses the challenges that this promising technology needs to overcome for more widespread use.
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Affiliation(s)
- Ji-Eun Kim
- Department of Chemical & Biochemical Engineering, Dongguk University, Seoul, 04620, Republic of Korea
| | - Jeon Hyeong Kang
- Department of Biological Sciences and Bioengineering, Inha University, Incheon, 22212, Republic of Korea
| | - Woo Hyun Kwon
- Laboratory of Tissue Engineering, Korea Institute of Radiological and Medical Sciences, Seoul, 01812, Republic of Korea
- Department of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Inseo Lee
- Department of Biological Sciences and Bioengineering, Inha University, Incheon, 22212, Republic of Korea
| | - Sang Jun Park
- Laboratory of Tissue Engineering, Korea Institute of Radiological and Medical Sciences, Seoul, 01812, Republic of Korea
| | - Chun-Ho Kim
- Laboratory of Tissue Engineering, Korea Institute of Radiological and Medical Sciences, Seoul, 01812, Republic of Korea
| | - Woo-Jin Jeong
- Department of Biological Sciences and Bioengineering, Inha University, Incheon, 22212, Republic of Korea.
- Department of Biological Engineering, Inha University, Incheon, 22212, Republic of Korea.
| | - Jun Shik Choi
- Laboratory of Tissue Engineering, Korea Institute of Radiological and Medical Sciences, Seoul, 01812, Republic of Korea.
| | - Kyobum Kim
- Department of Chemical & Biochemical Engineering, Dongguk University, Seoul, 04620, Republic of Korea.
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3
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Ni B, Kaplan DL, Buehler MJ. Generative design of de novo proteins based on secondary structure constraints using an attention-based diffusion model. Chem 2023; 9:1828-1849. [PMID: 37614363 PMCID: PMC10443900 DOI: 10.1016/j.chempr.2023.03.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
We report two generative deep learning models that predict amino acid sequences and 3D protein structures based on secondary structure design objectives via either overall content or per-residue structure. Both models are robust regarding imperfect inputs and offer de novo design capacity as they can discover new protein sequences not yet discovered from natural mechanisms or systems. The residue-level secondary structure design model generally yields higher accuracy and more diverse sequences. These findings suggest unexplored opportunities for protein designs and functional outcomes within the vast amino acid sequences beyond known proteins. Our models, based on an attention-based diffusion model and trained on a dataset extracted from experimentally known 3D protein structures, offer numerous downstream applications in conditional generative design of various biological or engineering systems. Future work may include additional conditioning, and an exploration of other functional properties of the generated proteins for various properties beyond structural objectives.
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Affiliation(s)
- Bo Ni
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
| | - David L. Kaplan
- Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
| | - Markus J. Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
- Lead contact
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4
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Villalobos-Alva J, Ochoa-Toledo L, Villalobos-Alva MJ, Aliseda A, Pérez-Escamirosa F, Altamirano-Bustamante NF, Ochoa-Fernández F, Zamora-Solís R, Villalobos-Alva S, Revilla-Monsalve C, Kemper-Valverde N, Altamirano-Bustamante MM. Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field. Front Bioeng Biotechnol 2022; 10:788300. [PMID: 35875501 PMCID: PMC9301016 DOI: 10.3389/fbioe.2022.788300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 05/25/2022] [Indexed: 11/23/2022] Open
Abstract
Proteins are some of the most fascinating and challenging molecules in the universe, and they pose a big challenge for artificial intelligence. The implementation of machine learning/AI in protein science gives rise to a world of knowledge adventures in the workhorse of the cell and proteome homeostasis, which are essential for making life possible. This opens up epistemic horizons thanks to a coupling of human tacit–explicit knowledge with machine learning power, the benefits of which are already tangible, such as important advances in protein structure prediction. Moreover, the driving force behind the protein processes of self-organization, adjustment, and fitness requires a space corresponding to gigabytes of life data in its order of magnitude. There are many tasks such as novel protein design, protein folding pathways, and synthetic metabolic routes, as well as protein-aggregation mechanisms, pathogenesis of protein misfolding and disease, and proteostasis networks that are currently unexplored or unrevealed. In this systematic review and biochemical meta-analysis, we aim to contribute to bridging the gap between what we call binomial artificial intelligence (AI) and protein science (PS), a growing research enterprise with exciting and promising biotechnological and biomedical applications. We undertake our task by exploring “the state of the art” in AI and machine learning (ML) applications to protein science in the scientific literature to address some critical research questions in this domain, including What kind of tasks are already explored by ML approaches to protein sciences? What are the most common ML algorithms and databases used? What is the situational diagnostic of the AI–PS inter-field? What do ML processing steps have in common? We also formulate novel questions