1
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Koch NG, Budisa N. Evolution of Pyrrolysyl-tRNA Synthetase: From Methanogenesis to Genetic Code Expansion. Chem Rev 2024. [PMID: 38953775 DOI: 10.1021/acs.chemrev.4c00031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Over 20 years ago, the pyrrolysine encoding translation system was discovered in specific archaea. Our Review provides an overview of how the once obscure pyrrolysyl-tRNA synthetase (PylRS) tRNA pair, originally responsible for accurately translating enzymes crucial in methanogenic metabolic pathways, laid the foundation for the burgeoning field of genetic code expansion. Our primary focus is the discussion of how to successfully engineer the PylRS to recognize new substrates and exhibit higher in vivo activity. We have compiled a comprehensive list of ncAAs incorporable with the PylRS system. Additionally, we also summarize recent successful applications of the PylRS system in creating innovative therapeutic solutions, such as new antibody-drug conjugates, advancements in vaccine modalities, and the potential production of new antimicrobials.
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Affiliation(s)
- Nikolaj G Koch
- Department of Chemistry, Institute of Physical Chemistry, University of Basel, 4058 Basel, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
| | - Nediljko Budisa
- Biocatalysis Group, Institute of Chemistry, Technische Universität Berlin, 10623 Berlin, Germany
- Chemical Synthetic Biology Chair, Department of Chemistry, University of Manitoba, Winnipeg MB R3T 2N2, Canada
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2
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Han Y, Zhang H, Zeng Z, Liu Z, Lu D, Liu Z. Descriptor-augmented machine learning for enzyme-chemical interaction predictions. Synth Syst Biotechnol 2024; 9:259-268. [PMID: 38450325 PMCID: PMC10915406 DOI: 10.1016/j.synbio.2024.02.006] [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: 11/01/2023] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/08/2024] Open
Abstract
Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals, as they could characterize enzymes and chemicals from the physicochemical and evolutionary perspective. This study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships prediction. We curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold cross-validation. The influence of protein and chemical descriptors was assessed in three scenarios, which were predicting the activity of unknown relations between known enzymes and known chemicals (new relationship evaluation), predicting the activity of novel enzymes on known chemicals (new enzyme evaluation), and predicting the activity of new chemicals on known enzymes (new chemical evaluation). The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes, whereas chemical descriptors appear no effect. A variety of sequence-based and structure-based protein descriptors were constructed, among which the esm-2 descriptor achieved the best results. Using enzyme families as labels showed that descriptors could cluster proteins well, which could explain the contributions of descriptors to the machine learning model. As a counterpart, in the new chemical evaluation, chemical descriptors made significant improvement in four out of the seven datasets, while protein descriptors appear no effect. We attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the models. The results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein descriptor strategy. This work provides guidance for the development of machine learning models for specific enzyme families.
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Affiliation(s)
- Yilei Han
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
| | - Haoye Zhang
- Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Zheni Zeng
- Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Zhiyuan Liu
- Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Diannan Lu
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
| | - Zheng Liu
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
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3
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Pham C, Nasr MA, Skarina T, Di Leo R, Kwan DH, Martin VJJ, Stogios PJ, Mahadevan R, Savchenko A. Functional and structural characterization of an IclR family transcription factor for the development of dicarboxylic acid biosensors. FEBS J 2024. [PMID: 38696354 DOI: 10.1111/febs.17149] [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: 12/15/2023] [Revised: 03/15/2024] [Accepted: 04/17/2024] [Indexed: 05/04/2024]
Abstract
Prokaryotic transcription factors (TFs) regulate gene expression in response to small molecules, thus representing promising candidates as versatile small molecule-detecting biosensors valuable for synthetic biology applications. The engineering of such biosensors requires thorough in vitro and in vivo characterization of TF ligand response as well as detailed molecular structure information. In this work, we functionally and structurally characterize the Pca regulon regulatory protein (PcaR) transcription factor belonging to the IclR transcription factor family. Here, we present in vitro functional analysis of the ligand profile of PcaR and the construction of genetic circuits for the characterization of PcaR as an in vivo biosensor in the model eukaryote Saccharomyces cerevisiae. We report the crystal structures of PcaR in the apo state and in complex with one of its ligands, succinate, which suggests the mechanism of dicarboxylic acid recognition by this transcription factor. This work contributes key structural and functional insights enabling the engineering of PcaR for dicarboxylic acid biosensors, in addition to providing more insights into the IclR family of regulators.
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Affiliation(s)
- Chester Pham
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada
| | - Mohamed A Nasr
- Centre for Applied Synthetic Biology, Concordia University, Montreal, Canada
- Department of Biology, Concordia University, Montreal, Canada
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Canada
| | - Tatiana Skarina
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada
| | - Rosa Di Leo
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada
| | - David H Kwan
- Centre for Applied Synthetic Biology, Concordia University, Montreal, Canada
- Department of Biology, Concordia University, Montreal, Canada
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Canada
- Department of Chemistry and Biochemistry, Concordia University, Montreal, Canada
| | - Vincent J J Martin
- Centre for Applied Synthetic Biology, Concordia University, Montreal, Canada
- Department of Biology, Concordia University, Montreal, Canada
| | - Peter J Stogios
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada
- The Institute of Biomedical Engineering, University of Toronto, Canada
| | - Alexei Savchenko
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Canada
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4
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Listov D, Goverde CA, Correia BE, Fleishman SJ. Opportunities and challenges in design and optimization of protein function. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00718-y. [PMID: 38565617 DOI: 10.1038/s41580-024-00718-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.
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Affiliation(s)
- Dina Listov
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Casper A Goverde
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Sarel Jacob Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel.
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5
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Zheng J, Guo N, Huang Y, Guo X, Wagner A. High temperature delays and low temperature accelerates evolution of a new protein phenotype. Nat Commun 2024; 15:2495. [PMID: 38553445 PMCID: PMC10980763 DOI: 10.1038/s41467-024-46332-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 02/19/2024] [Indexed: 04/02/2024] Open
Abstract
Since the origin of life, temperatures on earth have fluctuated both on short and long time scales. How such changes affect the rate at which Darwinian evolution can bring forth new phenotypes remains unclear. On the one hand, high temperature may accelerate phenotypic evolution because it accelerates most biological processes. On the other hand, it may slow phenotypic evolution, because proteins are usually less stable at high temperatures and therefore less evolvable. Here, to test these hypotheses experimentally, we evolved a green fluorescent protein in E. coli towards the new phenotype of yellow fluorescence at different temperatures. Yellow fluorescence evolved most slowly at high temperature and most rapidly at low temperature, in contradiction to the first hypothesis. Using high-throughput population sequencing, protein engineering, and biochemical assays, we determined that this is due to the protein-destabilizing effect of neofunctionalizing mutations. Destabilization is highly detrimental at high temperature, where neofunctionalizing mutations cannot be tolerated. Their detrimental effects can be mitigated through excess stability at low temperature, leading to accelerated adaptive evolution. By modifying protein folding stability, temperature alters the accessibility of mutational paths towards high-fitness genotypes. Our observations have broad implications for our understanding of how temperature changes affect evolutionary adaptations and innovations.
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Affiliation(s)
- Jia Zheng
- Zhejiang Key Laboratory of Structural Biology, School of Life Sciences, Westlake University, Hangzhou, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, China.
| | - Ning Guo
- Zhejiang Key Laboratory of Structural Biology, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Yuxiang Huang
- Zhejiang Key Laboratory of Structural Biology, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Xiang Guo
- Zhejiang Key Laboratory of Structural Biology, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- The Santa Fe Institute, Santa Fe, USA.
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6
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d'Oelsnitz S, Stofel SK, Love JD, Ellington AD. Snowprint: a predictive tool for genetic biosensor discovery. Commun Biol 2024; 7:163. [PMID: 38336860 PMCID: PMC10858194 DOI: 10.1038/s42003-024-05849-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates genetic design. Here we present Snowprint, a simple yet powerful bioinformatic tool for predicting regulator:operator interactions. Benchmarking results demonstrate that Snowprint predictions are significantly similar for >45% of experimentally validated regulator:operator pairs from organisms across nine phyla and for regulators that span five distinct structural families. We then use Snowprint to design promoters for 33 previously uncharacterized regulators sourced from diverse phylogenies, of which 28 are shown to influence gene expression and 24 produce a >20-fold dynamic range. A panel of the newly repurposed regulators are then screened for response to biomanufacturing-relevant compounds, yielding new sensors for a polyketide (olivetolic acid), terpene (geraniol), steroid (ursodiol), and alkaloid (tetrahydropapaverine) with induction ratios up to 10.7-fold. Snowprint represents a unique, protein-agnostic tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications. A web-accessible version of Snowprint is available at https://snowprint.groov.bio .
