1
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Pham C, Stogios PJ, Savchenko A, Mahadevan R. Computation-guided transcription factor biosensor specificity engineering for adipic acid detection. Comput Struct Biotechnol J 2024; 23:2211-2219. [PMID: 38817964 PMCID: PMC11137364 DOI: 10.1016/j.csbj.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 05/01/2024] [Accepted: 05/02/2024] [Indexed: 06/01/2024] Open
Abstract
Transcription factor (TF)-based biosensors that connect small-molecule sensing with readouts such as fluorescence have proven to be useful synthetic biology tools for applications in biotechnology. However, the development of specific TF-based biosensors is hindered by the limited repertoire of TFs specific for molecules of interest since current construction methods rely on a limited set of characterized TFs. In this study, we present an approach for engineering the specificity of TFs through a computation-based workflow using molecular docking that enables targeted alteration of TF ligand specificity. Using this method, we engineer the LysR family BenM TF to alter its specificity from its cognate ligand cis,cis-muconic acid to adipic acid through a single amino acid substitution identified by our computational workflow. When implemented in a cell-free system, the engineered biosensor shows higher ligand sensitivity, expanding the potential applications of this circuit. We further investigate ligand binding through molecular dynamics to analyze the substitution, elucidating the impact of modulating a single amino acid position on the mechanism of BenM ligand binding. This study represents the first application of biomolecular modeling methods for altering BenM specificity and for gaining insights into how mutations influence the structural dynamics of BenM. Such methods can potentially be applied to other TFs to alter specificity and analyze the dynamics responsible for these changes, highlighting the applicability of computational tools for informing experiments. In addition, our developed adipic acid biosensor can be applied for the identification and engineering of enzymes to produce adipic acid.
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Affiliation(s)
- Chester Pham
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario, Canada
| | - Peter J. Stogios
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario, Canada
| | - Alexei Savchenko
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario, Canada
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, Alberta, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario, Canada
- The Institute of Biomedical Engineering, University of Toronto, Ontario, Canada
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2
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Zheng F, Jiang X, Wen Y, Yang Y, Li M. Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength. Comput Struct Biotechnol J 2024; 23:460-472. [PMID: 38235359 PMCID: PMC10792694 DOI: 10.1016/j.csbj.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/14/2023] [Accepted: 12/16/2023] [Indexed: 01/19/2024] Open
Abstract
The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.
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Affiliation(s)
- Feifan Zheng
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Xin Jiang
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Yuhao Wen
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Yan Yang
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Minghui Li
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
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3
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Albayati SH, Nezhad NG, Taki AG, Rahman RNZRA. Efficient and easible biocatalysts: Strategies for enzyme improvement. A review. Int J Biol Macromol 2024; 276:133978. [PMID: 39038570 DOI: 10.1016/j.ijbiomac.2024.133978] [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: 04/18/2024] [Revised: 06/19/2024] [Accepted: 07/16/2024] [Indexed: 07/24/2024]
Abstract
Owing to the environmental friendliness and vast advantages that enzymes offer in the biotechnology and industry fields, biocatalysts are a prolific investigation field. However, the low catalytic activity, stability, and specific selectivity of the enzyme limit the range of the reaction enzymes involved in. A comprehensive understanding of the protein structure and dynamics in terms of molecular details enables us to tackle these limitations effectively and enhance the catalytic activity by enzyme engineering or modifying the supports and solvents. Along with different strategies including computational, enzyme engineering based on DNA recombination, enzyme immobilization, additives, chemical modification, and physicochemical modification approaches can be promising for the wide spread of industrial enzyme usage. This is attributed to the successful application of biocatalysts in industrial and synthetic processes requires a system that exhibits stability, activity, and reusability in a continuous flow process, thereby reducing the production cost. The main goal of this review is to display relevant approaches for improving enzyme characteristics to overcome their industrial application.
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Affiliation(s)
- Samah Hashim Albayati
- Enzyme and Microbial Technology Research Centre, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Nima Ghahremani Nezhad
- Enzyme and Microbial Technology Research Centre, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Anmar Ghanim Taki
- Department of Radiology Techniques, Health and Medical Techniques College, Alnoor University, Mosul, Iraq
| | - Raja Noor Zaliha Raja Abd Rahman
- Enzyme and Microbial Technology Research Centre, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; Institute Bioscience, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia.
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4
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Illig AM, Siedhoff NE, Davari MD, Schwaneberg U. Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort. J Chem Inf Model 2024; 64:6350-6360. [PMID: 39088689 DOI: 10.1021/acs.jcim.4c00704] [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: 08/03/2024]
Abstract
Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited screening throughput, hampers efficient protein engineering. Data-driven strategies have emerged as a powerful tool to model the protein fitness landscape that can be explored in silico, significantly accelerating protein engineering campaigns. However, such methods require a certain amount of data, which often cannot be provided, to generate a reliable model of the fitness landscape. Here, we introduce MERGE, a method that combines direct coupling analysis (DCA) and machine learning (ML). MERGE enables data-driven protein engineering when only limited data are available for training, typically ranging from 50 to 500 labeled sequences. Our method demonstrates remarkable performance in predicting a protein's fitness value and rank based on its sequence across diverse proteins and properties. Notably, MERGE outperforms state-of-the-art methods when only small data sets are available for modeling, requiring fewer computational resources, and proving particularly promising for protein engineers who have access to limited amounts of data.