such as Is it possible to discover what the rules of protein evolution are with the binomial AI–PS? How do protein folding pathways evolve? What are the rules that dictate the folds? What are the minimal nuclear protein structures? How do protein aggregates form and why do they exhibit different toxicities? What are the structural properties of amyloid proteins? How can we design an effective proteostasis network to deal with misfolded proteins? We are a cross-functional group of scientists from several academic disciplines, and we have conducted the systematic review using a variant of the PICO and PRISMA approaches. The search was carried out in four databases (PubMed, Bireme, OVID, and EBSCO Web of Science), resulting in 144 research articles. After three rounds of quality screening, 93 articles were finally selected for further analysis. A summary of our findings is as follows: regarding AI applications, there are mainly four types: 1) genomics, 2) protein structure and function, 3) protein design and evolution, and 4) drug design. In terms of the ML algorithms and databases used, supervised learning was the most common approach (85%). As for the databases used for the ML models, PDB and UniprotKB/Swissprot were the most common ones (21 and 8%, respectively). Moreover, we identified that approximately 63% of the articles organized their results into three steps, which we labeled pre-process, process, and post-process. A few studies combined data from several databases or created their own databases after the pre-process. Our main finding is that, as of today, there are no research road maps serving as guides to address gaps in our knowledge of the AI–PS binomial. All research efforts to collect, integrate multidimensional data features, and then analyze and validate them are, so far, uncoordinated and scattered throughout the scientific literature without a clear epistemic goal or connection between the studies. Therefore, our main contribution to the scientific literature is to offer a road map to help solve problems in drug design, protein structures, design, and function prediction while also presenting the “state of the art” on research in the AI–PS binomial until February 2021. Thus, we pave the way toward future advances in the synthetic redesign of novel proteins and protein networks and artificial metabolic pathways, learning lessons from nature for the welfare of humankind. Many of the novel proteins and metabolic pathways are currently non-existent in nature, nor are they used in the chemical industry or biomedical field.
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Affiliation(s)
- Jalil Villalobos-Alva
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Luis Ochoa-Toledo
- Instituto de Ciencias Aplicadas y Tecnología (ICAT), Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
| | - Mario Javier Villalobos-Alva
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Atocha Aliseda
- Instituto de Investigaciones Filosóficas, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
| | - Fernando Pérez-Escamirosa
- Instituto de Ciencias Aplicadas y Tecnología (ICAT), Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
| | | | - Francine Ochoa-Fernández
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Ricardo Zamora-Solís
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Sebastián Villalobos-Alva
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Cristina Revilla-Monsalve
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Nicolás Kemper-Valverde
- Instituto de Ciencias Aplicadas y Tecnología (ICAT), Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
| | - Myriam M. Altamirano-Bustamante
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
- *Correspondence: Myriam M. Altamirano-Bustamante,
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5
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Pereira JM, Vieira M, Santos SM. Step-by-step design of proteins for small molecule interaction: A review on recent milestones. Protein Sci 2021; 30:1502-1520. [PMID: 33934427 DOI: 10.1002/pro.4098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/21/2021] [Accepted: 04/23/2021] [Indexed: 01/01/2023]
Abstract
Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.
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Affiliation(s)
- José M Pereira
- CICECO & Departamento de Química, Universidade de Aveiro, Aveiro, Portugal
| | - Maria Vieira
- CICECO & Departamento de Química, Universidade de Aveiro, Aveiro, Portugal
| | - Sérgio M Santos
- CICECO & Departamento de Química, Universidade de Aveiro, Aveiro, Portugal
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6
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Yang C, Wu KB, Deng Y, Yuan J, Niu J. Geared Toward Applications: A Perspective on Functional Sequence-Controlled Polymers. ACS Macro Lett 2021; 10:243-257. [PMID: 34336395 PMCID: PMC8320758 DOI: 10.1021/acsmacrolett.0c00855] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Sequence-controlled polymers are an emerging class of synthetic polymers with a regulated sequence of monomers. In the past decade, tremendous progress has been made in the synthesis of polymers with the sophisticated sequence control approaching the level manifested in biopolymers. In contrast, the exploration of novel functions that can be achieved by controlling synthetic polymer sequences represents an emerging focus in polymer science. This Viewpoint will survey recent advances in the functional applications of sequence-controlled polymers and provide a perspective on the challenges and outlook for pursuing future applications of this fascinating class of macromolecules.
