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Malard F, Dias K, Baudy M, Thore S, Vialet B, Barthélémy P, Fribourg S, Karginov FV, Campagne S. Molecular Basis for the Calcium-Dependent Activation of the Ribonuclease EndoU. RESEARCH SQUARE 2024:rs.3.rs-4654759. [PMID: 39070628 PMCID: PMC11275989 DOI: 10.21203/rs.3.rs-4654759/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Ribonucleases (RNases) are ubiquitous enzymes that process or degrade RNA, essential for cellular functions and immune responses. The EndoU-like superfamily includes endoribonucleases conserved across bacteria, eukaryotes, and certain viruses, with an ancient evolutionary link to the ribonuclease A-like superfamily. Both bacterial EndoU and animal RNase A share a similar fold and function independently of cofactors. In contrast, the eukaryotic EndoU catalytic domain requires divalent metal ions for catalysis, possibly due to an N-terminal extension near the catalytic core. In this study, we used biophysical and computational techniques along with in vitro assays to investigate the calcium-dependent activation of human EndoU. We determined the crystal structure of EndoU bound to calcium and found that calcium binding remote from the catalytic triad triggers water-mediated intramolecular signaling and structural changes, activating the enzyme through allostery. Calcium-binding involves residues from both the catalytic core and the N-terminal extension, indicating that the N-terminal extension interacts with the catalytic core to modulate activity in response to calcium. Our findings suggest that similar mechanisms may be present across all eukaryotic EndoUs, highlighting a unique evolutionary adaptation that connects endoribonuclease activity to cellular signaling in eukaryotes.
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Affiliation(s)
- Florian Malard
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
- Univ. Bordeaux, CNRS, INSERM, IECB, US1, UAR 3033, F-33600 Pessac, France
| | - Kristen Dias
- Department of Molecular, Cell and Systems Biology, Institute for Integrative Genome Biology, University of California at Riverside, Riverside, CA, 92521, USA
| | - Margaux Baudy
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
- Univ. Bordeaux, CNRS, INSERM, IECB, US1, UAR 3033, F-33600 Pessac, France
| | - Stéphane Thore
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
| | - Brune Vialet
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
| | - Philippe Barthélémy
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
| | - Sébastien Fribourg
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
| | - Fedor V Karginov
- Department of Molecular, Cell and Systems Biology, Institute for Integrative Genome Biology, University of California at Riverside, Riverside, CA, 92521, USA
| | - Sébastien Campagne
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
- Univ. Bordeaux, CNRS, INSERM, IECB, US1, UAR 3033, F-33600 Pessac, France
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2
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Tripp A, Braun M, Wieser F, Oberdorfer G, Lechner H. Click, Compute, Create: A Review of Web-based Tools for Enzyme Engineering. Chembiochem 2024:e202400092. [PMID: 38634409 DOI: 10.1002/cbic.202400092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/14/2024] [Accepted: 04/15/2024] [Indexed: 04/19/2024]
Abstract
Enzyme engineering, though pivotal across various biotechnological domains, is often plagued by its time-consuming and labor-intensive nature. This review aims to offer an overview of supportive in silico methodologies for this demanding endeavor. Starting from methods to predict protein structures, to classification of their activity and even the discovery of new enzymes we continue with describing tools used to increase thermostability and production yields of selected targets. Subsequently, we discuss computational methods to modulate both, the activity as well as selectivity of enzymes. Last, we present recent approaches based on cutting-edge machine learning methods to redesign enzymes. With exception of the last chapter, there is a strong focus on methods easily accessible via web-interfaces or simple Python-scripts, therefore readily useable for a diverse and broad community.
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Affiliation(s)
- Adrian Tripp
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
| | - Markus Braun
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
| | - Florian Wieser
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
| | - Gustav Oberdorfer
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
- BioTechMed, Graz, Austria
| | - Horst Lechner
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
- BioTechMed, Graz, Austria
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3
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Zimmerman L, Alon N, Levin I, Koganitsky A, Shpigel N, Brestel C, Lapidoth GD. Context-dependent design of induced-fit enzymes using deep learning generates well-expressed, thermally stable and active enzymes. Proc Natl Acad Sci U S A 2024; 121:e2313809121. [PMID: 38437538 PMCID: PMC10945820 DOI: 10.1073/pnas.2313809121] [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: 08/15/2023] [Accepted: 02/09/2024] [Indexed: 03/06/2024] Open
Abstract
The potential of engineered enzymes in industrial applications is often limited by their expression levels, thermal stability, and catalytic diversity. De novo enzyme design faces challenges due to the complexity of enzymatic catalysis. An alternative approach involves expanding natural enzyme capabilities for new substrates and parameters. Here, we introduce CoSaNN (Conformation Sampling using Neural Network), an enzyme design strategy using deep learning for structure prediction and sequence optimization. CoSaNN controls enzyme conformations to expand chemical space beyond simple mutagenesis. It employs a context-dependent approach for generating enzyme designs, considering non-linear relationships in sequence and structure space. We also developed SolvIT, a graph NN predicting protein solubility in Escherichia coli, optimizing enzyme expression selection from larger design sets. Using this method, we engineered enzymes with superior expression levels, with 54% expressed in E. coli, and increased thermal stability, with over 30% having higher Tm than the template, with no high-throughput screening. Our research underscores AI's transformative role in protein design, capturing high-order interactions and preserving allosteric mechanisms in extensively modified enzymes, and notably enhancing expression success rates. This method's ease of use and efficiency streamlines enzyme design, opening broad avenues for biotechnological applications and broadening field accessibility.
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Affiliation(s)
| | - Noga Alon
- Enzymit Ltd., Ness-Ziona7403626, Israel
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4
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Corbella M, Pinto GP, Kamerlin SCL. Loop dynamics and the evolution of enzyme activity. Nat Rev Chem 2023; 7:536-547. [PMID: 37225920 DOI: 10.1038/s41570-023-00495-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/06/2023] [Indexed: 05/26/2023]
Abstract
In the early 2000s, Tawfik presented his 'New View' on enzyme evolution, highlighting the role of conformational plasticity in expanding the functional diversity of limited repertoires of sequences. This view is gaining increasing traction with increasing evidence of the importance of conformational dynamics in both natural and laboratory evolution of enzymes. The past years have seen several elegant examples of harnessing conformational (particularly loop) dynamics to successfully manipulate protein function. This Review revisits flexible loops as critical participants in regulating enzyme activity. We showcase several systems of particular interest: triosephosphate isomerase barrel proteins, protein tyrosine phosphatases and β-lactamases, while briefly discussing other systems in which loop dynamics are important for selectivity and turnover. We then discuss the implications for engineering, presenting examples of successful loop manipulation in either improving catalytic efficiency, or changing selectivity completely. Overall, it is becoming clearer that mimicking nature by manipulating the conformational dynamics of key protein loops is a powerful method of tailoring enzyme activity, without needing to target active-site residues.
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Affiliation(s)
- Marina Corbella
- Department of Chemistry, Uppsala University, Uppsala, Sweden
| | - Gaspar P Pinto
- Department of Chemistry, Uppsala University, Uppsala, Sweden
- Cortex Discovery GmbH, Regensburg, Germany
| | - Shina C L Kamerlin
- Department of Chemistry, Uppsala University, Uppsala, Sweden.
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA.