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Affiliation(s)
- Simon d'Oelsnitz
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
- Synthetic Biology HIVE, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.
| | - Sarah K Stofel
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA
| | - Joshua D Love
- Independent Web Developer, Bentonville, AR, 72712, USA
| | - Andrew D Ellington
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA
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7
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Joho Y, Vongsouthi V, Gomez C, Larsen JS, Ardevol A, Jackson CJ. Improving plastic degrading enzymes via directed evolution. Protein Eng Des Sel 2024; 37:gzae009. [PMID: 38713696 PMCID: PMC11091475 DOI: 10.1093/protein/gzae009] [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: 12/15/2023] [Revised: 04/30/2024] [Accepted: 05/05/2024] [Indexed: 05/09/2024] Open
Abstract
Plastic degrading enzymes have immense potential for use in industrial applications. Protein engineering efforts over the last decade have resulted in considerable enhancement of many properties of these enzymes. Directed evolution, a protein engineering approach that mimics the natural process of evolution in a laboratory, has been particularly useful in overcoming some of the challenges of structure-based protein engineering. For example, directed evolution has been used to improve the catalytic activity and thermostability of polyethylene terephthalate (PET)-degrading enzymes, although its use for the improvement of other desirable properties, such as solvent tolerance, has been less studied. In this review, we aim to identify some of the knowledge gaps and current challenges, and highlight recent studies related to the directed evolution of plastic-degrading enzymes.
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Affiliation(s)
- Yvonne Joho
- Manufacturing, Commonwealth Scientific and Industrial Research Organisation, Research Way, Clayton, Victoria 3168, Australia
- Research School of Chemistry, Australian National University, Sullivans Creek Rd, Canberra, ACT 2601, Australia
- CSIRO Advanced Engineering Biology Future Science Platform, GPO Box 1700, Canberra, ACT 2601, Australia
| | - Vanessa Vongsouthi
- Research School of Chemistry, Australian National University, Sullivans Creek Rd, Canberra, ACT 2601, Australia
| | - Chloe Gomez
- Research School of Chemistry, Australian National University, Sullivans Creek Rd, Canberra, ACT 2601, Australia
| | - Joachim S Larsen
- Research School of Chemistry, Australian National University, Sullivans Creek Rd, Canberra, ACT 2601, Australia
- ARC Centre of Excellence for Synthetic Biology, Research School of Chemistry, Australian National University, Sullivans Creek Rd, Canberra, ACT 2601, Australia
| | - Albert Ardevol
- Manufacturing, Commonwealth Scientific and Industrial Research Organisation, Research Way, Clayton, Victoria 3168, Australia
- CSIRO Advanced Engineering Biology Future Science Platform, GPO Box 1700, Canberra, ACT 2601, Australia
| | - Colin J Jackson
- Research School of Chemistry, Australian National University, Sullivans Creek Rd, Canberra, ACT 2601, Australia
- ARC Centre of Excellence for Synthetic Biology, Research School of Chemistry, Australian National University, Sullivans Creek Rd, Canberra, ACT 2601, Australia
- ARC Centre of Excellence for Innovations in Peptide & Protein Science, Research School of Chemistry, Australian National University, Sullivans Creek Rd, Canberra, ACT 2601, Australia
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8
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Xi C, Diao J, Moon TS. Advances in ligand-specific biosensing for structurally similar molecules. Cell Syst 2023; 14:1024-1043. [PMID: 38128482 PMCID: PMC10751988 DOI: 10.1016/j.cels.2023.10.009] [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: 05/21/2023] [Revised: 08/23/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.
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Affiliation(s)
- Chenggang Xi
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Jinjin Diao
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Tae Seok Moon
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO, USA.
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9
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Kunka A, Marques SM, Havlasek M, Vasina M, Velatova N, Cengelova L, Kovar D, Damborsky J, Marek M, Bednar D, Prokop Z. Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning. ACS Catal 2023; 13:12506-12518. [PMID: 37822856 PMCID: PMC10563018 DOI: 10.1021/acscatal.3c02575] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/24/2023] [Indexed: 10/13/2023]
Abstract
Thermostability is an essential requirement for the use of enzymes in the bioindustry. Here, we compare different protein stabilization strategies using a challenging target, a stable haloalkane dehalogenase DhaA115. We observe better performance of automated stabilization platforms FireProt and PROSS in designing multiple-point mutations over the introduction of disulfide bonds and strengthening the intra- and the inter-domain contacts by in silico saturation mutagenesis. We reveal that the performance of automated stabilization platforms was still compromised due to the introduction of some destabilizing mutations. Notably, we show that their prediction accuracy can be improved by applying manual curation or machine learning for the removal of potentially destabilizing mutations, yielding highly stable haloalkane dehalogenases with enhanced catalytic properties. A comparison of crystallographic structures revealed that current stabilization rounds were not accompanied by large backbone re-arrangements previously observed during the engineering stability of DhaA115. Stabilization was achieved by improving local contacts including protein-water interactions. Our study provides guidance for further improvement of automated structure-based computational tools for protein stabilization.
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Affiliation(s)
- Antonin Kunka
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - Sérgio M. Marques
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - Martin Havlasek
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
| | - Michal Vasina
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - Nikola Velatova
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
| | - Lucia Cengelova
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
| | - David Kovar
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - Jiri Damborsky
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - Martin Marek
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - David Bednar
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - Zbynek Prokop
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
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10
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Buda K, Miton CM, Fan XC, Tokuriki N. Molecular determinants of protein evolvability. Trends Biochem Sci 2023; 48:751-760. [PMID: 37330341 DOI: 10.1016/j.tibs.2023.05.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/18/2023] [Accepted: 05/23/2023] [Indexed: 06/19/2023]
Abstract
The plethora of biological functions that sustain life is rooted in the remarkable evolvability of proteins. An emerging view highlights the importance of a protein's initial state in dictating evolutionary success. A deeper comprehension of the mechanisms that govern the evolvability of these initial states can provide invaluable insights into protein evolution. In this review, we describe several molecular determinants of protein evolvability, unveiled by experimental evolution and ancestral sequence reconstruction studies. We further discuss how genetic variation and epistasis can promote or constrain functional innovation and suggest putative underlying mechanisms. By establishing a clear framework for these determinants, we provide potential indicators enabling the forecast of suitable evolutionary starting points and delineate molecular mechanisms in need of deeper exploration.
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Affiliation(s)
- Karol Buda
- Michael Smith Laboratories, University of British Columbia, Vancouver, Canada
| | - Charlotte M Miton
- Michael Smith Laboratories, University of British Columbia, Vancouver, Canada
| | - Xingyu Cara Fan
- Michael Smith Laboratories, University of British Columbia, Vancouver, Canada
| | - Nobuhiko Tokuriki
- Michael Smith Laboratories, University of British Columbia, Vancouver, Canada.
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11
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Nasr MA, Martin VJJ, Kwan DH. Divergent directed evolution of a TetR-type repressor towards aromatic molecules. Nucleic Acids Res 2023; 51:7675-7690. [PMID: 37377432 PMCID: PMC10415137 DOI: 10.1093/nar/gkad503] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 05/18/2023] [Accepted: 06/25/2023] [Indexed: 06/29/2023] Open
Abstract
Reprogramming cellular behaviour is one of the hallmarks of synthetic biology. To this end, prokaryotic allosteric transcription factors (aTF) have been repurposed as versatile tools for processing small molecule signals into cellular responses. Expanding the toolbox of aTFs that recognize new inducer molecules is of considerable interest in many applications. Here, we first establish a resorcinol responsive aTF-based biosensor in Escherichia coli using the TetR-family repressor RolR from Corynebacterium glutamicum. We then perform an iterative walk along the fitness landscape of RolR to identify new inducer specificities, namely catechol, methyl catechol, caffeic acid, protocatechuate, L-DOPA, and the tumour biomarker homovanillic acid. Finally, we demonstrate the versatility of these engineered aTFs by transplanting them into the model eukaryote Saccharomyces cerevisiae. This work provides a framework for efficient aTF engineering to expand ligand specificity towards novel molecules on laboratory timescales, which, more broadly, is invaluable across a wide range of applications such as protein and metabolic engineering, as well as point-of-care diagnostics.
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Affiliation(s)
- Mohamed A Nasr
- Centre for Applied Synthetic Biology, Concordia University, Montréal, Québec, Canada
- Department of Biology, Concordia University, Montréal, Québec, Canada
- PROTEO, Québec Network for Research on Protein Function, Structure, and Engineering, Québec City, Québec, Canada
| | - Vincent J J Martin
- Centre for Applied Synthetic Biology, Concordia University, Montréal, Québec, Canada
- Department of Biology, Concordia University, Montréal, Québec, Canada
| | - David H Kwan
- Centre for Applied Synthetic Biology, Concordia University, Montréal, Québec, Canada
- Department of Biology, Concordia University, Montréal, Québec, Canada
- Department of Chemistry and Biochemistry, Concordia University, Montréal, Québec, Canada
- PROTEO, Québec Network for Research on Protein Function, Structure, and Engineering, Québec City, Québec, Canada
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12
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Wijker S, Palmans ARA. Protein-Inspired Control over Synthetic Polymer Folding for Structured Functional Nanoparticles in Water. Chempluschem 2023; 88:e202300260. [PMID: 37417828 DOI: 10.1002/cplu.202300260] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/06/2023] [Accepted: 07/06/2023] [Indexed: 07/08/2023]
Abstract
The folding of proteins into functional nanoparticles with defined 3D structures has inspired chemists to create simple synthetic systems mimicking protein properties. The folding of polymers into nanoparticles in water proceeds via different strategies, resulting in the global compaction of the polymer chain. Herein, we review the different methods available to control the conformation of synthetic polymers and collapse/fold them into structured, functional nanoparticles, such as hydrophobic collapse, supramolecular self-assembly, and covalent cross-linking. A comparison is made between the design principles of protein folding to synthetic polymer folding and the formation of structured nanocompartments in water, highlighting similarities and differences in design and function. We also focus on the importance of structure for functional stability and diverse applications in complex media and cellular environments.