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Affiliation(s)
| | - Niklas E Siedhoff
- Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany
| | - Mehdi D Davari
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany
| | - Ulrich Schwaneberg
- Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany
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5
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Mandal N, Surpeta B, Brezovsky J. Reinforcing Tunnel Network Exploration in Proteins Using Gaussian Accelerated Molecular Dynamics. J Chem Inf Model 2024; 64:6623-6635. [PMID: 39143923 DOI: 10.1021/acs.jcim.4c00966] [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: 08/16/2024]
Abstract
Tunnels are structural conduits in biomolecules responsible for transporting chemical compounds and solvent molecules from the active site. They have been shown to be present in a wide variety of enzymes across all functional and structural classes. However, the study of such pathways is experimentally challenging, because they are typically transient. Computational methods, such as molecular dynamics (MD) simulations, have been successfully proposed to explore tunnels. Conventional MD (cMD) provides structural details to characterize tunnels but suffers from sampling limitations to capture rare tunnel openings on longer time scales. Therefore, in this study, we explored the potential of Gaussian accelerated MD (GaMD) simulations to improve the exploration of complex tunnel networks in enzymes. We used the haloalkane dehalogenase LinB and its two variants with engineered transport pathways, which are not only well-known for their application potential but have also been extensively studied experimentally and computationally regarding their tunnel networks and their importance in multistep catalytic reactions. Our study demonstrates that GaMD efficiently improves tunnel sampling and allows the identification of all known tunnels for LinB and its two mutants. Furthermore, the improved sampling provided insight into a previously unknown transient side tunnel (ST). The extensive conformational landscape explored by GaMD simulations allowed us to investigate in detail the mechanism of ST opening. We determined variant-specific dynamic properties of ST opening, which were previously inaccessible due to limited sampling of cMD. Our comprehensive analysis supports multiple indicators of the functional relevance of the ST, emphasizing its potential significance beyond structural considerations. In conclusion, our research proves that the GaMD method can overcome the sampling limitations of cMD for the effective study of tunnels in enzymes, providing further means for identifying rare tunnels in enzymes with the potential for drug development, precision medicine, and rational protein engineering.
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Affiliation(s)
- Nishita Mandal
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, Poznan 61-614, Poland
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, Warsaw 02-109, Poland
| | - Bartlomiej Surpeta
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, Poznan 61-614, Poland
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, Warsaw 02-109, Poland
| | - Jan Brezovsky
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, Poznan 61-614, Poland
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, Warsaw 02-109, Poland
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6
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Ali I, Wei DQ, Khan A, Feng Y, Waseem M, Hussain Z, Iqbal A, Ali SS, Mohammad A, Zheng J. Improving the substrate binding of acetyl-CoA carboxylase (AccB) from Streptomyces antibioticus through computational enzyme engineering. Biotechnol Appl Biochem 2024; 71:402-413. [PMID: 38287712 DOI: 10.1002/bab.2548] [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: 03/27/2023] [Accepted: 12/15/2023] [Indexed: 01/31/2024]
Abstract
Malonyl-CoA serves as the main building block for the biosynthesis of many important polyketides, as well as fatty acid-derived compounds, such as biofuel. Escherichia coli, Corynebacterium gultamicum, and Saccharomyces cerevisiae have recently been engineered for the biosynthesis of such compounds. However, the developed processes and strains often have insufficient productivity. In the current study, we used enzyme-engineering approach to improve the binding of acetyl-CoA with ACC. We generated different mutations, and the impact was calculated, which reported that three mutations, that is, S343A, T347W, and S350W, significantly improve the substrate binding. Molecular docking investigation revealed an altered binding network compared to the wild type. In mutants, additional interactions stabilize the binding of the inner tail of acetyl-CoA. Using molecular simulation, the stability, compactness, hydrogen bonding, and protein motions were estimated, revealing different dynamic properties owned by the mutants only but not by the wild type. The findings were further validated by using the binding-free energy (BFE) method, which revealed these mutations as favorable substitutions. The total BFE was reported to be -52.66 ± 0.11 kcal/mol for the wild type, -55.87 ± 0.16 kcal/mol for the S343A mutant, -60.52 ± 0.25 kcal/mol for T347W mutant, and -59.64 ± 0.25 kcal/mol for the S350W mutant. This shows that the binding of the substrate is increased due to the induced mutations and strongly corroborates with the docking results. In sum, this study provides information regarding the essential hotspot residues for the substrate binding and can be used for application in industrial processes.
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Affiliation(s)
- Imtiaz Ali
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Dong-Qing Wei
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Abbas Khan
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, P. R. China
- Sunway Microbiome Centre, School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia
| | - Yuanyuan Feng
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Muhammad Waseem
- Faculty of Rehabilitation and Allied Health Science, Riphah International University, Islamabad, Pakistan
| | - Zahid Hussain
- Centre for Biotechnology and Microbiology, University of Swat, Charbagh, Khyber Pakhtunkhwa, Pakistan
| | - Arshad Iqbal
- Centre for Biotechnology and Microbiology, University of Swat, Charbagh, Khyber Pakhtunkhwa, Pakistan
| | - Syed Shujait Ali
- Centre for Biotechnology and Microbiology, University of Swat, Charbagh, Khyber Pakhtunkhwa, Pakistan
| | - Anwar Mohammad
- Department of Biochemistry and Molecular Biology, Dasman Diabetes Institute, Dasman, Kuwait
| | - Jianting Zheng
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, P. R. China
- Joint International Research Laboratory of Metabolic & Developmental Sciences, Shanghai Jiao Tong University, Shanghai, P. R. China
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7
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Sagar, Takhellambam M, Rattan A, Prajapati VK. Unleashing the power of antibodies: Engineering for tomorrow's therapy. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 140:1-36. [PMID: 38762268 DOI: 10.1016/bs.apcsb.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2024]
Abstract
Antibodies play a crucial role in host defense against various diseases. Antibody engineering is a multidisciplinary field that seeks to improve the quality of life of humans. In the context of disease, antibodies are highly specialized proteins that form a critical line of defense against pathogens and the disease caused by them. These infections trigger the innate arm of immunity by presenting on antigen-presenting cells such as dendritic cells. This ultimately links to the adaptive arm, where antibody production and maturation occur against that particular antigen. Upon binding with their specific antigens, antibodies trigger various immune responses to eliminate pathogens in a process called complement-dependent cytotoxicity and phagocytosis of invading microorganisms by immune cells or induce antibody-dependent cellular cytotoxicity is done by antibodies. These engineered antibodies are being used for various purposes, such as therapeutics, diagnostics, and biotechnology research. Cutting-edge techniques that include hybridoma technology, transgenic mice, display techniques like phage, yeast and ribosome displays, and next-generation sequencing are ways to engineer antibodies and mass production for the use of humankind. Considering the importance of antibodies in protecting from a diverse array of pathogens, investing in research holds great promise to develop future therapeutic targets to combat various diseases.