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Affiliation(s)
- Cangjie Yang
- Department of Chemistry, Boston College, Chestnut Hill, Massachusetts 02467, United States
| | - Kevin B. Wu
- Department of Chemistry, Boston College, Chestnut Hill, Massachusetts 02467, United States
| | - Yu Deng
- Department of Chemistry, Boston College, Chestnut Hill, Massachusetts 02467, United States
| | - Jingsong Yuan
- Department of Chemistry, Boston College, Chestnut Hill, Massachusetts 02467, United States
| | - Jia Niu
- Department of Chemistry, Boston College, Chestnut Hill, Massachusetts 02467, United States
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7
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Hudson IL. Data Integration Using Advances in Machine Learning in Drug Discovery and Molecular Biology. Methods Mol Biol 2021; 2190:167-184. [PMID: 32804365 DOI: 10.1007/978-1-0716-0826-5_7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
While the term artificial intelligence and the concept of deep learning are not new, recent advances in high-performance computing, the availability of large annotated data sets required for training, and novel frameworks for implementing deep neural networks have led to an unprecedented acceleration of the field of molecular (network) biology and pharmacogenomics. The need to align biological data to innovative machine learning has stimulated developments in both data integration (fusion) and knowledge representation, in the form of heterogeneous, multiplex, and biological networks or graphs. In this chapter we briefly introduce several popular neural network architectures used in deep learning, namely, the fully connected deep neural network, recurrent neural network, convolutional neural network, and the autoencoder. Deep learning predictors, classifiers, and generators utilized in modern feature extraction may well assist interpretability and thus imbue AI tools with increased explication, potentially adding insights and advancements in novel chemistry and biology discovery.The capability of learning representations from structures directly without using any predefined structure descriptor is an important feature distinguishing deep learning from other machine learning methods and makes the traditional feature selection and reduction procedures unnecessary. In this chapter we briefly show how these technologies are applied for data integration (fusion) and analysis in drug discovery research covering these areas: (1) application of convolutional neural networks to predict ligand-protein interactions; (2) application of deep learning in compound property and activity prediction; (3) de novo design through deep learning. We also: (1) discuss some aspects of future development of deep learning in drug discovery/chemistry; (2) provide references to published information; (3) provide recently advocated recommendations on using artificial intelligence and deep learning in -omics research and drug discovery.
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Affiliation(s)
- Irene Lena Hudson
- Mathematical Sciences, School of Science, RMIT University, Melbourne, VIC, Australia.
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8
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Sunita, Sajid A, Singh Y, Shukla P. Computational tools for modern vaccine development. Hum Vaccin Immunother 2020; 16:723-735. [PMID: 31545127 PMCID: PMC7227725 DOI: 10.1080/21645515.2019.1670035] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 08/28/2019] [Accepted: 09/13/2019] [Indexed: 12/12/2022] Open
Abstract
Vaccines play an essential role in controlling the rates of fatality and morbidity. Vaccines not only arrest the beginning of different diseases but also assign a gateway for its elimination and reduce toxicity. This review gives an overview of the possible uses of computational tools for vaccine design. Moreover, we have described the initiatives of utilizing the diverse computational resources by exploring the immunological databases for developing epitope-based vaccines, peptide-based drugs, and other resources of immunotherapeutics. Finally, the applications of multi-graft and multivalent scaffolding, codon optimization and antibodyomics tools in identifying and designing in silico vaccine candidates are described.