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Affiliation(s)
- Stefan Wijker
- Institute for Complex Molecular Systems, Laboratory of Macromolecular and Organic Chemistry, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands
| | - Anja R A Palmans
- Institute for Complex Molecular Systems, Laboratory of Macromolecular and Organic Chemistry, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands
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13
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Weinstein JY, Martí-Gómez C, Lipsh-Sokolik R, Hoch SY, Liebermann D, Nevo R, Weissman H, Petrovich-Kopitman E, Margulies D, Ivankov D, McCandlish DM, Fleishman SJ. Designed active-site library reveals thousands of functional GFP variants. Nat Commun 2023; 14:2890. [PMID: 37210560 DOI: 10.1038/s41467-023-38099-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 04/13/2023] [Indexed: 05/22/2023] Open
Abstract
Mutations in a protein active site can lead to dramatic and useful changes in protein activity. The active site, however, is sensitive to mutations due to a high density of molecular interactions, substantially reducing the likelihood of obtaining functional multipoint mutants. We introduce an atomistic and machine-learning-based approach, called high-throughput Functional Libraries (htFuncLib), that designs a sequence space in which mutations form low-energy combinations that mitigate the risk of incompatible interactions. We apply htFuncLib to the GFP chromophore-binding pocket, and, using fluorescence readout, recover >16,000 unique designs encoding as many as eight active-site mutations. Many designs exhibit substantial and useful diversity in functional thermostability (up to 96 °C), fluorescence lifetime, and quantum yield. By eliminating incompatible active-site mutations, htFuncLib generates a large diversity of functional sequences. We envision that htFuncLib will be used in one-shot optimization of activity in enzymes, binders, and other proteins.
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Affiliation(s)
| | - Carlos Martí-Gómez
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Rosalie Lipsh-Sokolik
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Shlomo Yakir Hoch
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Demian Liebermann
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Reinat Nevo
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Haim Weissman
- Department of Molecular Chemistry and Materials Science, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | | | - David Margulies
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Dmitry Ivankov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - David M McCandlish
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel.
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14
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Gomez de Santos P, Mateljak I, Hoang MD, Fleishman SJ, Hollmann F, Alcalde M. Repertoire of Computationally Designed Peroxygenases for Enantiodivergent C-H Oxyfunctionalization Reactions. J Am Chem Soc 2023; 145:3443-3453. [PMID: 36689349 PMCID: PMC9936548 DOI: 10.1021/jacs.2c11118] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
The generation of enantiodivergent biocatalysts for C-H oxyfunctionalizations is ever more important in modern synthetic chemistry. Here, we have applied the FuncLib algorithm based on phylogenetic and Rosetta calculations to design a diverse repertoire of active, stable, and enantiodivergent fungal peroxygenases. 24 designs, each carrying 4-5 mutations in the catalytic core, were expressed functionally in yeast and benchmarked against characteristic model compounds. Several designs were active and stable in a range of temperature and pH, displaying unprecedented enantiodivergence, changing regioselectivity from alkyl to aromatic hydroxylation, and increasing catalytic efficiencies up to 10-fold, with 15-fold improvements in total turnover numbers over the parental enzyme. We find that this dramatic functional divergence stems from beneficial epistasis among the mutations and an extensive reorganization of the heme channel. Our work demonstrates that FuncLib can rapidly design highly functional libraries enriched in enantioselective peroxygenases not seen in nature for a range of biotechnological applications.
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Affiliation(s)
- Patricia Gomez de Santos
- Department
of Biocatalysis, Institute of Catalysis, ICP-CSIC, C/ Marie Curie
2, 28049 Madrid, Spain,EvoEnzyme
S.L., Parque Científico de Madrid, C/ Faraday 7, 28049 Madrid, Spain
| | - Ivan Mateljak
- EvoEnzyme
S.L., Parque Científico de Madrid, C/ Faraday 7, 28049 Madrid, Spain
| | - Manh Dat Hoang
- Department
of Biocatalysis, Institute of Catalysis, ICP-CSIC, C/ Marie Curie
2, 28049 Madrid, Spain,Chair
of Biochemical Engineering, Technical University
of Munich, Boltzmannstr. 15, 85748 Garching, Germany
| | - Sarel J. Fleishman
- Department
of Biomolecular Sciences, Weizmann Institute
of Science, 7610001 Rehovot, Israel
| | - Frank Hollmann
- Department
of Biotechnology, Delft University of Technology, van der Massweg 9, 2629HZ Delft, The Netherlands
| | - Miguel Alcalde
- Department
of Biocatalysis, Institute of Catalysis, ICP-CSIC, C/ Marie Curie
2, 28049 Madrid, Spain,
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15
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Miton CM, Tokuriki N. Insertions and Deletions (Indels): A Missing Piece of the Protein Engineering Jigsaw. Biochemistry 2023; 62:148-157. [PMID: 35830609 DOI: 10.1021/acs.biochem.2c00188] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Over the years, protein engineers have studied nature and borrowed its tricks to accelerate protein evolution in the test tube. While there have been considerable advances, our ability to generate new proteins in the laboratory is seemingly limited. One explanation for these shortcomings may be that insertions and deletions (indels), which frequently arise in nature, are largely overlooked during protein engineering campaigns. The profound effect of indels on protein structures, by way of drastic backbone alterations, could be perceived as "saltation" events that bring about significant phenotypic changes in a single mutational step. Should we leverage these effects to accelerate protein engineering and gain access to unexplored regions of adaptive landscapes? In this Perspective, we describe the role played by indels in the functional diversification of proteins in nature and discuss their untapped potential for protein engineering, despite their often-destabilizing nature. We hope to spark a renewed interest in indels, emphasizing that their wider study and use may prove insightful and shape the future of protein engineering by unlocking unique functional changes that substitutions alone could never achieve.
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Affiliation(s)
- Charlotte M Miton
- Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4 BC, Canada
| | - Nobuhiko Tokuriki
- Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4 BC, Canada
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16
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Clifton BE, Kozome D, Laurino P. Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design. Biochemistry 2023; 62:210-220. [PMID: 35245020 DOI: 10.1021/acs.biochem.1c00757] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
The rapid growth of sequence databases over the past two decades means that protein engineers faced with optimizing a protein for any given task will often have immediate access to a vast number of related protein sequences. These sequences encode information about the evolutionary history of the protein and the underlying sequence requirements to produce folded, stable, and functional protein variants. Methods that can take advantage of this information are an increasingly important part of the protein engineering tool kit. In this Perspective, we discuss the utility of sequence data in protein engineering and design, focusing on recent advances in three main areas: the use of ancestral sequence reconstruction as an engineering tool to generate thermostable and multifunctional proteins, the use of sequence data to guide engineering of multipoint mutants by structure-based computational protein design, and the use of unlabeled sequence data for unsupervised and semisupervised machine learning, allowing the generation of diverse and functional protein sequences in unexplored regions of sequence space. Altogether, these methods enable the rapid exploration of sequence space within regions enriched with functional proteins and therefore have great potential for accelerating the engineering of stable, functional, and diverse proteins for industrial and biomedical applications.
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Affiliation(s)
- Ben E Clifton
- Protein Engineering and Evolution Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna, Okinawa 904-0495, Japan
| | - Dan Kozome
- Protein Engineering and Evolution Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna, Okinawa 904-0495, Japan
| | - Paola Laurino
- Protein Engineering and Evolution Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna, Okinawa 904-0495, Japan
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17
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Kosonocky CW, Ellington AD. Evolving to Evolve, Dan Tawfik's Insights into Protein Engineering. Biochemistry 2023; 62:145-147. [PMID: 36647679 DOI: 10.1021/acs.biochem.2c00668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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18
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Ó'Fágáin C. Protein Stability: Enhancement and Measurement. Methods Mol Biol 2023; 2699:369-419. [PMID: 37647007 DOI: 10.1007/978-1-0716-3362-5_18] [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] [Indexed: 09/01/2023]
Abstract
This chapter defines protein stability, emphasizes its importance, and surveys the field of protein stabilization, with summary reference to a selection of 2014-2021 publications. One can enhance stability, particularly by protein engineering strategies but also by chemical modification and by other means. General protocols are set out on how to measure a given protein's (i) kinetic thermal stability and (ii) oxidative stability and (iii) how to undertake chemical modification of a protein in solution.
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Affiliation(s)
- Ciarán Ó'Fágáin
- School of Biotechnology, Dublin City University, Dublin, Ireland.