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5
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Ansbacher T, Tohar R, Cohen A, Cohen O, Levartovsky S, Arieli A, Matalon S, Bar DZ, Gal M, Weinberg E. A novel computationally engineered collagenase reduces the force required for tooth extraction in an ex-situ porcine jaw model. J Biol Eng 2023; 17:47. [PMID: 37461028 DOI: 10.1186/s13036-023-00366-4] [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: 05/07/2023] [Accepted: 07/05/2023] [Indexed: 07/20/2023] Open
Abstract
The currently employed tooth extraction methods in dentistry involve mechanical disruption of the periodontal ligament fibers, leading to inevitable trauma to the bundle bone comprising the socket walls. In our previous work, we have shown that a recombinantly expressed truncated version of clostridial collagenase G (ColG) purified from Escherichia coli efficiently reduced the force needed for tooth extraction in an ex-situ porcine jaw model, when injected into the periodontal ligament. Considering that enhanced thermostability often leads to higher enzymatic activity and to set the basis for additional rounds of optimization, we used a computational protein design approach to generate an enzyme to be more thermostable while conserving the key catalytic residues. This process generated a novel collagenase (ColG-variant) harboring sixteen mutations compared to ColG, with a nearly 4℃ increase in melting temperature. Herein, we explored the potential of ColG-variant to further decrease the physical effort required for tooth delivery using our established ex-situ porcine jaw model. An average reduction of 11% was recorded in the force applied to extract roots of mandibular split first and second premolar teeth treated with ColG-variant, relative to those treated with ColG. Our results show for the first time the potential of engineering enzyme properties for dental medicine and further contribute to minimally invasive tooth extraction.
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Affiliation(s)
- Tamar Ansbacher
- Department of Oral Biology, Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel
- Hadassah Academic College, 91010, Jerusalem, Israel
| | - Ran Tohar
- Department of Oral Biology, Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Adi Cohen
- Department of Oral Biology, Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Orel Cohen
- Department of Oral Biology, Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Shifra Levartovsky
- Department of Oral Rehabilitation, Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Adi Arieli
- Department of Oral Rehabilitation, Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Shlomo Matalon
- Department of Oral Rehabilitation, Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Daniel Z Bar
- Department of Oral Biology, Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Maayan Gal
- Department of Oral Biology, Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel.
| | - Evgeny Weinberg
- Department of Oral Biology, Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel.
- Department of Periodontology and Oral Implantology, Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel.
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6
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Williams JA, Biancucci M, Lessen L, Tian S, Balsaraf A, Chen L, Chesterman C, Maruggi G, Vandepaer S, Huang Y, Mallett CP, Steff AM, Bottomley MJ, Malito E, Wahome N, Harshbarger WD. Structural and computational design of a SARS-CoV-2 spike antigen with improved expression and immunogenicity. SCIENCE ADVANCES 2023; 9:eadg0330. [PMID: 37285422 PMCID: PMC10246912 DOI: 10.1126/sciadv.adg0330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/02/2023] [Indexed: 06/09/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern challenge the efficacy of approved vaccines, emphasizing the need for updated spike antigens. Here, we use an evolutionary-based design aimed at boosting protein expression levels of S-2P and improving immunogenic outcomes in mice. Thirty-six prototype antigens were generated in silico and 15 were produced for biochemical analysis. S2D14, which contains 20 computationally designed mutations within the S2 domain and a rationally engineered D614G mutation in the SD2 domain, has an ~11-fold increase in protein yield and retains RBD antigenicity. Cryo-electron microscopy structures reveal a mixture of populations in various RBD conformational states. Vaccination of mice with adjuvanted S2D14 elicited higher cross-neutralizing antibody titers than adjuvanted S-2P against the SARS-CoV-2 Wuhan strain and four variants of concern. S2D14 may be a useful scaffold or tool for the design of future coronavirus vaccines, and the approaches used for the design of S2D14 may be broadly applicable to streamline vaccine discovery.
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7
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Huang J, Xie X, Zheng Z, Ye L, Wang P, Xu L, Wu Y, Yan J, Yang M, Yan Y. De Novo Computational Design of a Lipase with Hydrolysis Activity towards Middle-Chained Fatty Acid Esters. Int J Mol Sci 2023; 24:ijms24108581. [PMID: 37239928 DOI: 10.3390/ijms24108581] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/08/2023] [Accepted: 05/09/2023] [Indexed: 05/28/2023] Open
Abstract
Innovations in biocatalysts provide great prospects for intolerant environments or novel reactions. Due to the limited catalytic capacity and the long-term and labor-intensive characteristics of mining enzymes with the desired functions, de novo enzyme design was developed to obtain industrial application candidates in a rapid and convenient way. Here, based on the catalytic mechanisms and the known structures of proteins, we proposed a computational protein design strategy combining de novo enzyme design and laboratory-directed evolution. Starting with the theozyme constructed using a quantum-mechanical approach, the theoretical enzyme-skeleton combinations were assembled and optimized via the Rosetta "inside-out" protocol. A small number of designed sequences were experimentally screened using SDS-PAGE, mass spectrometry and a qualitative activity assay in which the designed enzyme 1a8uD1 exhibited a measurable hydrolysis activity of 24.25 ± 0.57 U/g towards p-nitrophenyl octanoate. To improve the activity of the designed enzyme, molecular dynamics simulations and the RosettaDesign application were utilized to further optimize the substrate binding mode and amino acid sequence, thus keeping the residues of theozyme intact. The redesigned lipase 1a8uD1-M8 displayed enhanced hydrolysis activity towards p-nitrophenyl octanoate-3.34 times higher than that of 1a8uD1. Meanwhile, the natural skeleton protein (PDB entry 1a8u) did not display any hydrolysis activity, confirming that the hydrolysis abilities of the designed 1a8uD1 and the redesigned 1a8uD1-M8 were devised from scratch. More importantly, the designed 1a8uD1-M8 was also able to hydrolyze the natural middle-chained substrate (glycerol trioctanoate), for which the activity was 27.67 ± 0.69 U/g. This study indicates that the strategy employed here has great potential to generate novel enzymes exhibiting the desired reactions.
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Affiliation(s)
- Jinsha Huang
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xiaoman Xie
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Zhen Zheng
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Luona Ye
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Pengbo Wang
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Li Xu
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Ying Wu
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jinyong Yan
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Min Yang
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yunjun Yan
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
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8
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Lipsh-Sokolik R, Khersonsky O, Schröder SP, de Boer C, Hoch SY, Davies GJ, Overkleeft HS, Fleishman SJ. Combinatorial assembly and design of enzymes. Science 2023; 379:195-201. [PMID: 36634164 DOI: 10.1126/science.ade9434] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
The design of structurally diverse enzymes is constrained by long-range interactions that are necessary for accurate folding. We introduce an atomistic and machine learning strategy for the combinatorial assembly and design of enzymes (CADENZ) to design fragments that combine with one another to generate diverse, low-energy structures with stable catalytic constellations. We applied CADENZ to endoxylanases and used activity-based protein profiling to recover thousands of structurally diverse enzymes. Functional designs exhibit high active-site preorganization and more stable and compact packing outside the active site. Implementing these lessons into CADENZ led to a 10-fold improved hit rate and more than 10,000 recovered enzymes. This design-test-learn loop can be applied, in principle, to any modular protein family, yielding huge diversity and general lessons on protein design principles.