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Affiliation(s)
- Sagar
- Department of Biochemistry, University of Delhi South Campus, Benito Juarez Road, Dhaula Kuan, New Delhi, India
| | - Malemnganba Takhellambam
- Department of Biochemistry, University of Delhi South Campus, Benito Juarez Road, Dhaula Kuan, New Delhi, India
| | - Aditi Rattan
- Department of Biochemistry, University of Delhi South Campus, Benito Juarez Road, Dhaula Kuan, New Delhi, India
| | - Vijay Kumar Prajapati
- Department of Biochemistry, University of Delhi South Campus, Benito Juarez Road, Dhaula Kuan, New Delhi, India.
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8
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Ghaedizadeh S, Zeinali M, Dabirmanesh B, Rasekh B, Khajeh K, Banaei-Moghaddam AM. Rational design engineering of a more thermostable Sulfurihydrogenibium yellowstonense carbonic anhydrase for potential application in carbon dioxide capture technologies. BIOCHIMICA ET BIOPHYSICA ACTA. PROTEINS AND PROTEOMICS 2024; 1872:140962. [PMID: 37716447 DOI: 10.1016/j.bbapap.2023.140962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/18/2023] [Accepted: 09/06/2023] [Indexed: 09/18/2023]
Abstract
Implementing hyperthermostable carbonic anhydrases into CO2 capture and storage technologies in order to increase the rate of CO2 absorption from the industrial flue gases is of great importance from technical and economical points of view. The present study employed a combination of in silico tools to further improve thermostability of a known thermostable carbonic anhydrase from Sulfurihydrogenibium yellowstonense. Experimental results showed that our rationally engineered K100G mutant not only retained the overall structure and catalytic efficiency but also showed a 3 °C increase in the melting temperature and a two-fold improvement in the enzyme half-life at 85 °C. Based on the molecular dynamics simulation results, rearrangement of salt bridges and hydrogen interactions network causes a reduction in local flexibility of the K100G variant. In conclusion, our study demonstrated that thermostability can be improved through imposing local structural rigidity by engineering a single-point mutation on the surface of the enzyme.
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Affiliation(s)
- Shima Ghaedizadeh
- Laboratory of Genomics and Epigenomics (LGE), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Majid Zeinali
- Microbiology and Biotechnology Research Group, Research Institute of Petroleum Industry (RIPI), Tehran, Iran.
| | - Bahareh Dabirmanesh
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Behnam Rasekh
- Microbiology and Biotechnology Research Group, Research Institute of Petroleum Industry (RIPI), Tehran, Iran
| | - Khosrow Khajeh
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Ali Mohammad Banaei-Moghaddam
- Laboratory of Genomics and Epigenomics (LGE), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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9
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Yang ZJ, Shao Q, Jiang Y, Jurich C, Ran X, Juarez RJ, Yan B, Stull SL, Gollu A, Ding N. Mutexa: A Computational Ecosystem for Intelligent Protein Engineering. J Chem Theory Comput 2023; 19:7459-7477. [PMID: 37828731 PMCID: PMC10653112 DOI: 10.1021/acs.jctc.3c00602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Indexed: 10/14/2023]
Abstract
Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.
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Affiliation(s)
- Zhongyue J. Yang
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
- Vanderbilt
Institute of Chemical Biology, Vanderbilt
University, Nashville, Tennessee 37235, United States
- Department
of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235, United States
- Data
Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Qianzhen Shao
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yaoyukun Jiang
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Christopher Jurich
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Vanderbilt
Institute of Chemical Biology, Vanderbilt
University, Nashville, Tennessee 37235, United States
| | - Xinchun Ran
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Reecan J. Juarez
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37235, United States
| | - Bailu Yan
- Department
of Biostatistics, Vanderbilt University, Nashville, Tennessee 37205, United States
| | - Sebastian L. Stull
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Anvita Gollu
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Ning Ding
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
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10
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Patsch D, Eichenberger M, Voss M, Bornscheuer UT, Buller RM. LibGENiE - A bioinformatic pipeline for the design of information-enriched enzyme libraries. Comput Struct Biotechnol J 2023; 21:4488-4496. [PMID: 37736300 PMCID: PMC10510078 DOI: 10.1016/j.csbj.2023.09.013] [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: 06/02/2023] [Revised: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 09/23/2023] Open
Abstract
Enzymes are potent catalysts with high specificity and selectivity. To leverage nature's synthetic potential for industrial applications, various protein engineering techniques have emerged which allow to tailor the catalytic, biophysical, and molecular recognition properties of enzymes. However, the many possible ways a protein can be altered forces researchers to carefully balance between the exhaustiveness of an enzyme screening campaign and the required resources. Consequently, the optimal engineering strategy is often defined on a case-by-case basis. Strikingly, while predicting mutations that lead to an improved target function is challenging, here we show that the prediction and exclusion of deleterious mutations is a much more straightforward task as analyzed for an engineered carbonic acid anhydrase, a transaminase, a squalene-hopene cyclase and a Kemp eliminase. Combining such a pre-selection of allowed residues with advanced gene synthesis methods opens a path toward an efficient and generalizable library construction approach for protein engineering. To give researchers easy access to this methodology, we provide the website LibGENiE containing the bioinformatic tools for the library design workflow.