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19
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Wu T, Wang Y, Zhang N, Yin D, Xu Y, Nie Y, Mu X. Reshaping Substrate-Binding Pocket of Leucine Dehydrogenase for Bidirectionally Accessing Structurally Diverse Substrates. ACS Catal 2022. [DOI: 10.1021/acscatal.2c04735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Tao Wu
- Laboratory of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi214122, China
- Suqian Jiangnan University Institute of Industrial Technology, Suqian223800, China
| | - Yinmiao Wang
- Laboratory of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi214122, China
| | - Ningxin Zhang
- Laboratory of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi214122, China
| | - Dejing Yin
- Laboratory of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi214122, China
| | - Yan Xu
- Laboratory of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi214122, China
| | - Yao Nie
- Laboratory of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi214122, China
| | - Xiaoqing Mu
- Laboratory of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi214122, China
- Suqian Jiangnan University Institute of Industrial Technology, Suqian223800, China
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20
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Abstract
Chemical biosensors are an increasingly ubiquitous part of our lives. Beyond enzyme-coupled assays, recent synthetic biology advances now allow us to hijack more complex biosensing systems to respond to difficult to detect analytes, such as chemical small molecules. Here, we briefly overview recent advances in the biosensing of small molecules, including nucleic acid aptamers, allosteric transcription factors, and two-component systems. We then look more closely at a recently developed chemical sensing system, G protein-coupled receptor (GPCR)-based sensors. Finally, we consider the chemical sensing capabilities of the largest GPCR subfamily, olfactory receptors (ORs). We examine ORs' role in nature, their potential as a biomedical target, and their ability to detect compounds not amenable for detection using other biological scaffolds. We conclude by evaluating the current challenges, opportunities, and future applications of GPCR- and OR-based sensors.
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Affiliation(s)
- Amisha Patel
- School
of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Pamela Peralta-Yahya
- School
of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States,School
of Chemistry and Biochemistry, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States,E-mail:
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21
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Blazeck J, Karamitros CS, Ford K, Somody C, Qerqez A, Murray K, Burkholder NT, Marshall N, Sivakumar A, Lu WC, Tan B, Lamb C, Tanno Y, Siddiqui MY, Ashoura N, Coma S, Zhang XM, McGovern K, Kumada Y, Zhang YJ, Manfredi M, Johnson KA, D’Arcy S, Stone E, Georgiou G. Bypassing evolutionary dead ends and switching the rate-limiting step of a human immunotherapeutic enzyme. Nat Catal 2022; 5:952-967. [PMID: 36465553 PMCID: PMC9717613 DOI: 10.1038/s41929-022-00856-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 09/09/2022] [Indexed: 11/08/2022]
Abstract
The Trp metabolite kynurenine (KYN) accumulates in numerous solid tumours and mediates potent immunosuppression. Bacterial kynureninases (KYNases), which preferentially degrade kynurenine, can relieve immunosuppression in multiple cancer models, but immunogenicity concerns preclude their clinical use, while the human enzyme (HsKYNase) has very low activity for kynurenine and shows no therapeutic effect. Using fitness selections, we evolved a HsKYNase variant with 27-fold higher activity, beyond which exploration of >30 evolutionary trajectories involving the interrogation of >109 variants led to no further improvements. Introduction of two amino acid substitutions conserved in bacterial KYNases reduced enzyme fitness but potentiated rapid evolution of variants with ~500-fold improved activity and reversed substrate specificity, resulting in an enzyme capable of mediating strong anti-tumour effects in mice. Pre-steady-state kinetics revealed a switch in rate-determining step attributable to changes in both enzyme structure and conformational dynamics. Apart from its clinical significance, our work highlights how rationally designed substitutions can potentiate trajectories that overcome barriers in protein evolution.
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Affiliation(s)
- John Blazeck
- Department of Chemical Engineering, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Christos S. Karamitros
- Department of Chemical Engineering, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Kyle Ford
- Department of Chemical Engineering, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Catrina Somody
- Department of Chemical Engineering, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Ahlam Qerqez
- Department of Chemical Engineering, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Kyle Murray
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, Texas, USA
| | - Nathaniel T. Burkholder
- Department of Molecular Biosciences, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Nicholas Marshall
- Department of Chemical Engineering, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Anirudh Sivakumar
- Department of Chemical Engineering, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Wei-Cheng Lu
- Department of Chemical Engineering, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Bing Tan
- Department of Chemical Engineering, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Candice Lamb
- Department of Chemical Engineering, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Yuri Tanno
- Department of Chemical Engineering, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Menna Y. Siddiqui
- Department of Chemical Engineering, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Norah Ashoura
- Department of Molecular Biosciences, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Silvia Coma
- Ikena Oncology, Cambridge, Massachusetts, USA
| | | | | | - Yoichi Kumada
- Department of Molecular Chemistry and Engineering, Kyoto Institute of Technology, Kyoto, Japan
| | - Yan Jessie Zhang
- Department of Molecular Biosciences, University of Texas at Austin (UT Austin), Austin, Texas, USA
- Institute for Cellular and Molecular Biology, The University of Texas at Austin (UT Austin), Austin, Texas, USA
| | | | - Kenneth A. Johnson
- Department of Molecular Biosciences, University of Texas at Austin (UT Austin), Austin, Texas, USA
| | - Sheena D’Arcy
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, Texas, USA
| | - Everett Stone
- Department of Molecular Biosciences, University of Texas at Austin (UT Austin), Austin, Texas, USA
- Institute for Cellular and Molecular Biology, The University of Texas at Austin (UT Austin), Austin, Texas, USA
- Department of Oncology, University of Texas Dell Medical School, LiveSTRONG Cancer Institutes, Austin, Texas, USA
| | - George Georgiou
- Department of Chemical Engineering, University of Texas at Austin (UT Austin), Austin, Texas, USA
- Department of Molecular Biosciences, University of Texas at Austin (UT Austin), Austin, Texas, USA
- Institute for Cellular and Molecular Biology, The University of Texas at Austin (UT Austin), Austin, Texas, USA
- Department of Oncology, University of Texas Dell Medical School, LiveSTRONG Cancer Institutes, Austin, Texas, USA
- Department of Biomedical Engineering, University of Texas at Austin (UT Austin), Austin, TX, USA
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22
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Foley G, Mora A, Ross CM, Bottoms S, Sützl L, Lamprecht ML, Zaugg J, Essebier A, Balderson B, Newell R, Thomson RES, Kobe B, Barnard RT, Guddat L, Schenk G, Carsten J, Gumulya Y, Rost B, Haltrich D, Sieber V, Gillam EMJ, Bodén M. Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP). PLoS Comput Biol 2022; 18:e1010633. [PMID: 36279274 PMCID: PMC9632902 DOI: 10.1371/journal.pcbi.1010633] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 11/03/2022] [Accepted: 10/04/2022] [Indexed: 11/06/2022] Open
Abstract
Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.
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Affiliation(s)
- Gabriel Foley
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Ariane Mora
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Connie M. Ross
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Scott Bottoms
- Campus Straubing for Biotechnology and Sustainability, Technische Universität München, Straubing, Germany
| | - Leander Sützl
- Institut für Lebensmitteltechnologie, Universität für Bodenkultur Wien, Vienna, Austria
| | - Marnie L. Lamprecht
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Julian Zaugg
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Alexandra Essebier
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Brad Balderson
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Rhys Newell
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Raine E. S. Thomson
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Bostjan Kobe
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, The University of Queensland, Brisbane, Australia
| | - Ross T. Barnard
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Luke Guddat
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Gerhard Schenk
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Sustainable Minerals Institute, The University of Queensland, Brisbane, Australia
| | - Jörg Carsten
- Zentralinstitut für Katalyseforschung, Technische Universität München, Munich, Germany
| | - Yosephine Gumulya
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Burkhard Rost
- Fakultät für Informatik, Technische Universität München, Munich, Germany
| | - Dietmar Haltrich
- Institut für Lebensmitteltechnologie, Universität für Bodenkultur Wien, Vienna, Austria
| | - Volker Sieber
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Campus Straubing for Biotechnology and Sustainability, Technische Universität München, Straubing, Germany
- Zentralinstitut für Katalyseforschung, Technische Universität München, Munich, Germany
| | - Elizabeth M. J. Gillam
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- * E-mail: (MB); (EMJG)
| | - Mikael Bodén
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- * E-mail: (MB); (EMJG)
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23
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Tang QY, Ren W, Wang J, Kaneko K. The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database. Mol Biol Evol 2022; 39:6701686. [PMID: 36108094 PMCID: PMC9550990 DOI: 10.1093/molbev/msac197] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (AlphaFold DB), we perform comparative analyses of the proteins of different organisms. The statistics of AlphaFold-predicted structures show that, for organisms with higher complexity, their constituent proteins will have larger radii of gyration, higher coil fractions, and slower vibrations, statistically. By conducting normal mode analysis and scaling analyses, we demonstrate that higher organismal complexity correlates with lower fractal dimensions in both the structure and dynamics of the constituent proteins, suggesting that higher functional specialization is associated with higher organismal complexity. We also uncover the topology and sequence bases of these correlations. As the organismal complexity increases, the residue contact networks of the constituent proteins will be more assortative, and these proteins will have a higher degree of hydrophilic-hydrophobic segregation in the sequences. Furthermore, by comparing the statistical structural proximity across the proteomes with the phylogenetic tree of homologous proteins, we show that, statistical structural proximity across the proteomes may indirectly reflect the phylogenetic proximity, indicating a statistical trend of protein evolution in parallel with organism evolution. This study provides new insights into how the diversity in the functionality of proteins increases and how the dimensionality of the manifold of protein dynamics reduces during evolution, contributing to the understanding of the origin and evolution of lives.