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Affiliation(s)
- R Lipsh-Sokolik
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - O Khersonsky
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - S P Schröder
- Leiden Institute of Chemistry, Leiden University, Einsteinweg 55, 2300 RA Leiden, Netherlands
| | - C de Boer
- Leiden Institute of Chemistry, Leiden University, Einsteinweg 55, 2300 RA Leiden, Netherlands
| | - S-Y Hoch
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - G J Davies
- York Structural Biology Laboratory, Department of Chemistry, The University of York, Heslington, York YO10 5DD, UK
| | - H S Overkleeft
- Leiden Institute of Chemistry, Leiden University, Einsteinweg 55, 2300 RA Leiden, Netherlands
| | - S J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001 Rehovot, Israel
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9
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Gallarati S, van Gerwen P, Laplaza R, Vela S, Fabrizio A, Corminboeuf C. OSCAR: an extensive repository of chemically and functionally diverse organocatalysts. Chem Sci 2022; 13:13782-13794. [PMID: 36544722 PMCID: PMC9710326 DOI: 10.1039/d2sc04251g] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022] Open
Abstract
The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries, the field of organocatalysis is mostly dominated by case-by-case studies, with a lack of transferable data-driven tools that facilitate both the exploration of a wider range of catalyst space and the optimization of reaction properties. For these reasons, we introduce OSCAR, a repository of 4000 experimentally derived organocatalysts along with their corresponding building blocks and combinatorially enriched structures. We outline the fragment-based approach used for database generation and showcase the chemical diversity, in terms of functions and molecular properties, covered in OSCAR. The structures and corresponding stereoelectronic properties are publicly available (https://archive.materialscloud.org/record/2022.106) and constitute the starting point to build generative and predictive models for organocatalyst performance.
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Affiliation(s)
- Simone Gallarati
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Puck van Gerwen
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research - Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Ruben Laplaza
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research - Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Sergi Vela
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Alberto Fabrizio
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Clemence Corminboeuf
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research - Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
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10
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Chu AE, Fernandez D, Liu J, Eguchi RR, Huang PS. De Novo Design of a Highly Stable Ovoid TIM Barrel: Unlocking Pocket Shape towards Functional Design. BIODESIGN RESEARCH 2022; 2022:9842315. [PMID: 37850141 PMCID: PMC10521652 DOI: 10.34133/2022/9842315] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/26/2022] [Indexed: 10/19/2023] Open
Abstract
The ability to finely control the structure of protein folds is an important prerequisite to functional protein design. The TIM barrel fold is an important target for these efforts as it is highly enriched for diverse functions in nature. Although a TIM barrel protein has been designed de novo, the ability to finely alter the curvature of the central beta barrel and the overall architecture of the fold remains elusive, limiting its utility for functional design. Here, we report the de novo design of a TIM barrel with ovoid (twofold) symmetry, drawing inspiration from natural beta and TIM barrels with ovoid curvature. We use an autoregressive backbone sampling strategy to implement our hypothesis for elongated barrel curvature, followed by an iterative enrichment sequence design protocol to obtain sequences which yield a high proportion of successfully folding designs. Designed sequences are highly stable and fold to the designed barrel curvature as determined by a 2.1 Å resolution crystal structure. The designs show robustness to drastic mutations, retaining high melting temperatures even when multiple charged residues are buried in the hydrophobic core or when the hydrophobic core is ablated to alanine. As a scaffold with a greater capacity for hosting diverse hydrogen bonding networks and installation of binding pockets or active sites, the ovoid TIM barrel represents a major step towards the de novo design of functional TIM barrels.
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Affiliation(s)
- Alexander E. Chu
- Biophysics Program, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Daniel Fernandez
- Program in Chemistry, Engineering, And Medicine for Human Health (ChEM-H), Stanford University, Stanford, CA, USA
- Stanford ChEM-H, Macromolecular Structure Knowledge Center, Stanford University, Stanford, CA, USA
| | - Jingjia Liu
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Raphael R. Eguchi
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Stanford ChEM-H, Macromolecular Structure Knowledge Center, Stanford University, Stanford, CA, USA
- Department of Biochemistry, Stanford University, Stanford, CA, USA
| | - Po-Ssu Huang
- Biophysics Program, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Stanford ChEM-H, Macromolecular Structure Knowledge Center, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
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11
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Identification and Mutation Analysis of Nonconserved Residues on the TIM-Barrel Surface of GH5_5 Cellulases for Catalytic Efficiency and Stability Improvement. Appl Environ Microbiol 2022; 88:e0104622. [PMID: 36000858 PMCID: PMC9469711 DOI: 10.1128/aem.01046-22] [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: 11/20/2022] Open
Abstract
Exploring the potential functions of nonconserved residues on the outer side of α-helices and systematically optimizing them are pivotal for their application in protein engineering. Based on the evolutionary structural conservation analysis of GH5_5 cellulases, a practical molecular improvement strategy was developed. Highly variable sites on the outer side of the α-helices of the GH5_5 cellulase from Aspergillus niger (AnCel5A) were screened, and 14 out of the 34 highly variable sites were confirmed to exert a positive effect on the activity. After the modular combination of the positive mutations, the catalytic efficiency of the mutants was further improved. By using CMC-Na as the substrate, the catalytic efficiency and specific activity of variant AnCel5A_N193A/T300P/D307P were approximately 2.0-fold that of AnCel5A (227 ± 21 versus 451 ± 43 ml/s/mg and 1,726 ± 19 versus 3,472 ± 42 U/mg, respectively). The half-life (t1/2) of variant AnCel5A_N193A/T300P/D307P at 75°C was 2.36 times that of AnCel5A. The role of these sites was successfully validated in other GH5_5 cellulases. Computational analyses revealed that the flexibility of the loop 6-loop 7-loop 8 region was responsible for the increased catalytic performance. This work not only illustrated the important role of rapidly evolving positions on the outer side of the α-helices of GH5_5 cellulases but also revealed new insights into engineering the proteins that nature left as clues for us to find. IMPORTANCE A comprehensive understanding of the residues on the α-helices of the GH5_5 cellulases is important for catalytic efficiency and stability improvement. The main objective of this study was to use the evolutionary conservation and plasticity of the TIM-barrel fold to probe the relationship between nonconserved residues on the outer side of the α-helices and the catalytic efficiency of GH5_5 cellulases by conducting structure-guided protein engineering. By using a four-step nonconserved residue screening strategy, the functional role of nonconserved residues on the outer side of the α-helices was effectively identified, and a variant with superior performance and capability was constructed. Hence, this study proved the effectiveness of this strategy in engineering GH5_5 cellulases and provided a potential competitor for industrial applications. Furthermore, this study sheds new light on engineering TIM-barrel proteins.
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12
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Listov D, Lipsh‐Sokolik R, Rosset S, Yang C, Correia BE, Fleishman SJ. Assessing and enhancing foldability in designed proteins. Protein Sci 2022; 31:e4400. [PMID: 36040259 PMCID: PMC9375437 DOI: 10.1002/pro.4400] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/09/2022] [Accepted: 07/11/2022] [Indexed: 11/11/2022]
Abstract
Recent advances in protein-design methodology have led to a dramatic increase in reliability and scale. With these advances, dozens and even thousands of designed proteins are automatically generated and screened. Nevertheless, the success rate, particularly in design of functional proteins, is low and fundamental goals such as reliable de novo design of efficient enzymes remain beyond reach. Experimental analyses have consistently indicated that a major reason for design failure is inaccuracy and misfolding relative to the design conception. To address this challenge, we describe complementary methods to diagnose and ameliorate suboptimal regions in designed proteins: first, we develop a Rosetta atomistic computational mutation scanning approach to detect energetically suboptimal positions in designs (available on a web server https://pSUFER.weizmann.ac.il); second, we demonstrate that AlphaFold2 ab initio structure prediction flags regions that may misfold in designed enzymes and binders; and third, we focus FuncLib design calculations on suboptimal positions in a previously designed low-efficiency enzyme, improving its catalytic efficiency by 330-fold. Furthermore, applied to a de novo designed protein that exhibited limited stability, the same approach markedly improved stability and expressibility. Thus, foldability analysis and enhancement may dramatically increase the success rate in design of functional proteins.