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Affiliation(s)
- David Patsch
- Zurich University of Applied Sciences, School of Life Sciences and Facility Management, Institute of Chemistry and Biotechnology, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
- Institute of Biochemistry, Department of Biotechnology & Enzyme Catalysis, Greifswald University, Felix-Hausdorff-Strasse 4, 17487 Greifswald, Germany
| | - Michael Eichenberger
- Zurich University of Applied Sciences, School of Life Sciences and Facility Management, Institute of Chemistry and Biotechnology, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
| | - Moritz Voss
- Zurich University of Applied Sciences, School of Life Sciences and Facility Management, Institute of Chemistry and Biotechnology, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
| | - Uwe T. Bornscheuer
- Institute of Biochemistry, Department of Biotechnology & Enzyme Catalysis, Greifswald University, Felix-Hausdorff-Strasse 4, 17487 Greifswald, Germany
| | - Rebecca M. Buller
- Zurich University of Applied Sciences, School of Life Sciences and Facility Management, Institute of Chemistry and Biotechnology, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
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11
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Cai P, Liu S, Zhang D, Xing H, Han M, Liu D, Gong L, Hu QN. SynBioTools: a one-stop facility for searching and selecting synthetic biology tools. BMC Bioinformatics 2023; 24:152. [PMID: 37069545 PMCID: PMC10111727 DOI: 10.1186/s12859-023-05281-5] [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: 01/09/2023] [Accepted: 04/11/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology. RESULTS We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system. CONCLUSIONS SynBioTools is freely available at https://synbiotools.lifesynther.com/ . It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection.
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Affiliation(s)
- Pengli Cai
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Sheng Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Dachuan Zhang
- Ecological Systems Design, Institute of Environmental Engineering, ETH Zurich, 8093, Zurich, Switzerland
| | - Huadong Xing
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Mengying Han
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Dongliang Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Linlin Gong
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qian-Nan Hu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
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12
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Raczyńska A, Kapica P, Papaj K, Stańczak A, Shyntum D, Spychalska P, Byczek-Wyrostek A, Góra A. Transient binding sites at the surface of haloalkane dehalogenase LinB as locations for fine-tuning enzymatic activity. PLoS One 2023; 18:e0280776. [PMID: 36827335 PMCID: PMC9956002 DOI: 10.1371/journal.pone.0280776] [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: 01/15/2022] [Accepted: 01/09/2023] [Indexed: 02/25/2023] Open
Abstract
The haloalkane dehalogenase LinB is a well-known enzyme that contains buried active site and is used for many modelling studies. Using classical molecular dynamics simulations of enzymes and substrates, we searched for transient binding sites on the surface of the LinB protein by calculating maps of enzyme-ligand interactions that were then transformed into sparse matrices. All residues considered as functionally important for enzyme performance (e.g., tunnel entrances) were excluded from the analysis to concentrate rather on non-obvious surface residues. From a set of 130 surface residues, twenty-six were proposed as a promising improvement of enzyme performance. Eventually, based on rational selection and filtering out the potentially unstable mutants, a small library of ten mutants was proposed to validate the possibility of fine-tuning the LinB protein. Nearly half of the predicted mutant structures showed improved activity towards the selected substrates, which demonstrates that the proposed approach could be applied to identify non-obvious yet beneficial mutations for enzyme performance especially when obvious locations have already been explored.
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Affiliation(s)
- Agata Raczyńska
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Gliwice, Poland
| | - Patryk Kapica
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Gliwice, Poland
| | - Katarzyna Papaj
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Gliwice, Poland
| | - Agnieszka Stańczak
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Gliwice, Poland
| | - Divine Shyntum
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Gliwice, Poland
| | - Patrycja Spychalska
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Gliwice, Poland
| | | | - Artur Góra
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Gliwice, Poland
- * E-mail:
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13
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Alteration of Chain-Length Selectivity and Thermostability of Rhizopus oryzae Lipase via Virtual Saturation Mutagenesis Coupled with Disulfide Bond Design. Appl Environ Microbiol 2023; 89:e0187822. [PMID: 36602359 PMCID: PMC9888275 DOI: 10.1128/aem.01878-22] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Rhizopus oryzae lipase (ROL) is one of the most important enzymes used in the food, biofuel, and pharmaceutical industries. However, the highly demanding conditions of industrial processes can reduce its stability and activity. To seek a feasible method to improve both the catalytic activity and the thermostability of this lipase, first, the structure of ROL was divided into catalytic and noncatalytic regions by identifying critical amino acids in the crevice-like binding pocket. Second, a mutant screening library aimed at improvement of ROL catalytic performance by virtual saturation mutagenesis of residues in the catalytic region was constructed based on Rosetta's Cartesian_ddg protocol. A double mutant, E265V/S267W (with an E-to-V change at residue 265 and an S-to-W change at residue 267), with markedly improved catalytic activity toward diverse chain-length fatty acid esters was identified. Then, computational design of disulfide bonds was conducted for the noncatalytic amino acids of E265V/S267W, and two potential disulfide bonds, S61C-S115C and E190C-E238C, were identified as candidates. Experimental data validated that the variant E265V/S267W/S61C-S115C/E190C-E238C had superior stability, with an increase of 8.5°C in the melting temperature and a half-life of 31.7 min at 60°C, 4.2-fold longer than that of the wild-type enzyme. Moreover, the variant improved the lipase activity toward five 4-nitrophenyl esters by 1.5 to 3.8 times, exhibiting a potential to modify the catalytic efficiency. IMPORTANCE Rhizopus oryzae lipase (ROL) is very attractive in biotechnology and industry as a safe and environmentally friendly biocatalyst. Functional expression of ROL in Escherichia coli facilitates effective high-throughput screening for positive variants. This work highlights a method to improve both selectivity and thermostability based on a combination of virtual saturation mutagenesis in the substrate pocket and disulfide bond prediction in the noncatalytic region. Using the method, ROL thermostability and activity to diverse 4-nitrophenyl esters could be substantially improved. The strategy of rational introduction of multiple mutations in different functional domains of the enzyme is a great prospect in the modification of biocatalysts.