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Affiliation(s)
| | - Weitong Ren
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Jun Wang
- School of Physics, National Laboratory of Solid State Microstructure, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, People’s Republic of China
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24
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Gutierrez-Rus LI, Alcalde M, Risso VA, Sanchez-Ruiz JM. Efficient Base-Catalyzed Kemp Elimination in an Engineered Ancestral Enzyme. Int J Mol Sci 2022; 23:ijms23168934. [PMID: 36012203 PMCID: PMC9408544 DOI: 10.3390/ijms23168934] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 11/25/2022] Open
Abstract
The routine generation of enzymes with completely new active sites is a major unsolved problem in protein engineering. Advances in this field have thus far been modest, perhaps due, at least in part, to the widespread use of modern natural proteins as scaffolds for de novo engineering. Most modern proteins are highly evolved and specialized and, consequently, difficult to repurpose for completely new functionalities. Conceivably, resurrected ancestral proteins with the biophysical properties that promote evolvability, such as high stability and conformational diversity, could provide better scaffolds for de novo enzyme generation. Kemp elimination, a non-natural reaction that provides a simple model of proton abstraction from carbon, has been extensively used as a benchmark in de novo enzyme engineering. Here, we present an engineered ancestral β-lactamase with a new active site that is capable of efficiently catalyzing Kemp elimination. The engineering of our Kemp eliminase involved minimalist design based on a single function-generating mutation, inclusion of an extra polypeptide segment at a position close to the de novo active site, and sharply focused, low-throughput library screening. Nevertheless, its catalytic parameters (kcat/KM~2·105 M−1 s−1, kcat~635 s−1) compare favorably with the average modern natural enzyme and match the best proton-abstraction de novo Kemp eliminases that are reported in the literature. The general implications of our results for de novo enzyme engineering are discussed.
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Affiliation(s)
- Luis I. Gutierrez-Rus
- Departamento de Quimica Fisica, Facultad de Ciencias, Unidad de Excelencia de Quimica Aplicada a Biomedicina y Medioambiente (UEQ), Universidad de Granada, 18071 Granada, Spain
| | - Miguel Alcalde
- Department of Biocatalysis, Institute of Catalysis and Petrochemistry, CSIC, Cantoblanco, 28049 Madrid, Spain
| | - Valeria A. Risso
- Departamento de Quimica Fisica, Facultad de Ciencias, Unidad de Excelencia de Quimica Aplicada a Biomedicina y Medioambiente (UEQ), Universidad de Granada, 18071 Granada, Spain
- Correspondence: (V.A.R.); (J.M.S.-R.)
| | - Jose M. Sanchez-Ruiz
- Departamento de Quimica Fisica, Facultad de Ciencias, Unidad de Excelencia de Quimica Aplicada a Biomedicina y Medioambiente (UEQ), Universidad de Granada, 18071 Granada, Spain
- Correspondence: (V.A.R.); (J.M.S.-R.)
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25
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Natural Evolution Provides Strong Hints about Laboratory Evolution of Designer Enzymes. Proc Natl Acad Sci U S A 2022; 119:e2207904119. [PMID: 35901204 PMCID: PMC9351539 DOI: 10.1073/pnas.2207904119] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
Laboratory evolution combined with computational enzyme design provides the opportunity to generate novel biocatalysts. Nevertheless, it has been challenging to understand how laboratory evolution optimizes designer enzymes by introducing seemingly random mutations. A typical enzyme optimized with laboratory evolution is the abiological Kemp eliminase, initially designed by grafting active site residues into a natural protein scaffold. Here, we relate the catalytic power of laboratory-evolved Kemp eliminases to the statistical energy ([Formula: see text]) inferred from their natural homologous sequences using the maximum entropy model. The [Formula: see text] of designs generated by directed evolution is correlated with enhanced activity and reduced stability, thus displaying a stability-activity trade-off. In contrast, the [Formula: see text] for mutants in catalytic-active remote regions (in which remote residues are important for catalysis) is strongly anticorrelated with the activity. These findings provide an insight into the role of protein scaffolds in the adaption to new enzymatic functions. It also indicates that the valley in the [Formula: see text] landscape can guide enzyme design for abiological catalysis. Overall, the connection between laboratory and natural evolution contributes to understanding what is optimized in the laboratory and how new enzymatic function emerges in nature, and provides guidance for computational enzyme design.
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Xu G, Poelarends GJ. Unlocking New Reactivities in Enzymes by Iminium Catalysis. Angew Chem Int Ed Engl 2022; 61:e202203613. [PMID: 35524737 PMCID: PMC9400869 DOI: 10.1002/anie.202203613] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Indexed: 12/11/2022]
Abstract
The application of biocatalysis in conquering challenging synthesis requires the constant input of new enzymes. Developing novel biocatalysts by absorbing catalysis modes from synthetic chemistry has yielded fruitful new-to-nature enzymes. Organocatalysis was originally bio-inspired and has become the third pillar of asymmetric catalysis. Transferring organocatalytic reactions back to enzyme platforms is a promising approach for biocatalyst creation. Herein, we summarize recent developments in the design of novel biocatalysts that adopt iminium catalysis, a fundamental branch in organocatalysis. By repurposing existing enzymes or constructing artificial enzymes, various biocatalysts for iminium catalysis have been created and optimized via protein engineering to promote valuable abiological transformations. Recent advances in iminium biocatalysis illustrate the power of combining chemomimetic biocatalyst design and directed evolution to generate useful new-to-nature enzymes.
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Affiliation(s)
- Guangcai Xu
- Department of Chemical and Pharmaceutical Biology, Groningen Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713, AV Groningen, The Netherlands
| | - Gerrit J Poelarends
- Department of Chemical and Pharmaceutical Biology, Groningen Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713, AV Groningen, The Netherlands
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d'Oelsnitz S, Kim W, Burkholder NT, Javanmardi K, Thyer R, Zhang Y, Alper HS, Ellington AD. Using fungible biosensors to evolve improved alkaloid biosyntheses. Nat Chem Biol 2022; 18:981-989. [PMID: 35799063 DOI: 10.1038/s41589-022-01072-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 05/26/2022] [Indexed: 12/25/2022]
Abstract
A key bottleneck in the microbial production of therapeutic plant metabolites is identifying enzymes that can improve yield. The facile identification of genetically encoded biosensors can overcome this limitation and become part of a general method for engineering scaled production. We have developed a combined screening and selection approach that quickly refines the affinities and specificities of generalist transcription factors; using RamR as a starting point, we evolve highly specific (>100-fold preference) and sensitive (half-maximum effective concentration (EC50) < 30 μM) biosensors for the alkaloids tetrahydropapaverine, papaverine, glaucine, rotundine and noscapine. High-resolution structures reveal multiple evolutionary avenues for the malleable effector-binding site and the creation of new pockets for different chemical moieties. These sensors further enabled the evolution of a streamlined pathway for tetrahydropapaverine, a precursor to four modern pharmaceuticals, collapsing multiple methylation steps into a single evolved enzyme. Our methods for evolving biosensors enable the rapid engineering of pathways for therapeutic alkaloids.
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Affiliation(s)
- Simon d'Oelsnitz
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA.
| | - Wantae Kim
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA
| | | | - Kamyab Javanmardi
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
| | - Ross Thyer
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, USA
| | - Yan Zhang
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
| | - Hal S Alper
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA
| | - Andrew D Ellington
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA.
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28
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Riziotis IG, Thornton JM. Capturing the geometry, function, and evolution of enzymes with 3D templates. Protein Sci 2022; 31:e4363. [PMID: 35762726 PMCID: PMC9207746 DOI: 10.1002/pro.4363] [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: 03/04/2022] [Revised: 05/06/2022] [Accepted: 05/14/2022] [Indexed: 11/05/2022]
Abstract
Structural templates are 3D signatures representing protein functional sites, such as ligand binding cavities, metal coordination motifs, or catalytic sites. Here we explore methods to generate template libraries and algorithms to query structures for conserved 3D motifs. Applications of templates are discussed, as well as some exemplar cases for examining evolutionary links in enzymes. We also introduce the concept of using more than one template per structure to represent flexible sites, as an approach to better understand catalysis through snapshots captured in enzyme structures. Functional annotation from structure is an important topic that has recently resurfaced due to the new more accurate methods of protein structure prediction. Therefore, we anticipate that template‐based functional site detection will be a powerful tool in the task of characterizing a vast number of new protein models.