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Affiliation(s)
- Sunita
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
- Bacterial Pathogenesis Laboratory, Department of Zoology, University of Delhi, Delhi
| | - Andaleeb Sajid
- National Institutes of Health, National Cancer Institute, Bethesda, MD, USA
| | - Yogendra Singh
- Bacterial Pathogenesis Laboratory, Department of Zoology, University of Delhi, Delhi
| | - Pratyoosh Shukla
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
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9
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Yu CH, Buehler MJ. Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling. APL Bioeng 2020; 4:016108. [PMID: 32206742 PMCID: PMC7078008 DOI: 10.1063/1.5133026] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 01/29/2020] [Indexed: 11/14/2022] Open
Abstract
We report the use of a deep learning model to design de novo proteins, based on the interplay of elementary building blocks via hierarchical patterns. The deep neural network model is based on translating protein sequences and structural information into a musical score that features different pitches for each of the amino acids, and variations in note length and note volume reflecting secondary structure information and information about the chain length and distinct protein molecules. We train a deep learning model whose architecture is composed of several long short-term memory units from data consisting of musical representations of proteins classified by certain features, focused here on alpha-helix rich proteins. Using the deep learning model, we then generate de novo musical scores and translate the pitch information and chain lengths into sequences of amino acids. We use a Basic Local Alignment Search Tool to compare the predicted amino acid sequences against known proteins, and estimate folded protein structures using the Optimized protein fold RecognitION method (ORION) and MODELLER. We find that the method proposed here can be used to design de novo proteins that do not exist yet, and that the designed proteins fold into specified secondary structures. We validate the newly predicted protein by molecular dynamics equilibration in explicit water and subsequent characterization using a normal mode analysis. The method provides a tool to design novel protein materials that could find useful applications as materials in biology, medicine, and engineering.
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Affiliation(s)
| | - Markus J. Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM),
Department of Civil and Environmental Engineering, Massachusetts Institute of
Technology, 77 Massachusetts Ave. 1-290, Cambridge, Massachusetts 02139,
USA
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10
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Liu H, Cao M, Wang Y, Lv B, Li C. Bioengineering oligomerization and monomerization of enzymes: learning from natural evolution to matching the demands for industrial applications. Crit Rev Biotechnol 2020; 40:231-246. [PMID: 31914816 DOI: 10.1080/07388551.2019.1711014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
It is generally accepted that oligomeric enzymes evolve from their monomeric ancestors, and the evolution process generates superior structural benefits for functional advantages. Furthermore, adjusting the transition between different oligomeric states is an important mechanism for natural enzymes to regulate their catalytic functions for adapting environmental fluctuations in nature, which inspires researchers to mimic such a strategy to develop artificially oligomerized enzymes through protein engineering for improved performance under specific conditions. On the other hand, transforming oligomeric enzymes into their monomers is needed in fundamental research for deciphering catalytic mechanisms as well as exploring their catalytic capacities for better industrial applications. In this article, strategies for developing artificially oligomerized and monomerized enzymes are reviewed and highlighted by their applications. Furthermore, advances in the computational prediction of oligomeric structures are introduced, which would accelerate the systematic design of oligomeric and monomeric enzymes. Finally, the current challenges and future directions in this field are discussed.
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Affiliation(s)
- Hu Liu
- Institute for Synthetic Biosystem, Department of Biochemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, China
| | - Mingming Cao
- Institute for Synthetic Biosystem, Department of Biochemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, China
| | - Ying Wang
- Institute for Synthetic Biosystem, Department of Biochemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, China
| | - Bo Lv
- Institute for Synthetic Biosystem, Department of Biochemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, China
| | - Chun Li
- Institute for Synthetic Biosystem, Department of Biochemical Engineering, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, China
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11
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Sheldon RA, Brady D. Broadening the Scope of Biocatalysis in Sustainable Organic Synthesis. CHEMSUSCHEM 2019; 12:2859-2881. [PMID: 30938093 DOI: 10.1002/cssc.201900351] [Citation(s) in RCA: 155] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 02/05/2019] [Accepted: 03/04/2019] [Indexed: 05/21/2023]
Abstract
This Review is aimed at synthetic organic chemists who may be familiar with organometallic catalysis but have no experience with biocatalysis, and seeks to provide an answer to the perennial question: if it is so attractive, why wasn't it extensively used in the past? The development of biocatalysis in industrial organic synthesis is traced from the middle of the last century. Advances in molecular biology in the last two decades, in particular genome sequencing, gene synthesis and directed evolution of proteins, have enabled remarkable improvements in scope and substantially reduced biocatalyst development times and cost contributions. Additionally, improvements in biocatalyst recovery and reuse have been facilitated by developments in enzyme immobilization technologies. Biocatalysis has become eminently competitive with chemocatalysis and the biocatalytic production of important pharmaceutical intermediates, such as enantiopure alcohols and amines, has become mainstream organic synthesis. The synthetic space of biocatalysis has significantly expanded and is currently being extended even further to include new-to-nature biocatalytic reactions.