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14
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Not a Mistake but a Feature: Promiscuous Activity of Enzymes Meeting Mycotoxins. Catalysts 2022. [DOI: 10.3390/catal12101095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Mycotoxins are dangerous compounds and find multiple routes to enter living bodies of humans and animals. To solve the issue and degrade the toxicants, (bio)catalytic processes look very promising. Hexahistidine-tagged organophosphorus hydrolase (His6-OPH) is a well-studied catalyst for degradation of organophosphorus neurotoxins and lactone-containing quorum-sensing signal molecules. Moreover, the catalytic characteristics in hydrolysis of several mycotoxins (patulin, deoxynivalenol, zearalenone, and sterigmatocystin) were studied in this investigation. The best Michaelis constant and catalytic constant were estimated in the case of sterigmatocystin and patulin, respectively. A possible combination of His6-OPH with inorganic sorbents treated by low-temperature plasma was investigated. Further, enzyme–polyelectrolyte complexes of poly(glutamic acid) with His6-OPH and another enzymatic mycotoxin degrader (thermolysin) were successfully used to modify fiber materials. These catalytically active prototypes of protective materials appear to be useful for preventing surface contact and exposure to mycotoxins and other chemicals that are substrates for the enzymes used.
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15
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Oliveira SMD, Densmore D. Hardware, Software, and Wetware Codesign Environment for Synthetic Biology. BIODESIGN RESEARCH 2022; 2022:9794510. [PMID: 37850136 PMCID: PMC10521664 DOI: 10.34133/2022/9794510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 08/10/2022] [Indexed: 10/19/2023] Open
Abstract
Synthetic biology is the process of forward engineering living systems. These systems can be used to produce biobased materials, agriculture, medicine, and energy. One approach to designing these systems is to employ techniques from the design of embedded electronics. These techniques include abstraction, standards, modularity, automated design, and formal semantic models of computation. Together, these elements form the foundation of "biodesign automation," where software, robotics, and microfluidic devices combine to create exciting biological systems of the future. This paper describes a "hardware, software, wetware" codesign vision where software tools can be made to act as "genetic compilers" that transform high-level specifications into engineered "genetic circuits" (wetware). This is followed by a process where automation equipment, well-defined experimental workflows, and microfluidic devices are explicitly designed to house, execute, and test these circuits (hardware). These systems can be used as either massively parallel experimental platforms or distributed bioremediation and biosensing devices. Next, scheduling and control algorithms (software) manage these systems' actual execution and data analysis tasks. A distinguishing feature of this approach is how all three of these aspects (hardware, software, and wetware) may be derived from the same basic specification in parallel and generated to fulfill specific cost, performance, and structural requirements.
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Affiliation(s)
- Samuel M. D. Oliveira
- Department of Electrical and Computer Engineering, Boston University, MA 02215, USA
- Biological Design Center, Boston University, MA 02215, USA
| | - Douglas Densmore
- Department of Electrical and Computer Engineering, Boston University, MA 02215, USA
- Biological Design Center, Boston University, MA 02215, USA
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16
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Enhancing the Catalytic Activity of Type II L-Asparaginase from Bacillus licheniformis through Semi-Rational Design. Int J Mol Sci 2022; 23:ijms23179663. [PMID: 36077061 PMCID: PMC9456134 DOI: 10.3390/ijms23179663] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 01/10/2023] Open
Abstract
Low catalytic activity is a key factor limiting the widespread application of type II L-asparaginase (ASNase) in the food and pharmaceutical industries. In this study, smart libraries were constructed by semi-rational design to improve the catalytic activity of type II ASNase from Bacillus licheniformis. Mutants with greatly enhanced catalytic efficiency were screened by saturation mutations and combinatorial mutations. A quintuple mutant ILRAC was ultimately obtained with specific activity of 841.62 IU/mg and kcat/Km of 537.15 min−1·mM−1, which were 4.24-fold and 6.32-fold more than those of wild-type ASNase. The highest specific activity and kcat/Km were firstly reported in type II ASNase from Bacillus licheniformis. Additionally, enhanced pH stability and superior thermostability were both achieved in mutant ILRAC. Meanwhile, structural alignment and molecular dynamic simulation demonstrated that high structure stability and strong substrate binding were beneficial for the improved thermal stability and enzymatic activity of mutant ILRAC. This is the first time that enzymatic activity of type II ASNase from Bacillus licheniformis has been enhanced by the semi-rational approach, and results provide new insights into enzymatic modification of L-asparaginase for industrial applications.
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17
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Timonina DS, Suplatov DA. Analysis of Multiple Protein Alignments Using 3D-Structural Information on the Orientation of Amino Acid Side-Chains. Mol Biol 2022. [DOI: 10.1134/s0026893322040136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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18
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Sowlati-Hashjin S, Gandhi A, Garton M. Dawn of a New Era for Membrane Protein Design. BIODESIGN RESEARCH 2022; 2022:9791435. [PMID: 37850134 PMCID: PMC10521746 DOI: 10.34133/2022/9791435] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/20/2022] [Indexed: 10/19/2023] Open
Abstract
A major advancement has recently occurred in the ability to predict protein secondary structure from sequence using artificial neural networks. This new accessibility to high-quality predicted structures provides a big opportunity for the protein design community. It is particularly welcome for membrane protein design, where the scarcity of solved structures has been a major limitation of the field for decades. Here, we review the work done to date on the membrane protein design and set out established and emerging tools that can be used to most effectively exploit this new access to structures.
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Affiliation(s)
- Shahin Sowlati-Hashjin
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, Canada, M5S 3E2
| | - Aanshi Gandhi
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, Canada, M5S 3E2
| | - Michael Garton
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, Canada, M5S 3E2
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19
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Vasina M, Velecký J, Planas-Iglesias J, Marques SM, Skarupova J, Damborsky J, Bednar D, Mazurenko S, Prokop Z. Tools for computational design and high-throughput screening of therapeutic enzymes. Adv Drug Deliv Rev 2022; 183:114143. [PMID: 35167900 DOI: 10.1016/j.addr.2022.114143] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 02/04/2022] [Accepted: 02/09/2022] [Indexed: 12/16/2022]
Abstract
Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.
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Affiliation(s)
- Michal Vasina
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic
| | - Jan Velecký
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic
| | - Joan Planas-Iglesias
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic
| | - Sergio M Marques
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic
| | - Jana Skarupova
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic; Enantis, INBIT, Kamenice 34, Brno, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic.
| | - Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic.
| | - Zbynek Prokop
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic.