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29
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Tripathi A, Dutta Dubey K. Combined MD and QM/MM Calculations Reveal Allostery-Driven Promiscuity in Dipeptide Epimerases of Enolase Family. Chem Asian J 2022; 17:e202200528. [PMID: 35722826 DOI: 10.1002/asia.202200528] [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: 05/23/2022] [Revised: 06/19/2022] [Indexed: 11/10/2022]
Abstract
The adaptability of the active site to amplify the secondary function is supposed to be the fundamental cause of the promiscuity and the evolution of new functions in enzymes. In most cases, mutations occur close to the active site and/or in the catalytic site to change the active site plasticity to accommodate the non-native substrate. In the present study, using MD simulations and hybrid QM/MM calculations, we have shown a way to enhance the promiscuity, i. e., the allostery-driven promiscuity. Using a case study of the AEE enzyme where the capping loop recognizes the substrate, herein, we show that a single site mutation (D321G) far from the capping loop can induce a large conformational change in the capping loop to recognize different substrates for different functions. The QM/MM calculations for the WT and mutated enzyme provide a first validation of the mechanism of 1,1-proton transfer and dehydration by the AEE enzyme. Since AEE epimerase possesses a highly conserved TIM-barrel fold, we believe that our study provides a crucial lead to understanding the mechanism of emergence of secondary function which can be useful to repurpose ancient enzymes for modern usage.
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Affiliation(s)
- Ankita Tripathi
- Department of Chemistry, School of Natural Science, Shiv Nadar University Delhi-NCR, NH91 Tehsil Dadri, Greater Noida, Uttar Pradesh, 201314, India
| | - Kshatresh Dutta Dubey
- Department of Chemistry, School of Natural Science, Shiv Nadar University Delhi-NCR, NH91 Tehsil Dadri, Greater Noida, Uttar Pradesh, 201314, India.,Center for Informatics, Department of Chemistry, School of Natural Science, Shiv Nadar University Delhi-NCR, NH91 Tehsil Dadri, Greater Noida, Uttar Pradesh, 201314, India
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30
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Xu G, Poelarends GJ. Unlocking New Reactivities in Enzymes by Iminium Catalysis. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202203613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Guangcai Xu
- University of Groningen: Rijksuniversiteit Groningen Chemical and Pharmaceutical Biology NETHERLANDS
| | - Gerrit J. Poelarends
- University of Groningen Chemical and Pharmaceutical Biology Antonius Deusinglaan 1 9713 AV Groningen NETHERLANDS
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31
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Planas-Iglesias J, Opaleny F, Ulbrich P, Stourac J, Sanusi Z, Pinto GP, Schenkmayerova A, Byska J, Damborsky J, Kozlikova B, Bednar D. LoopGrafter: a web tool for transplanting dynamical loops for protein engineering. Nucleic Acids Res 2022; 50:W465-W473. [PMID: 35438789 PMCID: PMC9252738 DOI: 10.1093/nar/gkac249] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/15/2022] [Accepted: 04/01/2022] [Indexed: 01/01/2023] Open
Abstract
The transplantation of loops between structurally related proteins is a compelling method to improve the activity, specificity and stability of enzymes. However, despite the interest of loop regions in protein engineering, the available methods of loop-based rational protein design are scarce. One particular difficulty related to loop engineering is the unique dynamism that enables them to exert allosteric control over the catalytic function of enzymes. Thus, when engaging in a transplantation effort, such dynamics in the context of protein structure need consideration. A second practical challenge is identifying successful excision points for the transplantation or grafting. Here, we present LoopGrafter (https://loschmidt.chemi.muni.cz/loopgrafter/), a web server that specifically guides in the loop grafting process between structurally related proteins. The server provides a step-by-step interactive procedure in which the user can successively identify loops in the two input proteins, calculate their geometries, assess their similarities and dynamics, and select a number of loops to be transplanted. All possible different chimeric proteins derived from any existing recombination point are calculated, and 3D models for each of them are constructed and energetically evaluated. The obtained results can be interactively visualized in a user-friendly graphical interface and downloaded for detailed structural analyses.
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Affiliation(s)
- Joan Planas-Iglesias
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic.,International Clinical Research Center, St Anne's University Hospital Brno, 656 916 Brno, Czech Republic
| | - Filip Opaleny
- Department of Visual Computing, Faculty of Informatics, Masaryk University, 602 00 Brno, Czech Republic
| | - Pavol Ulbrich
- Department of Visual Computing, Faculty of Informatics, Masaryk University, 602 00 Brno, Czech Republic
| | - Jan Stourac
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic.,International Clinical Research Center, St Anne's University Hospital Brno, 656 916 Brno, Czech Republic
| | - Zainab Sanusi
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
| | - Gaspar P Pinto
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic.,International Clinical Research Center, St Anne's University Hospital Brno, 656 916 Brno, Czech Republic
| | - Andrea Schenkmayerova
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic.,International Clinical Research Center, St Anne's University Hospital Brno, 656 916 Brno, Czech Republic
| | - Jan Byska
- Department of Visual Computing, Faculty of Informatics, Masaryk University, 602 00 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic.,International Clinical Research Center, St Anne's University Hospital Brno, 656 916 Brno, Czech Republic
| | - Barbora Kozlikova
- Department of Visual Computing, Faculty of Informatics, Masaryk University, 602 00 Brno, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic.,International Clinical Research Center, St Anne's University Hospital Brno, 656 916 Brno, Czech Republic
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32
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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: 4] [Impact Index Per Article: 2.0] [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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33
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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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34
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Tavanti M. Synthetic DNA Libraries for Protein Engineering Toward Process Improvement in Drug Synthesis. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2397:33-45. [PMID: 34813058 DOI: 10.1007/978-1-0716-1826-4_3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Speeding-up enzyme engineering by directed evolution is a primary target to be achieved for a wider uptake of biocatalysis in pharmaceutical process development. The capability to rapidly generate the designed sequence diversity has profound implications in the overall optimization of protein function. Drawbacks associated with traditional PCR methods for sequence diversification interfere with the generation of all the variants that have been designed. On the contrary, the enhanced quality of synthetic DNA libraries makes the exploration of sequence space more efficient. Here, methods for the effective utilization of synthetic DNA libraries are described. The overall procedure allows the generation of ready-to-screen libraries within two weeks from synthetic DNA acquisition.
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Affiliation(s)
- Michele Tavanti
- Early Chemical development Pharmaceutical Sciences, R&D AstraZeneca, Cambridge Biomedical Campus, Cambridge, UK. .,Synthetic Biochemistry, Medicinal Science and Technology, Pharma R&D GlaxoSmithKline Medicines Research Centre, Stevenage, UK.
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35
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Qu G, Bi Y, Liu B, Li J, Han X, Liu W, Jiang Y, Qin Z, Sun Z. Unlocking the Stereoselectivity and Substrate Acceptance of Enzymes: Proline‐Induced Loop Engineering Test. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202110793] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Ge Qu
- Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China
- National Technology Innovation Center of Synthetic Biology Tianjin 300308 China
| | - Yuexin Bi
- Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China
- University of Science and Technology of China Hefei 230027 China
| | - Beibei Liu
- Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China
| | - Junkuan Li
- Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China
- Department of Chemistry School of Science Tianjin University Tianjin 300072 China
| | - Xu Han
- Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China
- National Technology Innovation Center of Synthetic Biology Tianjin 300308 China
| | - Weidong Liu
- Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China
- National Technology Innovation Center of Synthetic Biology Tianjin 300308 China
| | - Yingying Jiang
- Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China
- University of Chinese Academy of Sciences Beijing 100049 China
| | - Zongmin Qin
- Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China
- University of Chinese Academy of Sciences Beijing 100049 China
| | - Zhoutong Sun
- Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China
- National Technology Innovation Center of Synthetic Biology Tianjin 300308 China
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36
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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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You S, Zhang YX, Shi F, Zhang WX, Li J, Zhang S, Chen ZL, Zhao WG, Wang J. Lowering energy consumption for fermentable sugar production from Ramulus mori: Engineered xylanase synergy and improved pretreatment strategy. BIORESOURCE TECHNOLOGY 2022; 344:126368. [PMID: 34808317 DOI: 10.1016/j.biortech.2021.126368] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/12/2021] [Accepted: 11/13/2021] [Indexed: 06/13/2023]
Abstract
Biorefinery of Ramulus mori with lower energy consumption through improved enzyme and pretreatment strategies was reported. Directed evolution and saturation mutagenesis were used for the modification of xylanase, the yield of fermentable sugars and the degree of synergy (DS) were determined for different pretreatment (seawater/non-seawater) and enzyme treatment groups (xylanase/cellulase/co-treatment). The dominant mutant I133A/Q143Y of Bispora sp. xylanase XYL10C_ΔN was obtained with improved specific activity (1860 U/mg), catalytic efficiency (1150 mL/s∙mg) at 40 °C, and thermostability (T50 increased by 7 °C). With the pretreatment of seawater immersion, the highest yield of fermentable sugars for Ramulus mori at 40 °C reached 199 μmol/g when hydrolyzed with cellulase and I133A/Q143Y, with the highest DS of 2.6; this was 4.5-fold that of the group hydrolyzed by cellulase alone with non-seawater pretreatment. Thus, bioconversion of reducing sugar from Ramulus mori was improved significantly at lower temperatures, which provides an efficient and energy-saving wayfor biofuel production.