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Affiliation(s)
- Dina Listov
- Department of Biomolecular SciencesWeizmann Institute of ScienceRehovotIsrael
| | | | - Stéphane Rosset
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Che Yang
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Bruno E. Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
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13
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van Lente JJ, Baig MI, de Vos WM, Lindhoud S. Biocatalytic membranes through aqueous phase separation. J Colloid Interface Sci 2022; 616:903-910. [DOI: 10.1016/j.jcis.2022.02.094] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/26/2022] [Accepted: 02/20/2022] [Indexed: 12/31/2022]
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14
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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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15
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Wu L, Qin L, Nie Y, Xu Y, Zhao YL. Computer-aided understanding and engineering of enzymatic selectivity. Biotechnol Adv 2021; 54:107793. [PMID: 34217814 DOI: 10.1016/j.biotechadv.2021.107793] [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: 02/03/2021] [Revised: 04/26/2021] [Accepted: 06/28/2021] [Indexed: 12/26/2022]
Abstract
Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally occurring enzymes are often inefficient or have undesired selectivity toward non-native substrates hinders the broadening of biocatalytic applications. To match the demands of specific selectivity in asymmetric synthesis, biochemists have implemented various computer-aided strategies in understanding and engineering enzymatic selectivity, diversifying the available repository of artificial enzymes. Here, given that the entire asymmetric catalytic cycle, involving precise interactions within the active pocket and substrate transport in the enzyme channel, could affect the enzymatic efficiency and selectivity, we presented a comprehensive overview of the computer-aided workflow for enzymatic selectivity. This review includes a mechanistic understanding of enzymatic selectivity based on quantum mechanical calculations, rational design of enzymatic selectivity guided by enzyme-substrate interactions, and enzymatic selectivity regulation via enzyme channel engineering. Finally, we discussed the computational paradigm for designing enzyme selectivity in silico to facilitate the advancement of asymmetric biosynthesis.
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Affiliation(s)
- Lunjie Wu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Lei Qin
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yao Nie
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Suqian Industrial Technology Research Institute of Jiangnan University, Suqian 223814, China.
| | - Yan Xu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China.
| | - Yi-Lei Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, MOE-LSB & MOE-LSC, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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16
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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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17
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Romero-Romero S, Kordes S, Michel F, Höcker B. Evolution, folding, and design of TIM barrels and related proteins. Curr Opin Struct Biol 2021; 68:94-104. [PMID: 33453500 PMCID: PMC8250049 DOI: 10.1016/j.sbi.2020.12.007] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 12/13/2020] [Accepted: 12/14/2020] [Indexed: 12/16/2022]
Abstract
Proteins are chief actors in life that perform a myriad of exquisite functions. This diversity has been enabled through the evolution and diversification of protein folds. Analysis of sequences and structures strongly suggest that numerous protein pieces have been reused as building blocks and propagated to many modern folds. This information can be traced to understand how the protein world has diversified. In this review, we discuss the latest advances in the analysis of protein evolutionary units, and we use as a model system one of the most abundant and versatile topologies, the TIM-barrel fold, to highlight the existing common principles that interconnect protein evolution, structure, folding, function, and design.
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Affiliation(s)
| | - Sina Kordes
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany
| | - Florian Michel
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany
| | - Birte Höcker
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany.
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18
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Kriegel M, Wiederanders HJ, Alkhashrom S, Eichler J, Muller YA. A PROSS-designed extensively mutated estrogen receptor α variant displays enhanced thermal stability while retaining native allosteric regulation and structure. Sci Rep 2021; 11:10509. [PMID: 34006920 PMCID: PMC8131754 DOI: 10.1038/s41598-021-89785-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/22/2021] [Indexed: 11/13/2022] Open
Abstract
Protein stability limitations often hamper the exploration of proteins as drug targets. Here, we show that the application of PROSS server algorithms to the ligand-binding domain of human estrogen receptor alpha (hERα) enabled the development of variant ERPRS* that comprises 24 amino acid substitutions and exhibits multiple improved characteristics. The protein displays enhanced production rates in E. coli, crystallizes readily and its thermal stability is increased significantly by 23 °C. hERα is a nuclear receptor (NR) family member. In NRs, protein function is allosterically regulated by its interplay with small molecule effectors and the interaction with coregulatory proteins. The in-depth characterization of ERPRS* shows that these cooperative effects are fully preserved despite that 10% of all residues were substituted. Crystal structures reveal several salient features, i.e. the introduction of a tyrosine corner in a helix-loop-helix segment and the formation of a novel surface salt bridge network possibly explaining the enhanced thermal stability. ERPRS* shows that prior successes in computational approaches for stabilizing proteins can be extended to proteins with complex allosteric regulatory behaviors as present in NRs. Since NRs including hERα are implicated in multiple diseases, our ERPRS* variant shows significant promise for facilitating the development of novel hERα modulators.
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Affiliation(s)
- Mark Kriegel
- Division of Biotechnology, Department of Biology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Hanna J Wiederanders
- Division of Biotechnology, Department of Biology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sewar Alkhashrom
- Department of Chemistry and Pharmacy, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jutta Eichler
- Department of Chemistry and Pharmacy, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Yves A Muller
- Division of Biotechnology, Department of Biology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
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19
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Madhavan A, Arun KB, Binod P, Sirohi R, Tarafdar A, Reshmy R, Kumar Awasthi M, Sindhu R. Design of novel enzyme biocatalysts for industrial bioprocess: Harnessing the power of protein engineering, high throughput screening and synthetic biology. BIORESOURCE TECHNOLOGY 2021; 325:124617. [PMID: 33450638 DOI: 10.1016/j.biortech.2020.124617] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 12/19/2020] [Accepted: 12/22/2020] [Indexed: 05/13/2023]
Abstract
Biocatalysts have wider applications in various industries. Biocatalysts are generating bigger attention among researchers due to their unique catalytic properties like activity, specificity and stability. However the industrial use of many enzymes is hindered by low catalytic efficiency and stability during industrial processes. Properties of enzymes can be altered by protein engineering. Protein engineers are increasingly study the structure-function characteristics, engineering attributes, design of computational tools for enzyme engineering, and functional screening processes to improve the design and applications of enzymes. The potent and innovative techniques of enzyme engineering deliver outstanding opportunities for tailoring industrially important enzymes for the versatile production of biochemicals. An overview of the current trends in enzyme engineering is explored with important representative examples.
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Affiliation(s)
- Aravind Madhavan
- Rajiv Gandhi Centre for Biotechnology, Trivandrum 695 014, India
| | - K B Arun
- Rajiv Gandhi Centre for Biotechnology, Trivandrum 695 014, India
| | - Parameswaran Binod
- Microbial Processes and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Trivandrum 695 019, India
| | - Ranjna Sirohi
- The Center for Energy and Environmental Sustainability, Lucknow 226 010, Uttar Pradesh, India
| | - Ayon Tarafdar
- Division of Livestock Production and Management, ICAR - Indian Veterinary Research Institute, Izatnagar, Bareilly 243 122, Uttar Pradesh, India
| | - R Reshmy
- Post Graduate and Research Department of Chemistry, Bishop Moore College, Mavelikara 690 110, Kerala, India
| | - Mukesh Kumar Awasthi
- College of Natural Resources and Environment, North West A & F University, Yangling, Shaanxi 712 100, China
| | - Raveendran Sindhu
- Microbial Processes and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Trivandrum 695 019, India.