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20
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Khersonsky O, Fleishman SJ. What Have We Learned from Design of Function in Large Proteins? BIODESIGN RESEARCH 2022; 2022:9787581. [PMID: 37850148 PMCID: PMC10521758 DOI: 10.34133/2022/9787581] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 02/21/2022] [Indexed: 10/19/2023] Open
Abstract
The overarching goal of computational protein design is to gain complete control over protein structure and function. The majority of sophisticated binders and enzymes, however, are large and exhibit diverse and complex folds that defy atomistic design calculations. Encouragingly, recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design accuracy. In these approaches, evolutionary constraints mitigate the risk from misfolding and aggregation, focusing atomistic design calculations on a small but highly enriched sequence subspace. Such methods have dramatically optimized diverse proteins, including vaccine immunogens, enzymes for sustainable chemistry, and proteins with therapeutic potential. The new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design, in principle, to any natural protein of known sequence. We envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.
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Affiliation(s)
- Olga Khersonsky
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Sarel J. Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
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21
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Lemay-St-Denis C, Doucet N, Pelletier JN. Integrating dynamics into enzyme engineering. Protein Eng Des Sel 2022; 35:6842866. [PMID: 36416215 DOI: 10.1093/protein/gzac015] [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: 06/21/2022] [Revised: 11/02/2022] [Accepted: 11/06/2022] [Indexed: 11/24/2022] Open
Abstract
Enzyme engineering has become a widely adopted practice in research labs and industry. In parallel, the past decades have seen tremendous strides in characterizing the dynamics of proteins, using a growing array of methodologies. Importantly, links have been established between the dynamics of proteins and their function. Characterizing the dynamics of an enzyme prior to, and following, its engineering is beginning to inform on the potential of 'dynamic engineering', i.e. the rational modification of protein dynamics to alter enzyme function. Here we examine the state of knowledge at the intersection of enzyme engineering and protein dynamics, describe current challenges and highlight pioneering work in the nascent area of dynamic engineering.
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Affiliation(s)
- Claudèle Lemay-St-Denis
- PROTEO, The Québec Network for Research on Protein, Function, Engineering and Applications, Quebec, QC, Canada
- CGCC, Center in Green Chemistry and Catalysis, Montreal, QC, Canada
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, Canada
| | - Nicolas Doucet
- PROTEO, The Québec Network for Research on Protein, Function, Engineering and Applications, Quebec, QC, Canada
- Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique (INRS), Université du Québec, Laval, QC, Canada
| | - Joelle N Pelletier
- PROTEO, The Québec Network for Research on Protein, Function, Engineering and Applications, Quebec, QC, Canada
- CGCC, Center in Green Chemistry and Catalysis, Montreal, QC, Canada
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, Canada
- Chemistry Department, Université de Montréal, Montreal, QC, Canada
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22
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Shegay MV, Švedas VK, Voevodin VV, Suplatov DA, Popova NN. Guide tree optimization with genetic algorithm to improve multiple protein 3D-structure alignment. Bioinformatics 2022; 38:985-989. [PMID: 34849594 DOI: 10.1093/bioinformatics/btab798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/23/2021] [Accepted: 11/19/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION With the increasing availability of 3D-data, the focus of comparative bioinformatic analysis is shifting from protein sequence alignments toward more content-rich 3D-alignments. This raises the need for new ways to improve the accuracy of 3D-superimposition. RESULTS We proposed guide tree optimization with genetic algorithm (GA) as a universal tool to improve the alignment quality of multiple protein 3D-structures systematically. As a proof of concept, we implemented the suggested GA-based approach in popular Matt and Caretta multiple protein 3D-structure alignment (M3DSA) algorithms, leading to a statistically significant improvement of the TM-score quality indicator by up to 220-1523% on 'SABmark Superfamilies' (in 49-77% of cases) and 'SABmark Twilight' (in 59-80% of cases) datasets. The observed improvement in collections of distant homologies highlights the potentials of GA to optimize 3D-alignments of diverse protein superfamilies as one plausible tool to study the structure-function relationship. AVAILABILITY AND IMPLEMENTATION The source codes of patched gaCaretta and gaMatt programs are available open-access at https://github.com/n-canter/gamaps. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Maksim V Shegay
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
| | - Vytas K Švedas
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia.,Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
| | - Vladimir V Voevodin
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia.,Research Computing Center, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
| | - Dmitry A Suplatov
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
| | - Nina N Popova
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
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23
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Cadet XF, Gelly JC, van Noord A, Cadet F, Acevedo-Rocha CG. Learning Strategies in Protein Directed Evolution. Methods Mol Biol 2022; 2461:225-275. [PMID: 35727454 DOI: 10.1007/978-1-0716-2152-3_15] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.