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Affiliation(s)
- Shuai You
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212100, PR China; Key Laboratory of Silkworm and Mulberry Gene tic Improvement, Ministry of Agriculture and Rural Affairs, Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu 212100, PR China
| | - Yi-Xin Zhang
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212100, PR China
| | - Fan Shi
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212100, PR China
| | - Wen-Xin Zhang
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212100, PR China
| | - Jing Li
- Department of Nephrology, Affiliated Hospital of Jiangsu University, Zhenjiang 212001, PR China
| | - Sheng Zhang
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212100, PR China
| | - Zhong-Li Chen
- Xinyuan Cocoon Silk Group Co., Ltd., Nantong 226600, PR China
| | - Wei-Guo Zhao
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212100, PR China; Key Laboratory of Silkworm and Mulberry Gene tic Improvement, Ministry of Agriculture and Rural Affairs, Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu 212100, PR China
| | - Jun Wang
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212100, PR China; Key Laboratory of Silkworm and Mulberry Gene tic Improvement, Ministry of Agriculture and Rural Affairs, Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu 212100, PR China.
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38
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Jackson C, Toth-Petroczy A, Kolodny R, Hollfelder F, Fuxreiter M, Caroline Lynn Kamerlin S, Tokuriki N. Adventures on the routes of protein evolution — in memoriam Dan Salah Tawfik (1955 - 2021). J Mol Biol 2022; 434:167462. [DOI: 10.1016/j.jmb.2022.167462] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 01/17/2022] [Indexed: 12/21/2022]
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39
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Ming H, Yuan B, Qu G, Sun Z. Engineering the activity of amine dehydrogenase in the asymmetric reductive amination of hydroxyl ketones. Catal Sci Technol 2022. [DOI: 10.1039/d2cy00391k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
An engineered AmDH derived from a leucine dehydrogenase was used as the starting enzyme to improve its activity in the synthesis of (R)-3-amino-1-butanol. Preparative-scale synthesis of the (R)-product (90% yield, >99%) was performed on a gram-scale.
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Affiliation(s)
- Hui Ming
- Department of Life Sciences and Medicine, University of Science and technology of China, Hefei 230022, China
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West 7th Avenue, Tianjin Airport Economic Area, Tianjin 300308, China
| | - Bo Yuan
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West 7th Avenue, Tianjin Airport Economic Area, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China
| | - Ge Qu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West 7th Avenue, Tianjin Airport Economic Area, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China
| | - Zhoutong Sun
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West 7th Avenue, Tianjin Airport Economic Area, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China
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40
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Naganathan AN, Dani R, Gopi S, Aranganathan A, Narayan A. Folding Intermediates, Heterogeneous Native Ensembles and Protein Function. J Mol Biol 2021; 433:167325. [PMID: 34695380 DOI: 10.1016/j.jmb.2021.167325] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 10/17/2021] [Accepted: 10/18/2021] [Indexed: 01/01/2023]
Abstract
Single domain proteins fold via diverse mechanisms emphasizing the intricate relationship between energetics and structure, which is a direct consequence of functional constraints and demands imposed at the level of sequence. On the other hand, elucidating the interplay between folding mechanisms and function is challenging in large proteins, given the inherent shortcomings in identifying metastable states experimentally and the sampling limitations associated with computational methods. Here, we show that free energy profiles and surfaces of large systems (>150 residues), as predicted by a statistical mechanical model, display a wide array of folding mechanisms with ubiquitous folding intermediates and heterogeneous native ensembles. Importantly, residues around the ligand binding or enzyme active site display a larger tendency to partially unfold and this manifests as intermediates or excited states along the folding coordinate in ligand binding domains, transcription repressors, and representative enzymes from all the six classes, including the SARS-CoV-2 receptor binding domain (RBD) of the spike protein and the protease Mpro. It thus appears that it is relatively easier to distill the imprints of function on the folding landscape of larger proteins as opposed to smaller systems. We discuss how an understanding of energetic-entropic features in ordered proteins can pinpoint specific avenues through which folding mechanisms, populations of partially structured states and function can be engineered.
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Affiliation(s)
- Athi N Naganathan
- Department of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.
| | - Rahul Dani
- Department of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Soundhararajan Gopi
- Department of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India. https://twitter.com/Soundha
| | - Akashnathan Aranganathan
- Department of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Abhishek Narayan
- Department of Physics, University of Alberta, Edmonton, AB T6G 2E1, Canada
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41
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Buchholz PCF, van Loo B, Eenink BDG, Bornberg-Bauer E, Pleiss J. Ancestral sequences of a large promiscuous enzyme family correspond to bridges in sequence space in a network representation. J R Soc Interface 2021; 18:20210389. [PMID: 34727710 DOI: 10.1098/rsif.2021.0389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Evolutionary relationships of protein families can be characterized either by networks or by trees. Whereas trees allow for hierarchical grouping and reconstruction of the most likely ancestral sequences, networks lack a time axis but allow for thresholds of pairwise sequence identity to be chosen and, therefore, the clustering of family members with presumably more similar functions. Here, we use the large family of arylsulfatases and phosphonate monoester hydrolases to investigate similarities, strengths and weaknesses in tree and network representations. For varying thresholds of pairwise sequence identity, values of betweenness centrality and clustering coefficients were derived for nodes of the reconstructed ancestors to measure the propensity to act as a bridge in a network. Based on these properties, ancestral protein sequences emerge as bridges in protein sequence networks. Interestingly, many ancestral protein sequences appear close to extant sequences. Therefore, reconstructed ancestor sequences might also be interpreted as yet-to-be-identified homologues. The concept of ancestor reconstruction is compared to consensus sequences, too. It was found that hub sequences in a network, e.g. reconstructed ancestral sequences that are connected to many neighbouring sequences, share closer similarity with derived consensus sequences. Therefore, some reconstructed ancestor sequences can also be interpreted as consensus sequences.
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Affiliation(s)
- Patrick C F Buchholz
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
| | - Bert van Loo
- Department of Applied Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK.,Institute for Evolution and Biodiversity, University of Münster, Hüfferstraße 1, Münster 48149, Germany
| | - Bernard D G Eenink
- Institute for Evolution and Biodiversity, University of Münster, Hüfferstraße 1, Münster 48149, Germany
| | - Erich Bornberg-Bauer
- Institute for Evolution and Biodiversity, University of Münster, Hüfferstraße 1, Münster 48149, Germany.,Department of Protein Evolution, Max Planck Institute for Developmental Biology, Max-Planck-Ring 5, Tübingen 72076, Germany
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
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42
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Orlando M, Buchholz PCF, Lotti M, Pleiss J. The GH19 Engineering Database: Sequence diversity, substrate scope, and evolution in glycoside hydrolase family 19. PLoS One 2021; 16:e0256817. [PMID: 34699529 PMCID: PMC8547705 DOI: 10.1371/journal.pone.0256817] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 08/16/2021] [Indexed: 01/21/2023] Open
Abstract
The glycoside hydrolase 19 (GH19) is a bifunctional family of chitinases and endolysins, which have been studied for the control of plant fungal pests, the recycle of chitin biomass, and the treatment of multi-drug resistant bacteria. The GH19 domain-containing sequences (22,461) were divided into a chitinase and an endolysin subfamily by analyzing sequence networks, guided by taxonomy and the substrate specificity of characterized enzymes. The chitinase subfamily was split into seventeen groups, thus extending the previous classification. The endolysin subfamily is more diverse and consists of thirty-four groups. Despite their sequence diversity, twenty-six residues are conserved in chitinases and endolysins, which can be distinguished by two specific sequence patterns at six and four positions, respectively. Their location outside the catalytic cleft suggests a possible mechanism for substrate specificity that goes beyond the direct interaction with the substrate. The evolution of the GH19 catalytic domain was investigated by large-scale phylogeny. The inferred evolutionary history and putative horizontal gene transfer events differ from previous works. While no clear patterns were detected in endolysins, chitinases varied in sequence length by up to four loop insertions, causing at least eight distinct presence/absence loop combinations. The annotated GH19 sequences and structures are accessible via the GH19 Engineering Database (GH19ED, https://gh19ed.biocatnet.de). The GH19ED has been developed to support the prediction of substrate specificity and the search for novel GH19 enzymes from neglected taxonomic groups or in regions of the sequence space where few sequences have been described yet.