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20
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Peleg Y, Vincentelli R, Collins BM, Chen KE, Livingstone EK, Weeratunga S, Leneva N, Guo Q, Remans K, Perez K, Bjerga GEK, Larsen Ø, Vaněk O, Skořepa O, Jacquemin S, Poterszman A, Kjær S, Christodoulou E, Albeck S, Dym O, Ainbinder E, Unger T, Schuetz A, Matthes S, Bader M, de Marco A, Storici P, Semrau MS, Stolt-Bergner P, Aigner C, Suppmann S, Goldenzweig A, Fleishman SJ. Community-Wide Experimental Evaluation of the PROSS Stability-Design Method. J Mol Biol 2021; 433:166964. [PMID: 33781758 DOI: 10.1016/j.jmb.2021.166964] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 03/08/2021] [Accepted: 03/22/2021] [Indexed: 10/21/2022]
Abstract
Recent years have seen a dramatic improvement in protein-design methodology. Nevertheless, most methods demand expert intervention, limiting their widespread adoption. By contrast, the PROSS algorithm for improving protein stability and heterologous expression levels has been successfully applied to a range of challenging enzymes and binding proteins. Here, we benchmark the application of PROSS as a stand-alone tool for protein scientists with no or limited experience in modeling. Twelve laboratories from the Protein Production and Purification Partnership in Europe (P4EU) challenged the PROSS algorithm with 14 unrelated protein targets without support from the PROSS developers. For each target, up to six designs were evaluated for expression levels and in some cases, for thermal stability and activity. In nine targets, designs exhibited increased heterologous expression levels either in prokaryotic and/or eukaryotic expression systems under experimental conditions that were tailored for each target protein. Furthermore, we observed increased thermal stability in nine of ten tested targets. In two prime examples, the human Stem Cell Factor (hSCF) and human Cadherin-Like Domain (CLD12) from the RET receptor, the wild type proteins were not expressible as soluble proteins in E. coli, yet the PROSS designs exhibited high expression levels in E. coli and HEK293 cells, respectively, and improved thermal stability. We conclude that PROSS may improve stability and expressibility in diverse cases, and that improvement typically requires target-specific expression conditions. This study demonstrates the strengths of community-wide efforts to probe the generality of new methods and recommends areas for future research to advance practically useful algorithms for protein science.
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Affiliation(s)
- Yoav Peleg
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel.
| | - Renaud Vincentelli
- Unité Mixte de Recherche (UMR) 7257, Centre National de la Recherche Scientifique (CNRS) Aix-Marseille Université, Architecture et Fonction des Macromolécules Biologiques (AFMB), Marseille, France
| | - Brett M Collins
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Kai-En Chen
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Emma K Livingstone
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Saroja Weeratunga
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Natalya Leneva
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Qian Guo
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Kim Remans
- European Molecular Biology Laboratory (EMBL), Protein Expression and Purification Core Facility, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Kathryn Perez
- European Molecular Biology Laboratory (EMBL), Protein Expression and Purification Core Facility, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Gro E K Bjerga
- NORCE Norwegian Research Centre, Postboks 22 Nygårdstangen, 5038 Bergen, Norway
| | - Øivind Larsen
- NORCE Norwegian Research Centre, Postboks 22 Nygårdstangen, 5038 Bergen, Norway
| | - Ondřej Vaněk
- Department of Biochemistry, Faculty of Science, Charles University, Hlavova 2030/8, 12840 Prague, Czech Republic
| | - Ondřej Skořepa
- Department of Biochemistry, Faculty of Science, Charles University, Hlavova 2030/8, 12840 Prague, Czech Republic
| | - Sophie Jacquemin
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Centre National de la Recherche Scientifique (CNRS), UMR 7104, Institut National de la Santé et de la Recherche Médicale (INSERM), U1258, Université de Strasbourg, France
| | - Arnaud Poterszman
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Centre National de la Recherche Scientifique (CNRS), UMR 7104, Institut National de la Santé et de la Recherche Médicale (INSERM), U1258, Université de Strasbourg, France
| | - Svend Kjær
- Structural Biology Science Technology Platform, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Evangelos Christodoulou
- Structural Biology Science Technology Platform, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Shira Albeck
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Orly Dym
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Elena Ainbinder
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Tamar Unger
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Anja Schuetz
- Max-Delbrück Center for Molecular Medicine (MDC), Robert-Rössle-Straße 10, 13125 Berlin-Buch, Germany
| | - Susann Matthes
- Max-Delbrück Center for Molecular Medicine (MDC), Robert-Rössle-Straße 10, 13125 Berlin-Buch, Germany
| | - Michael Bader
- Max-Delbrück Center for Molecular Medicine (MDC), Robert-Rössle-Straße 10, 13125 Berlin-Buch, Germany; University of Lübeck, Institute for Biology, Ratzeburger Allee 160, 23562 Lübeck, Germany; Charité University Medicine, Charitéplatz 1, 10117 Berlin, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
| | - Ario de Marco
- Laboratory for Environmental and Life Sciences, University of Nova Gorica, Slovenia
| | - Paola Storici
- Elettra Sincrotrone Trieste - SS 14 - km 163, 5 in Area Science Park, 34149 Basovizza, Trieste, Italy
| | - Marta S Semrau
- Elettra Sincrotrone Trieste - SS 14 - km 163, 5 in Area Science Park, 34149 Basovizza, Trieste, Italy
| | - Peggy Stolt-Bergner
- Vienna Biocenter Core Facilities GmbH, Dr. Bohr-gasse 3, 1030 Vienna, Austria
| | - Christian Aigner
- Vienna Biocenter Core Facilities GmbH, Dr. Bohr-gasse 3, 1030 Vienna, Austria
| | - Sabine Suppmann
- Max-Planck Institute of Biochemistry, Biochemistry Core Facility, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Adi Goldenzweig
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.
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21
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Caldwell SJ, Haydon IC, Piperidou N, Huang PS, Bick MJ, Sjöström HS, Hilvert D, Baker D, Zeymer C. Tight and specific lanthanide binding in a de novo TIM barrel with a large internal cavity designed by symmetric domain fusion. Proc Natl Acad Sci U S A 2020; 117:30362-30369. [PMID: 33203677 PMCID: PMC7720202 DOI: 10.1073/pnas.2008535117] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
De novo protein design has succeeded in generating a large variety of globular proteins, but the construction of protein scaffolds with cavities that could accommodate large signaling molecules, cofactors, and substrates remains an outstanding challenge. The long, often flexible loops that form such cavities in many natural proteins are difficult to precisely program and thus challenging for computational protein design. Here we describe an alternative approach to this problem. We fused two stable proteins with C2 symmetry-a de novo designed dimeric ferredoxin fold and a de novo designed TIM barrel-such that their symmetry axes are aligned to create scaffolds with large cavities that can serve as binding pockets or enzymatic reaction chambers. The crystal structures of two such designs confirm the presence of a 420 cubic Ångström chamber defined by the top of the designed TIM barrel and the bottom of the ferredoxin dimer. We functionalized the scaffold by installing a metal-binding site consisting of four glutamate residues close to the symmetry axis. The protein binds lanthanide ions with very high affinity as demonstrated by tryptophan-enhanced terbium luminescence. This approach can be extended to other metals and cofactors, making this scaffold a modular platform for the design of binding proteins and biocatalysts.