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Affiliation(s)
- Xavier F Cadet
- PEACCEL, Artificial Intelligence Department, Paris, France
| | - Jean Christophe Gelly
- Laboratoire d'Excellence GR-Ex, Paris, France
- BIGR, DSIMB, UMR_S1134, INSERM, University of Paris & University of Reunion, Paris, France
| | | | - Frédéric Cadet
- Laboratoire d'Excellence GR-Ex, Paris, France
- BIGR, DSIMB, UMR_S1134, INSERM, University of Paris & University of Reunion, Paris, France
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24
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Seo E, Kim M, Park S, Park S, Oh D, Bornscheuer U, Park J. Enzyme Access Tunnel Engineering in Baeyer‐Villiger Monooxygenases to Improve Oxidative Stability and Biocatalyst Performance. Adv Synth Catal 2021. [DOI: 10.1002/adsc.202101044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Eun‐Ji Seo
- Department of Food Science and Engineering Ewha Womans University Seoul 03760 Republic of Korea
| | - Myeong‐Ju Kim
- Department of Food Science and Engineering Ewha Womans University Seoul 03760 Republic of Korea
| | - So‐Yeon Park
- Department of Food Science and Engineering Ewha Womans University Seoul 03760 Republic of Korea
| | - Seongsoon Park
- Department of Chemistry, Center for NanoBio Applied Technology Sungshin Women's University Seoul 01133 Republic of Korea
| | - Deok‐Kun Oh
- Department of Bioscience and Biotechnology Konkuk University Seoul 05029 Republic of Korea
| | - Uwe Bornscheuer
- Institute of Biochemistry, Department of Biotechnology & Enzyme Catalysis Greifswald University Greifswald 17487 Germany
| | - Jin‐Byung Park
- Department of Food Science and Engineering Ewha Womans University Seoul 03760 Republic of Korea
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25
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Protein-Protein Interactions: Insight from Molecular Dynamics Simulations and Nanoparticle Tracking Analysis. Molecules 2021; 26:molecules26185696. [PMID: 34577167 PMCID: PMC8472368 DOI: 10.3390/molecules26185696] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/13/2021] [Accepted: 09/16/2021] [Indexed: 11/23/2022] Open
Abstract
Protein-protein interaction plays an essential role in almost all cellular processes and biological functions. Coupling molecular dynamics (MD) simulations and nanoparticle tracking analysis (NTA) assay offered a simple, rapid, and direct approach in monitoring the protein-protein binding process and predicting the binding affinity. Our case study of designed ankyrin repeats proteins (DARPins)—AnkGAG1D4 and the single point mutated AnkGAG1D4-Y56A for HIV-1 capsid protein (CA) were investigated. As reported, AnkGAG1D4 bound with CA for inhibitory activity; however, it lost its inhibitory strength when tyrosine at residue 56 AnkGAG1D4, the most key residue was replaced by alanine (AnkGAG1D4-Y56A). Through NTA, the binding of DARPins and CA was measured by monitoring the increment of the hydrodynamic radius of the AnkGAG1D4-gold conjugated nanoparticles (AnkGAG1D4-GNP) and AnkGAG1D4-Y56A-GNP upon interaction with CA in buffer solution. The size of the AnkGAG1D4-GNP increased when it interacted with CA but not AnkGAG1D4-Y56A-GNP. In addition, a much higher binding free energy (∆GB) of AnkGAG1D4-Y56A (−31 kcal/mol) obtained from MD further suggested affinity for CA completely reduced compared to AnkGAG1D4 (−60 kcal/mol). The possible mechanism of the protein-protein binding was explored in detail by decomposing the binding free energy for crucial residues identification and hydrogen bond analysis.
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26
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Scherer M, Fleishman SJ, Jones PR, Dandekar T, Bencurova E. Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals. Front Bioeng Biotechnol 2021; 9:673005. [PMID: 34211966 PMCID: PMC8239229 DOI: 10.3389/fbioe.2021.673005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 05/06/2021] [Indexed: 11/13/2022] Open
Abstract
To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO2 or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.
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Affiliation(s)
- Marc Scherer
- Department of Bioinformatics, Julius-Maximilians University of Würzburg, Würzburg, Germany
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Patrik R Jones
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Thomas Dandekar
- Department of Bioinformatics, Julius-Maximilians University of Würzburg, Würzburg, Germany
| | - Elena Bencurova
- Department of Bioinformatics, Julius-Maximilians University of Würzburg, Würzburg, Germany
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27
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Nikulin M, Švedas V. Prospects of Using Biocatalysis for the Synthesis and Modification of Polymers. Molecules 2021; 26:2750. [PMID: 34067052 PMCID: PMC8124709 DOI: 10.3390/molecules26092750] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/30/2021] [Accepted: 05/04/2021] [Indexed: 11/16/2022] Open
Abstract
Trends in the dynamically developing application of biocatalysis for the synthesis and modification of polymers over the past 5 years are considered, with an emphasis on the production of biodegradable, biocompatible and functional polymeric materials oriented to medical applications. The possibilities of using enzymes not only as catalysts for polymerization but also for the preparation of monomers for polymerization or oligomers for block copolymerization are considered. Special attention is paid to the prospects and existing limitations of biocatalytic production of new synthetic biopolymers based on natural compounds and monomers from biomass, which can lead to a huge variety of functional biomaterials. The existing experience and perspectives for the integration of bio- and chemocatalysis in this area are discussed.
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Affiliation(s)
- Maksim Nikulin
- Belozersky Institute of Physicochemical Biology, Lomonosov Moscow State University, Lenin Hills 1, bldg. 40, 119991 Moscow, Russia;
| | - Vytas Švedas
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Lenin Hills 1, bldg. 73, 119991 Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Lenin Hills 1, bldg. 4, 119991 Moscow, Russia
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28
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Marques SM, Planas-Iglesias J, Damborsky J. Web-based tools for computational enzyme design. Curr Opin Struct Biol 2021; 69:19-34. [PMID: 33667757 DOI: 10.1016/j.sbi.2021.01.010] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 01/14/2021] [Accepted: 01/27/2021] [Indexed: 12/30/2022]
Abstract
Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.
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Affiliation(s)
- Sérgio M Marques
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/C13, 625 00 Brno, Czech Republic; International Centre for Clinical Research, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Joan Planas-Iglesias
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/C13, 625 00 Brno, Czech Republic; International Centre for Clinical Research, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/C13, 625 00 Brno, Czech Republic; International Centre for Clinical Research, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic.