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Affiliation(s)
- Marco Orlando
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Patrick C. F. Buchholz
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Marina Lotti
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
- * E-mail:
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43
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Akanuma S, Yamaguchi M, Yamagishi A. Comprehensive mutagenesis to identify amino acid residues contributing to the difference in thermostability between two originally thermostable ancestral proteins. PLoS One 2021; 16:e0258821. [PMID: 34673819 PMCID: PMC8530338 DOI: 10.1371/journal.pone.0258821] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 10/05/2021] [Indexed: 11/19/2022] Open
Abstract
Further improvement of the thermostability of inherently thermostable proteins is an attractive challenge because more thermostable proteins are industrially more useful and serve as better scaffolds for protein engineering. To establish guidelines that can be applied for the rational design of hyperthermostable proteins, we compared the amino acid sequences of two ancestral nucleoside diphosphate kinases, Arc1 and Bac1, reconstructed in our previous study. Although Bac1 is a thermostable protein whose unfolding temperature is around 100°C, Arc1 is much more thermostable with an unfolding temperature of 114°C. However, only 12 out of 139 amino acids are different between the two sequences. In this study, one or a combination of amino acid(s) in Bac1 was/were substituted by a residue(s) found in Arc1 at the same position(s). The best mutant, which contained three amino acid substitutions (S108D, G116A and L120P substitutions), showed an unfolding temperature more than 10°C higher than that of Bac1. Furthermore, a combination of the other nine amino acid substitutions also led to improved thermostability of Bac1, although the effects of individual substitutions were small. Therefore, not only the sum of the contributions of individual amino acids, but also the synergistic effects of multiple amino acids are deeply involved in the stability of a hyperthermostable protein. Such insights will be helpful for future rational design of hyperthermostable proteins.
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Affiliation(s)
- Satoshi Akanuma
- Faculty of Human Sciences, Waseda University, Tokorozawa, Saitama, Japan
- * E-mail:
| | - Minako Yamaguchi
- Department of Applied Life Sciences, Tokyo University of Pharmacy and Life Sciences, Hachioji, Tokyo, Japan
| | - Akihiko Yamagishi
- Department of Applied Life Sciences, Tokyo University of Pharmacy and Life Sciences, Hachioji, Tokyo, Japan
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44
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Koch NG, Goettig P, Rappsilber J, Budisa N. Engineering Pyrrolysyl-tRNA Synthetase for the Incorporation of Non-Canonical Amino Acids with Smaller Side Chains. Int J Mol Sci 2021; 22:11194. [PMID: 34681855 PMCID: PMC8538471 DOI: 10.3390/ijms222011194] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 10/13/2021] [Accepted: 10/14/2021] [Indexed: 01/11/2023] Open
Abstract
Site-specific incorporation of non-canonical amino acids (ncAAs) into proteins has emerged as a universal tool for systems bioengineering at the interface of chemistry, biology, and technology. The diversification of the repertoire of the genetic code has been achieved for amino acids with long and/or bulky side chains equipped with various bioorthogonal tags and useful spectral probes. Although ncAAs with relatively small side chains and similar properties are of great interest to biophysics, cell biology, and biomaterial science, they can rarely be incorporated into proteins. To address this gap, we report the engineering of PylRS variants capable of incorporating an entire library of aliphatic "small-tag" ncAAs. In particular, we performed mutational studies of a specific PylRS, designed to incorporate the shortest non-bulky ncAA (S-allyl-l-cysteine) possible to date and based on this knowledge incorporated aliphatic ncAA derivatives. In this way, we have not only increased the number of translationally active "small-tag" ncAAs, but also determined key residues responsible for maintaining orthogonality, while engineering the PylRS for these interesting substrates. Based on the known plasticity of PylRS toward different substrates, our approach further expands the reassignment capacities of this enzyme toward aliphatic amino acids with smaller side chains endowed with valuable functionalities.
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Affiliation(s)
- Nikolaj G. Koch
- Institut für Chemie, Technische Universität Berlin, 10623 Berlin, Germany;
- Institut für Biotechnologie-Bioanalytik, Technische Universität Berlin, 10623 Berlin, Germany;
| | - Peter Goettig
- Structural Biology Group, Department of Biosciences, University of Salzburg, 5020 Salzburg, Austria;
| | - Juri Rappsilber
- Institut für Biotechnologie-Bioanalytik, Technische Universität Berlin, 10623 Berlin, Germany;
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Nediljko Budisa
- Institut für Chemie, Technische Universität Berlin, 10623 Berlin, Germany;
- Department of Chemistry, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
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45
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Qu G, Bi Y, Liu B, Li J, Han X, Liu W, Jiang Y, Qin Z, Sun Z. Unlocking the Stereoselectivity and Substrate Acceptance of Enzymes: Proline-Induced Loop Engineering Test. Angew Chem Int Ed Engl 2021; 61:e202110793. [PMID: 34658118 DOI: 10.1002/anie.202110793] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 09/25/2021] [Indexed: 12/12/2022]
Abstract
Protein stability and evolvability influence each other. Although protein dynamics play essential roles in various catalytically important properties, their high flexibility and diversity makes it difficult to incorporate such properties into rational engineering. Therefore, how to unlock the potential evolvability in a user-friendly rational design process remains a challenge. In this endeavor, we describe a method for engineering an enantioselective alcohol dehydrogenase. It enables synthetically important substrate acceptance for 4-chlorophenyl pyridine-2-yl ketone, and perfect stereocontrol of both (S)- and (R)-configured products. Thermodynamic analysis unveiled the subtle interaction between enzyme stability and evolvability, while computational studies provided insights into the origin of selectivity and substrate recognition. Preparative-scale synthesis of the (S)-product (73 % yield; >99 % ee) was performed on a gram-scale. This proof-of-principle study demonstrates that interfaced proline residues can be rationally engineered to unlock evolvability and thus provide access to new biocatalysts with highly improved catalytic performance.
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Affiliation(s)
- Ge Qu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.,National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Yuexin Bi
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.,University of Science and Technology of China, Hefei, 230027, China
| | - Beibei Liu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Junkuan Li
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.,Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
| | - Xu Han
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.,National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Weidong Liu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.,National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Yingying Jiang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zongmin Qin
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhoutong Sun
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.,National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
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46
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Pinto GP, Corbella M, Demkiv AO, Kamerlin SCL. Exploiting enzyme evolution for computational protein design. Trends Biochem Sci 2021; 47:375-389. [PMID: 34544655 DOI: 10.1016/j.tibs.2021.08.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/18/2021] [Accepted: 08/24/2021] [Indexed: 11/15/2022]
Abstract
Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.
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Affiliation(s)
- Gaspar P Pinto
- Department of Chemistry - BMC, Uppsala University, BMC Box 576, S-751 23 Uppsala, Sweden
| | - Marina Corbella
- Department of Chemistry - BMC, Uppsala University, BMC Box 576, S-751 23 Uppsala, Sweden
| | - Andrey O Demkiv
- Department of Chemistry - BMC, Uppsala University, BMC Box 576, S-751 23 Uppsala, Sweden
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47
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Wang Y, Xue P, Cao M, Yu T, Lane ST, Zhao H. Directed Evolution: Methodologies and Applications. Chem Rev 2021; 121:12384-12444. [PMID: 34297541 DOI: 10.1021/acs.chemrev.1c00260] [Citation(s) in RCA: 184] [Impact Index Per Article: 61.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Directed evolution aims to expedite the natural evolution process of biological molecules and systems in a test tube through iterative rounds of gene diversifications and library screening/selection. It has become one of the most powerful and widespread tools for engineering improved or novel functions in proteins, metabolic pathways, and even whole genomes. This review describes the commonly used gene diversification strategies, screening/selection methods, and recently developed continuous evolution strategies for directed evolution. Moreover, we highlight some representative applications of directed evolution in engineering nucleic acids, proteins, pathways, genetic circuits, viruses, and whole cells. Finally, we discuss the challenges and future perspectives in directed evolution.
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Affiliation(s)
- Yajie Wang
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Mingfeng Cao
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Tianhao Yu
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Stephan T Lane
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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48
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Affiliation(s)
- Amir Aharoni
- Department of Life Sciences Ben‐Gurion University of the Negev Be’er Sheva Israel
| | - Sarel J. Fleishman
- Department of Biomolecular Sciences Weizmann Institute of Science Rehovot Israel
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49
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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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50
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Spence MA, Kaczmarski JA, Saunders JW, Jackson CJ. Ancestral sequence reconstruction for protein engineers. Curr Opin Struct Biol 2021; 69:131-141. [PMID: 34023793 DOI: 10.1016/j.sbi.2021.04.001] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/22/2021] [Accepted: 04/07/2021] [Indexed: 12/11/2022]
Abstract
In addition to its value in the study of molecular evolution, ancestral sequence reconstruction (ASR) has emerged as a useful methodology for engineering proteins with enhanced properties. Proteins generated by ASR often exhibit unique or improved activity, stability, and/or promiscuity, all of which are properties that are valued by protein engineers. Comparison between extant proteins and evolutionary intermediates generated by ASR also allows protein engineers to identify substitutions that have contributed to functional innovation or diversification within protein families. As ASR becomes more widely adopted as a protein engineering approach, it is important to understand the applications, limitations, and recent developments of this technique. This review highlights recent exemplifications of ASR, as well as technical aspects of the reconstruction process that are relevant to protein engineering.
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Affiliation(s)
- Matthew A Spence
- Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia
| | - Joe A Kaczmarski
- Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia
| | - Jake W Saunders
- Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia
| | - Colin J Jackson
- Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia; ARC Centre of Excellence for Innovations in Peptide & Protein Science, Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia; ARC Centre of Excellence for Innovations in Synthetic Biology, Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia.
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