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Affiliation(s)
- Shane J Caldwell
- Department of Biochemistry, University of Washington, Seattle, WA 98195
- Institute for Protein Design, University of Washington, Seattle, WA 98195
| | - Ian C Haydon
- Department of Biochemistry, University of Washington, Seattle, WA 98195
- Institute for Protein Design, University of Washington, Seattle, WA 98195
| | - Nikoletta Piperidou
- Laboratory of Organic Chemistry, Eidgenössische Technische Hochschule (ETH) Zürich, 8093 Zürich, Switzerland
| | - Po-Ssu Huang
- Department of Biochemistry, University of Washington, Seattle, WA 98195
- Institute for Protein Design, University of Washington, Seattle, WA 98195
- Department of Bioengineering, Stanford University, Shriram Center for Bioengineering and Chemical Engineering, Stanford, CA 94305
| | - Matthew J Bick
- Department of Biochemistry, University of Washington, Seattle, WA 98195
- Institute for Protein Design, University of Washington, Seattle, WA 98195
| | - H Sebastian Sjöström
- Laboratory of Organic Chemistry, Eidgenössische Technische Hochschule (ETH) Zürich, 8093 Zürich, Switzerland
| | - Donald Hilvert
- Laboratory of Organic Chemistry, Eidgenössische Technische Hochschule (ETH) Zürich, 8093 Zürich, Switzerland
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195;
- Institute for Protein Design, University of Washington, Seattle, WA 98195
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195
| | - Cathleen Zeymer
- Laboratory of Organic Chemistry, Eidgenössische Technische Hochschule (ETH) Zürich, 8093 Zürich, Switzerland;
- Department of Chemistry, Technische Universität München, 85747 Garching, Germany
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22
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Ding D, Li J, Bai D, Fang H, Lin J, Zhang D. Biosensor-based monitoring of the central metabolic pathway metabolites. Biosens Bioelectron 2020; 167:112456. [DOI: 10.1016/j.bios.2020.112456] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 07/08/2020] [Accepted: 07/14/2020] [Indexed: 12/31/2022]
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23
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Lipsh-Sokolik R, Listov D, Fleishman SJ. The AbDesign computational pipeline for modular backbone assembly and design of binders and enzymes. Protein Sci 2020; 30:151-159. [PMID: 33040418 PMCID: PMC7737780 DOI: 10.1002/pro.3970] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/07/2020] [Accepted: 10/09/2020] [Indexed: 12/12/2022]
Abstract
The functional sites of many protein families are dominated by diverse backbone regions that lack secondary structure (loops) but fold stably into their functionally competent state. Nevertheless, the design of structured loop regions from scratch, especially in functional sites, has met with great difficulty. We therefore developed an approach, called AbDesign, to exploit the natural modularity of many protein families and computationally assemble a large number of new backbones by combining naturally occurring modular fragments. This strategy yielded large, atomically accurate, and highly efficient proteins, including antibodies and enzymes exhibiting dozens of mutations from any natural protein. The combinatorial backbone‐conformation space that can be accessed by AbDesign even for a modestly sized family of homologs may exceed the diversity in the entire PDB, providing the sub‐Ångstrom level of control over the positioning of active‐site groups that is necessary for obtaining highly active proteins. This manuscript describes how to implement the pipeline using code that is freely available at https://github.com/Fleishman‐Lab/AbDesign_for_enzymes.
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Affiliation(s)
| | - Dina Listov
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
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24
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Chowdhury R, Grisewood MJ, Boorla VS, Yan Q, Pfleger BF, Maranas CD. IPRO+/-: Computational Protein Design Tool Allowing for Insertions and Deletions. Structure 2020; 28:1344-1357.e4. [PMID: 32857964 DOI: 10.1016/j.str.2020.08.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 07/01/2020] [Accepted: 08/07/2020] [Indexed: 12/30/2022]
Abstract
Insertions and deletions (indels) in protein sequences alter the residue spacing along the polypeptide backbone and consequently open up possibilities for tuning protein function in a way that is inaccessible by amino acid substitution alone. We describe an optimization-based computational protein redesign approach centered around predicting beneficial combinations of indels along with substitutions and also obtain putative substrate-docked structures for these protein variants. This modified algorithmic capability would be of interest for enzyme engineering and broadly inform other protein design tasks. We highlight this capability by (1) identifying active variants of a bacterial thioesterase enzyme ('TesA) with experimental corroboration, (2) recapitulating existing active TEM-1 β-Lactamase sequences of different sizes, and (3) identifying shorter 4-Coumarate:CoA ligases with enhanced in vitro activities toward non-native substrates. A separate PyRosetta-based open-source tool, Indel-Maker (http://www.maranasgroup.com/software.htm), has also been created to construct computational models of user-defined protein variants with specific indels and substitutions.
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Affiliation(s)
- Ratul Chowdhury
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Matthew J Grisewood
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Veda Sheersh Boorla
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Qiang Yan
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Brian F Pfleger
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
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25
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Pang C, Yin X, Zhang G, Liu S, Zhou J, Li J, Du G. Current progress and prospects of enzyme technologies in future foods. ACTA ACUST UNITED AC 2020. [DOI: 10.1007/s43393-020-00008-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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26
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Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RYR, Watkins A, Zimmerman L, Bonneau R. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 2020; 17:665-680. [PMID: 32483333 PMCID: PMC7603796 DOI: 10.1038/s41592-020-0848-2] [Citation(s) in RCA: 417] [Impact Index Per Article: 104.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
| | - Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Steven M Lewis
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biochemistry, Duke University, Durham, NC, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Aprahamian
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kyle A Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, USA
| | - Patrick Barth
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Benjamin Basanta
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Biological Physics Structure and Design PhD Program, University of Washington, Seattle, WA, USA
| | - Brian J Bender
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Kristin Blacklock
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Scott E Boyken
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Phil Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Bystroff
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Patrick Conway
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lorna Dsilva
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Alexander S Ford
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Brandon Frenz
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Darwin Y Fu
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Sharon Guffy
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott Horowitz
- Department of Chemistry & Biochemistry, University of Denver, Denver, CO, USA
- The Knoebel Institute for Healthy Aging, University of Denver, Denver, CO, USA
| | - Po-Ssu Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Tim M Jacobs
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - David K Johnson
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - John Karanicolas
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Hamed Khakzad
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
| | - Karen R Khar
- Cyrus Biotechnology, Seattle, WA, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Sagar D Khare
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Indigo C King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Robert Kleffner
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daisuke Kuroda
- Medical Device Development and Regulation Research Center, School of Engineering, University of Tokyo, Tokyo, Japan
- Department of Bioengineering, School of Engineering, University of Tokyo, Tokyo, Japan
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemistry, Franklin & Marshall College, Lancaster, PA, USA
| | - Jason K Lai
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Thomas Linsky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Nir London
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Joseph H Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lars Malmström
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Enrique Marcos
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Research in Biomedicine Barcelona, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Orly Marcu
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nicholas A Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Santrupti Nerli
- Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Christoffer Norn
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shane Ó'Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Noah Ollikainen
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Michael S Pacella
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ryan E Pavlovicz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Manasi Pethe
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Kala Bharath Pilla
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Barak Raveh
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Aliza Rubenstein
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Marion F Sauer
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Andreas Scheck
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Sedan
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alexander M Sevy
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Lei Shi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | | | - Shannon Smith
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Yifan Song
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Amelie Stein
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Szegedy
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Frank D Teets
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Summer B Thyme
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ray Yu-Ruei Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Lior Zimmerman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
- Department of Computer Science, New York University, New York, NY, USA.
- Center for Data Science, New York University, New York, NY, USA.