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29
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Rapp LR, Marques SM, Zukic E, Rowlinson B, Sharma M, Grogan G, Damborsky J, Hauer B. Substrate Anchoring and Flexibility Reduction in CYP153A M.aq Leads to Highly Improved Efficiency toward Octanoic Acid. ACS Catal 2021. [DOI: 10.1021/acscatal.0c05193] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lea R. Rapp
- Institute of Biochemistry and Technical Biochemistry, Department of Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Sérgio M. Marques
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic
- International Centre for Clinical Research, St. Anne’s University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Erna Zukic
- York Structural Biology Laboratory, Department of Chemistry, University of York, Heslington, York YO10 5DD, U.K
| | - Benjamin Rowlinson
- York Structural Biology Laboratory, Department of Chemistry, University of York, Heslington, York YO10 5DD, U.K
| | - Mahima Sharma
- York Structural Biology Laboratory, Department of Chemistry, University of York, Heslington, York YO10 5DD, U.K
| | - Gideon Grogan
- York Structural Biology Laboratory, Department of Chemistry, University of York, Heslington, York YO10 5DD, U.K
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic
- International Centre for Clinical Research, St. Anne’s University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Bernhard Hauer
- Institute of Biochemistry and Technical Biochemistry, Department of Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
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30
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Timonina D, Sharapova Y, Švedas V, Suplatov D. Bioinformatic analysis of subfamily-specific regions in 3D-structures of homologs to study functional diversity and conformational plasticity in protein superfamilies. Comput Struct Biotechnol J 2021; 19:1302-1311. [PMID: 33738079 PMCID: PMC7933735 DOI: 10.1016/j.csbj.2021.02.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 02/07/2023] Open
Abstract
Local 3D-structural differences in homologous proteins contribute to functional diversity observed in a superfamily, but so far received little attention as bioinformatic analysis was usually carried out at the level of amino acid sequences. We have developed Zebra3D - the first-of-its-kind bioinformatic software for systematic analysis of 3D-alignments of protein families using machine learning. The new tool identifies subfamily-specific regions (SSRs) - patterns of local 3D-structure (i.e. single residues, loops, or secondary structure fragments) that are spatially equivalent within families/subfamilies, but are different among them, and thus can be associated with functional diversity and function-related conformational plasticity. Bioinformatic analysis of protein superfamilies by Zebra3D can be used to study 3D-determinants of catalytic activity and specific accommodation of ligands, help to prepare focused libraries for directed evolution or assist development of chimeric enzymes with novel properties by exchange of equivalent regions between homologs, and to characterize plasticity in binding sites. A companion Mustguseal web-server is available to automatically construct a 3D-alignment of functionally diverse proteins, thus reducing the minimal input required to operate Zebra3D to a single PDB code. The Zebra3D + Mustguseal combined approach provides the opportunity to systematically explore the value of SSRs in superfamilies and to use this information for protein design and drug discovery. The software is available open-access at https://biokinet.belozersky.msu.ru/Zebra3D.
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Affiliation(s)
- Daria Timonina
- Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Lenin Hills 1-73, Moscow 119234, Russia
| | - Yana Sharapova
- Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Lenin Hills 1-73, Moscow 119234, Russia
- Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Lenin Hills 1-73, Moscow 119234, Russia
| | - Vytas Švedas
- Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Lenin Hills 1-73, Moscow 119234, Russia
- Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Lenin Hills 1-73, Moscow 119234, Russia
| | - Dmitry Suplatov
- Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Lenin Hills 1-73, Moscow 119234, Russia
- Corresponding author.
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31
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Stourac J, Dubrava J, Musil M, Horackova J, Damborsky J, Mazurenko S, Bednar D. FireProtDB: database of manually curated protein stability data. Nucleic Acids Res 2021; 49:D319-D324. [PMID: 33166383 PMCID: PMC7778887 DOI: 10.1093/nar/gkaa981] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/18/2020] [Accepted: 10/12/2020] [Indexed: 01/13/2023] Open
Abstract
The majority of naturally occurring proteins have evolved to function under mild conditions inside the living organisms. One of the critical obstacles for the use of proteins in biotechnological applications is their insufficient stability at elevated temperatures or in the presence of salts. Since experimental screening for stabilizing mutations is typically laborious and expensive, in silico predictors are often used for narrowing down the mutational landscape. The recent advances in machine learning and artificial intelligence further facilitate the development of such computational tools. However, the accuracy of these predictors strongly depends on the quality and amount of data used for training and testing, which have often been reported as the current bottleneck of the approach. To address this problem, we present a novel database of experimental thermostability data for single-point mutants FireProtDB. The database combines the published datasets, data extracted manually from the recent literature, and the data collected in our laboratory. Its user interface is designed to facilitate both types of the expected use: (i) the interactive explorations of individual entries on the level of a protein or mutation and (ii) the construction of highly customized and machine learning-friendly datasets using advanced searching and filtering. The database is freely available at https://loschmidt.chemi.muni.cz/fireprotdb.
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Affiliation(s)
- Jan Stourac
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Juraj Dubrava
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic.,Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
| | - Milos Musil
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic.,Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
| | - Jana Horackova
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
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32
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Yan W, Yu C, Chen J, Zhou J, Shen B. ANCA: A Web Server for Amino Acid Networks Construction and Analysis. Front Mol Biosci 2020; 7:582702. [PMID: 33330622 PMCID: PMC7711068 DOI: 10.3389/fmolb.2020.582702] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 10/19/2020] [Indexed: 02/05/2023] Open
Abstract
Amino acid network (AAN) models empower us to gain insights into protein structures and functions by describing a protein 3D structure as a graph, where nodes represent residues and edges as amino acid interactions. Here, we present the ANCA, an interactive Web server for Amino Acids Network Construction and Analysis based on a single structure or a set of structures from the Protein Data Bank. The main purpose of ANCA is to provide a portal for three types of an environment-dependent residue contact energy (ERCE)-based network model, including amino acid contact energy network (AACEN), node-weighted amino acid contact energy network (NACEN), and edge-weighted amino acid contact energy network (EACEN). For comparison, the C-alpha distance-based network model is also included, which can be extended to protein–DNA/RNA complexes. Then, the analyses of different types of AANs were performed and compared from node, edge, and network levels. The network and corresponding structure can be visualized directly in the browser. The ANCA enables researchers to investigate diverse concerns in the framework of AAN, such as the interpretation of allosteric regulation and functional residues. The ANCA portal, together with an extensive help, is available at http://sysbio.suda.edu.cn/anca/.
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Affiliation(s)
- Wenying Yan
- Center for Systems Biology, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Chunjiang Yu
- School of Biotechnology, Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, China
| | - Jiajia Chen
- School of Chemistry, Biology and Materials Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Jianhong Zhou
- Public Library of Science, San Francisco, CA, United States
| | - Bairong Shen
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
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