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Affiliation(s)
- Roger A Sheldon
- Molecular Sciences Institute, School of Chemistry, University of the Witwatersrand, Johannesburg, 2050, South Africa
- Department of Biotechnology, Delft University of Technology, Section BOC, van der Maasweg 9, 2629 HZ, Delft, The Netherlands
| | - Dean Brady
- Molecular Sciences Institute, School of Chemistry, University of the Witwatersrand, Johannesburg, 2050, South Africa
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12
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Richardson LC, Connell ND, Lewis SM, Pauwels E, Murch RS. Cyberbiosecurity: A Call for Cooperation in a New Threat Landscape. Front Bioeng Biotechnol 2019; 7:99. [PMID: 31245363 PMCID: PMC6562220 DOI: 10.3389/fbioe.2019.00099] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 04/18/2019] [Indexed: 11/13/2022] Open
Abstract
The life sciences now interface broadly with information technology (IT) and cybersecurity. This convergence is a key driver in the explosion of biotechnology research and its industrial applications in health care, agriculture, manufacturing, automation, artificial intelligence, and synthetic biology. As the information and handling mechanisms for biological materials have become increasingly digitized, many market sectors are now vulnerable to threats at the digital interface. This growing landscape will be addressed by cyberbiosecurity, the emerging field at the convergence of both the life sciences and IT disciplines. This manuscript summarizes the current cyberbiosecurity landscape, identifies existing vulnerabilities, and calls for formalized collaboration across a swath of disciplines to develop frameworks for early response systems to anticipate, identify, and mitigate threats in this emerging domain.
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Affiliation(s)
| | - Nancy D Connell
- Johns Hopkins Center for Health Security, Bloomberg School of Public Health, Baltimore, MD, United States
| | | | - Eleonore Pauwels
- Wilson Center Science and Technology Innovation Program, The Wilson Center, Washington, DC, United States
| | - Randy S Murch
- Virginia Tech Research Center, School of Public and International Affairs, Virginia Polytechnic Institute and State University, Arlington, VA, United States
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13
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Iyengar ARS, Gupta S, Jawalekar S, Pande AH. Protein Chimerization: A New Frontier for Engineering Protein Therapeutics with Improved Pharmacokinetics. J Pharmacol Exp Ther 2019; 370:703-714. [DOI: 10.1124/jpet.119.257063] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 04/16/2019] [Indexed: 12/20/2022] Open
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14
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Sormanni P, Aprile FA, Vendruscolo M. Third generation antibody discovery methods: in silico rational design. Chem Soc Rev 2018; 47:9137-9157. [PMID: 30298157 DOI: 10.1039/c8cs00523k] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.
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Affiliation(s)
- Pietro Sormanni
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK.
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15
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Rinaldi S, Van der Kamp MW, Ranaghan KE, Mulholland AJ, Colombo G. Understanding Complex Mechanisms of Enzyme Reactivity: The Case of Limonene-1,2-Epoxide Hydrolases. ACS Catal 2018. [DOI: 10.1021/acscatal.8b00863] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Silvia Rinaldi
- Istituto di Chimica del Riconoscimento Molecolare, C.N.R., Via Mario Bianco 9, 20131 Milano, Italy
| | - Marc W. Van der Kamp
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock’s Close, Bristol BS8 1TS, United Kingdom
- School of Biochemistry, University of Bristol, University Walk, Bristol BS8 1TD, United Kingdom
| | - Kara E. Ranaghan
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock’s Close, Bristol BS8 1TS, United Kingdom
| | - Adrian J. Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock’s Close, Bristol BS8 1TS, United Kingdom
| | - Giorgio Colombo
- Istituto di Chimica del Riconoscimento Molecolare, C.N.R., Via Mario Bianco 9, 20131 Milano, Italy
- Dipartimento di Chimica, Università degli Studi di Pavia, Via Taramelli 12, 27100 Pavia, Italy
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Setiawan D, Brender J, Zhang Y. Recent advances in automated protein design and its future challenges. Expert Opin Drug Discov 2018; 13:587-604. [PMID: 29695210 DOI: 10.1080/17460441.2018.1465922] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.
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Affiliation(s)
- Dani Setiawan
- a Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , MI , USA
| | - Jeffrey Brender
- b Radiation Biology Branch , Center for Cancer Research, National Cancer Institute - NIH , Bethesda , MD , USA
| | - Yang Zhang
- a Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , MI , USA.,c Department of Biological Chemistry , University of Michigan , Ann Arbor , MI , USA
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