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27
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Weinstein J, Khersonsky O, Fleishman SJ. Practically useful protein-design methods combining phylogenetic and atomistic calculations. Curr Opin Struct Biol 2020; 63:58-64. [PMID: 32505941 DOI: 10.1016/j.sbi.2020.04.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 04/06/2020] [Indexed: 12/11/2022]
Abstract
Our ability to design new or improved biomolecular activities depends on understanding the sequence-function relationships in proteins. The large size and fold complexity of most proteins, however, obscure these relationships, and protein-optimization methods continue to rely on laborious experimental iterations. Recently, a deeper understanding of the roles of stability-threshold effects and biomolecular epistasis in proteins has led to the development of hybrid methods that combine phylogenetic analysis with atomistic design calculations. These methods enable reliable and even single-step optimization of protein stability, expressibility, and activity in proteins that were considered outside the scope of computational design. Furthermore, ancestral-sequence reconstruction produces insights on missing links in the evolution of enzymes and binders that may be used in protein design. Through the combination of phylogenetic and atomistic calculations, the long-standing goal of general computational methods that can be universally applied to study and optimize proteins finally seems within reach.
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Affiliation(s)
- Jonathan Weinstein
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - 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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28
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Neotropical termite microbiomes as sources of novel plant cell wall degrading enzymes. Sci Rep 2020; 10:3864. [PMID: 32123275 PMCID: PMC7052144 DOI: 10.1038/s41598-020-60850-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 12/24/2019] [Indexed: 11/08/2022] Open
Abstract
In this study, we used shotgun metagenomic sequencing to characterise the microbial metabolic potential for lignocellulose transformation in the gut of two colonies of Argentine higher termite species with different feeding habits, Cortaritermes fulviceps and Nasutitermes aquilinus. Our goal was to assess the microbial community compositions and metabolic capacity, and to identify genes involved in lignocellulose degradation. Individuals from both termite species contained the same five dominant bacterial phyla (Spirochaetes, Firmicutes, Proteobacteria, Fibrobacteres and Bacteroidetes) although with different relative abundances. However, detected functional capacity varied, with C. fulviceps (a grass-wood-feeder) gut microbiome samples containing more genes related to amino acid metabolism, whereas N. aquilinus (a wood-feeder) gut microbiome samples were enriched in genes involved in carbohydrate metabolism and cellulose degradation. The C. fulviceps gut microbiome was enriched specifically in genes coding for debranching- and oligosaccharide-degrading enzymes. These findings suggest an association between the primary food source and the predicted categories of the enzymes present in the gut microbiomes of each species. To further investigate the termite microbiomes as sources of biotechnologically relevant glycosyl hydrolases, a putative GH10 endo-β-1,4-xylanase, Xyl10E, was cloned and expressed in Escherichia coli. Functional analysis of the recombinant metagenome-derived enzyme showed high specificity towards beechwood xylan (288.1 IU/mg), with the optimum activity at 50 °C and a pH-activity range from 5 to 10. These characteristics suggest that Xy110E may be a promising candidate for further development in lignocellulose deconstruction applications.
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Affiliation(s)
- Marco Foscato
- Department of Chemistry, University of Bergen, Allégaten 41, N-5007 Bergen, Norway
| | - Vidar R. Jensen
- Department of Chemistry, University of Bergen, Allégaten 41, N-5007 Bergen, Norway
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30
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Kuhlman B, Bradley P. Advances in protein structure prediction and design. Nat Rev Mol Cell Biol 2019; 20:681-697. [PMID: 31417196 PMCID: PMC7032036 DOI: 10.1038/s41580-019-0163-x] [Citation(s) in RCA: 387] [Impact Index Per Article: 77.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2019] [Indexed: 12/18/2022]
Abstract
The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.
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Affiliation(s)
- Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC, USA.
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
| | - Philip Bradley
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
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31
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Towards functional de novo designed proteins. Curr Opin Chem Biol 2019; 52:102-111. [DOI: 10.1016/j.cbpa.2019.06.011] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 04/25/2019] [Accepted: 06/06/2019] [Indexed: 12/31/2022]
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32
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Weinstein JY, Elazar A, Fleishman SJ. A lipophilicity-based energy function for membrane-protein modelling and design. PLoS Comput Biol 2019; 15:e1007318. [PMID: 31461441 PMCID: PMC6736313 DOI: 10.1371/journal.pcbi.1007318] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 09/10/2019] [Accepted: 08/01/2019] [Indexed: 01/14/2023] Open
Abstract
Membrane-protein design is an exciting and increasingly successful research area which has led to landmarks including the design of stable and accurate membrane-integral proteins based on coiled-coil motifs. Design of topologically more complex proteins, such as most receptors, channels, and transporters, however, demands an energy function that balances contributions from intra-protein contacts and protein-membrane interactions. Recent advances in water-soluble all-atom energy functions have increased the accuracy in structure-prediction benchmarks. The plasma membrane, however, imposes different physical constraints on protein solvation. To understand these constraints, we recently developed a high-throughput experimental screen, called dsTβL, and inferred apparent insertion energies for each amino acid at dozens of positions across the bacterial plasma membrane. Here, we express these profiles as lipophilicity energy terms in Rosetta and demonstrate that the new energy function outperforms previous ones in modelling and design benchmarks. Rosetta ab initio simulations starting from an extended chain recapitulate two-thirds of the experimentally determined structures of membrane-spanning homo-oligomers with <2.5Å root-mean-square deviation within the top-predicted five models (available online: http://tmhop.weizmann.ac.il). Furthermore, in two sequence-design benchmarks, the energy function improves discrimination of stabilizing point mutations and recapitulates natural membrane-protein sequences of known structure, thereby recommending this new energy function for membrane-protein modelling and design.
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Affiliation(s)
| | - Assaf Elazar
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Sarel Jacob Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
- * E-mail:
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Ribeiro LF, Amarelle V, Alves LDF, Viana de Siqueira GM, Lovate GL, Borelli TC, Guazzaroni ME. Genetically Engineered Proteins to Improve Biomass Conversion: New Advances and Challenges for Tailoring Biocatalysts. Molecules 2019; 24:molecules24162879. [PMID: 31398877 PMCID: PMC6719137 DOI: 10.3390/molecules24162879] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 07/30/2019] [Accepted: 08/06/2019] [Indexed: 01/02/2023] Open
Abstract
Protein engineering emerged as a powerful approach to generate more robust and efficient biocatalysts for bio-based economy applications, an alternative to ecologically toxic chemistries that rely on petroleum. On the quest for environmentally friendly technologies, sustainable and low-cost resources such as lignocellulosic plant-derived biomass are being used for the production of biofuels and fine chemicals. Since most of the enzymes used in the biorefinery industry act in suboptimal conditions, modification of their catalytic properties through protein rational design and in vitro evolution techniques allows the improvement of enzymatic parameters such as specificity, activity, efficiency, secretability, and stability, leading to better yields in the production lines. This review focuses on the current application of protein engineering techniques for improving the catalytic performance of enzymes used to break down lignocellulosic polymers. We discuss the use of both classical and modern methods reported in the literature in the last five years that allowed the boosting of biocatalysts for biomass degradation.
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Affiliation(s)
- Lucas Ferreira Ribeiro
- Department of Biology, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, University of São Paulo, Ribeirão Preto 14040-901, Brazil.
| | - Vanesa Amarelle
- Department of Microbial Biochemistry and Genomics, Biological Research Institute Clemente Estable, Montevideo, PC 11600, Uruguay
| | - Luana de Fátima Alves
- Department of Biochemistry and Immunology, Faculdade de Medicina de Ribeirão Preto, University of São Paulo, Ribeirão Preto 14049-900, Brazil
| | | | - Gabriel Lencioni Lovate
- Department of Biochemistry and Immunology, Faculdade de Medicina de Ribeirão Preto, University of São Paulo, Ribeirão Preto 14049-900, Brazil
| | - Tiago Cabral Borelli
- Department of Biology, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, University of São Paulo, Ribeirão Preto 14040-901, Brazil
| | - María-Eugenia Guazzaroni
- Department of Biology, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, University of São Paulo, Ribeirão Preto 14040-901, Brazil.
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Warszawski S, Borenstein Katz A, Lipsh R, Khmelnitsky L, Ben Nissan G, Javitt G, Dym O, Unger T, Knop O, Albeck S, Diskin R, Fass D, Sharon M, Fleishman SJ. Optimizing antibody affinity and stability by the automated design of the variable light-heavy chain interfaces. PLoS Comput Biol 2019; 15:e1007207. [PMID: 31442220 PMCID: PMC6728052 DOI: 10.1371/journal.pcbi.1007207] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 09/05/2019] [Accepted: 06/21/2019] [Indexed: 11/18/2022] Open
Abstract
Antibodies developed for research and clinical applications may exhibit suboptimal stability, expressibility, or affinity. Existing optimization strategies focus on surface mutations, whereas natural affinity maturation also introduces mutations in the antibody core, simultaneously improving stability and affinity. To systematically map the mutational tolerance of an antibody variable fragment (Fv), we performed yeast display and applied deep mutational scanning to an anti-lysozyme antibody and found that many of the affinity-enhancing mutations clustered at the variable light-heavy chain interface, within the antibody core. Rosetta design combined enhancing mutations, yielding a variant with tenfold higher affinity and substantially improved stability. To make this approach broadly accessible, we developed AbLIFT, an automated web server that designs multipoint core mutations to improve contacts between specific Fv light and heavy chains (http://AbLIFT.weizmann.ac.il). We applied AbLIFT to two unrelated antibodies targeting the human antigens VEGF and QSOX1. Strikingly, the designs improved stability, affinity, and expression yields. The results provide proof-of-principle for bypassing laborious cycles of antibody engineering through automated computational affinity and stability design.
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Affiliation(s)
- Shira Warszawski
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | | | - Rosalie Lipsh
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Lev Khmelnitsky
- Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Gili Ben Nissan
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Gabriel Javitt
- Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Orly Dym
- Israel Structural Proteomics Center, Weizmann Institute of Science, Rehovot, Israel
| | - Tamar Unger
- Israel Structural Proteomics Center, Weizmann Institute of Science, Rehovot, Israel
| | - Orli Knop
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shira Albeck
- Israel Structural Proteomics Center, Weizmann Institute of Science, Rehovot, Israel
| | - Ron Diskin
- Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Deborah Fass
- Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Michal Sharon
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Sarel J. Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
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Baier F, Hong N, Yang G, Pabis A, Miton CM, Barrozo A, Carr PD, Kamerlin SC, Jackson CJ, Tokuriki N. Cryptic genetic variation shapes the adaptive evolutionary potential of enzymes. eLife 2019; 8:40789. [PMID: 30719972 PMCID: PMC6372284 DOI: 10.7554/elife.40789] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Accepted: 01/22/2019] [Indexed: 12/21/2022] Open
Abstract
Genetic variation among orthologous proteins can cause cryptic phenotypic properties that only manifest in changing environments. Such variation may impact the evolvability of proteins, but the underlying molecular basis remains unclear. Here, we performed comparative directed evolution of four orthologous metallo-β-lactamases toward a new function and found that different starting genotypes evolved to distinct evolutionary outcomes. Despite a low initial fitness, one ortholog reached a significantly higher fitness plateau than its counterparts, via increasing catalytic activity. By contrast, the ortholog with the highest initial activity evolved to a less-optimal and phenotypically distinct outcome through changes in expression, oligomerization and activity. We show how cryptic molecular properties and conformational variation of active site residues in the initial genotypes cause epistasis, that could lead to distinct evolutionary outcomes. Our work highlights the importance of understanding the molecular details that connect genetic variation to protein function to improve the prediction of protein evolution.
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Affiliation(s)
- Florian Baier
- Michael Smith Laboratory, University of British Columbia, Vancouver, Canada
| | - Nansook Hong
- Research School of Chemistry, Australian National University, Canberra, Australia
| | - Gloria Yang
- Michael Smith Laboratory, University of British Columbia, Vancouver, Canada
| | - Anna Pabis
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Charlotte M Miton
- Michael Smith Laboratory, University of British Columbia, Vancouver, Canada
| | - Alexandre Barrozo
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Paul D Carr
- Research School of Chemistry, Australian National University, Canberra, Australia
| | - Shina Cl Kamerlin
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Colin J Jackson
- Research School of Chemistry, Australian National University, Canberra, Australia
| | - Nobuhiko Tokuriki
- Michael Smith Laboratory, University of British Columbia, Vancouver, Canada
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Netzer R, Listov D, Lipsh R, Dym O, Albeck S, Knop O, Kleanthous C, Fleishman SJ. Ultrahigh specificity in a network of computationally designed protein-interaction pairs. Nat Commun 2018; 9:5286. [PMID: 30538236 PMCID: PMC6290019 DOI: 10.1038/s41467-018-07722-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 11/21/2018] [Indexed: 01/21/2023] Open
Abstract
Protein networks in all organisms comprise homologous interacting pairs. In these networks, some proteins are specific, interacting with one or a few binding partners, whereas others are multispecific and bind a range of targets. We describe an algorithm that starts from an interacting pair and designs dozens of new pairs with diverse backbone conformations at the binding site as well as new binding orientations and sequences. Applied to a high-affinity bacterial pair, the algorithm results in 18 new ones, with cognate affinities from pico- to micromolar. Three pairs exhibit 3-5 orders of magnitude switch in specificity relative to the wild type, whereas others are multispecific, collectively forming a protein-interaction network. Crystallographic analysis confirms design accuracy, including in new backbones and polar interactions. Preorganized polar interaction networks are responsible for high specificity, thus defining design principles that can be applied to program synthetic cellular interaction networks of desired affinity and specificity. The molecular basis of ultrahigh specificity in protein-protein interactions remains obscure. The authors present a computational method to design atomically accurate new pairs exhibiting >100,000-fold specificity switches, generating a large and complex interaction network.
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Affiliation(s)
- Ravit Netzer
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Dina Listov
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Rosalie Lipsh
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Orly Dym
- Structural Proteomics Unit, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Shira Albeck
- Structural Proteomics Unit, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Orli Knop
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Colin Kleanthous
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel.
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D’Souza A, Torres J, Bhattacharjya S. Expanding heme-protein folding space using designed multi-heme β-sheet mini-proteins. Commun Chem 2018. [DOI: 10.1038/s42004-018-0078-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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38
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Automated Design of Efficient and Functionally Diverse Enzyme Repertoires. Mol Cell 2018; 72:178-186.e5. [PMID: 30270109 DOI: 10.1016/j.molcel.2018.08.033] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 08/02/2018] [Accepted: 08/21/2018] [Indexed: 11/22/2022]
Abstract
Substantial improvements in enzyme activity demand multiple mutations at spatially proximal positions in the active site. Such mutations, however, often exhibit unpredictable epistatic (non-additive) effects on activity. Here we describe FuncLib, an automated method for designing multipoint mutations at enzyme active sites using phylogenetic analysis and Rosetta design calculations. We applied FuncLib to two unrelated enzymes, a phosphotriesterase and an acetyl-CoA synthetase. All designs were active, and most showed activity profiles that significantly differed from the wild-type and from one another. Several dozen designs with only 3-6 active-site mutations exhibited 10- to 4,000-fold higher efficiencies with a range of alternative substrates, including hydrolysis of the toxic organophosphate nerve agents soman and cyclosarin and synthesis of butyryl-CoA. FuncLib is implemented as a web server (http://FuncLib.weizmann.ac.il); it circumvents iterative, high-throughput experimental screens and opens the way to designing highly efficient and diverse catalytic repertoires.
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