1
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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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2
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Tan ZW, Tee WV, Guarnera E, Berezovsky IN. AlloMAPS 2: allosteric fingerprints of the AlphaFold and Pfam-trRosetta predicted structures for engineering and design. Nucleic Acids Res 2022; 51:D345-D351. [PMID: 36169226 PMCID: PMC9825619 DOI: 10.1093/nar/gkac828] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/26/2022] [Accepted: 09/15/2022] [Indexed: 01/29/2023] Open
Abstract
AlloMAPS 2 is an update of the Allosteric Mutation Analysis and Polymorphism of Signalling database, which contains data on allosteric communication obtained for predicted structures in the AlphaFold database (AFDB) and trRosetta-predicted Pfam domains. The data update contains Allosteric Signalling Maps (ASMs) and Allosteric Probing Maps (APMs) quantifying allosteric effects of mutations and of small probe binding, respectively. To ensure quality of the ASMs and APMs, we performed careful and accurate selection of protein sets containing high-quality predicted structures in both databases for each organism/structure, and the data is available for browsing and download. The data for remaining structures are available for download and should be used at user's discretion and responsibility. We believe these massive data can facilitate both diagnostics and drug design within the precision medicine paradigm. Specifically, it can be instrumental in the analysis of allosteric effects of pathological and rescue mutations, providing starting points for fragment-based design of allosteric effectors. The exhaustive character of allosteric signalling and probing fingerprints will be also useful in future developments of corresponding machine learning applications. The database is freely available at: http://allomaps.bii.a-star.edu.sg.
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Affiliation(s)
- Zhen Wah Tan
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Wei-Ven Tee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Enrico Guarnera
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Igor N Berezovsky
- To whom correspondence should be addressed. Tel: +65 6478 8269; Fax: +65 6478 9047;
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3
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Berezovsky IN, Nussinov R. Multiscale Allostery: Basic Mechanisms and Versatility in Diagnostics and Drug Design. J Mol Biol 2022; 434:167751. [PMID: 35863488 DOI: 10.1016/j.jmb.2022.167751] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671, Singapore; Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579, Singapore.
| | - Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboraory, National Cancer Institute, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
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4
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Tee WV, Wah Tan Z, Guarnera E, Berezovsky IN. Conservation and diversity in allosteric fingerprints of proteins for evolutionary-inspired engineering and design. J Mol Biol 2022; 434:167577. [PMID: 35395233 DOI: 10.1016/j.jmb.2022.167577] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 03/30/2022] [Accepted: 03/30/2022] [Indexed: 11/26/2022]
Abstract
Hand-in-hand work of physics and evolution delivered protein universe with diversity of forms, sizes, and functions. Pervasiveness and advantageous traits of allostery made it an important component of the protein function regulation, calling for thorough investigation of its structural determinants and evolution. Learning directly from nature, we explored here allosteric communication in several major folds and repeat proteins, including α/β and β-barrels, β-propellers, Ig-like fold, ankyrin and α/β leucine-rich repeat proteins, which provide structural platforms for many different enzymatic and signalling functions. We obtained a picture of conserved allosteric communication characteristic in different fold types, modifications of the structure-driven signalling patterns via sequence-determined divergence to specific functions, as well as emergence and potential diversification of allosteric regulation in multi-domain proteins and oligomeric assemblies. Our observations will be instrumental in facilitating the engineering and de novo design of proteins with allosterically regulated functions, including development of therapeutic biologics. In particular, results described here may guide the identification of the optimal structural platforms (e.g. fold type, size, and oligomerization states) and the types of diversifications/perturbations, such as mutations, effector binding, and order-disorder transition. The tunable allosteric linkage across distant regions can be used as a pivotal component in the design/engineering of modular biological systems beyond the traditional scaffolding function.
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Affiliation(s)
- Wei-Ven Tee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671
| | - Zhen Wah Tan
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671
| | - Enrico Guarnera
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671
| | - Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671; Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, Singapore 117597.
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5
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Tee WV, Tan ZW, Lee K, Guarnera E, Berezovsky IN. Exploring the Allosteric Territory of Protein Function. J Phys Chem B 2021; 125:3763-3780. [PMID: 33844527 DOI: 10.1021/acs.jpcb.1c00540] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
While the pervasiveness of allostery in proteins is commonly accepted, we further show the generic nature of allosteric mechanisms by analyzing here transmembrane ion-channel viroporin 3a and RNA-dependent RNA polymerase (RdRp) from SARS-CoV-2 along with metabolic enzymes isocitrate dehydrogenase 1 (IDH1) and fumarate hydratase (FH) implicated in cancers. Using the previously developed structure-based statistical mechanical model of allostery (SBSMMA), we share our experience in analyzing the allosteric signaling, predicting latent allosteric sites, inducing and tuning targeted allosteric response, and exploring the allosteric effects of mutations. This, yet incomplete list of phenomenology, forms a complex and unique allosteric territory of protein function, which should be thoroughly explored. We propose a generic computational framework, which not only allows one to obtain a comprehensive allosteric control over proteins but also provides an opportunity to approach the fragment-based design of allosteric effectors and drug candidates. The advantages of allosteric drugs over traditional orthosteric compounds, complemented by the emerging role of the allosteric effects of mutations in the expansion of the cancer mutational landscape and in the increased mutability of viral proteins, leave no choice besides further extensive studies of allosteric mechanisms and their biomedical implications.
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Affiliation(s)
- Wei-Ven Tee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117597, Singapore
| | - Zhen Wah Tan
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Keene Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Enrico Guarnera
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117597, Singapore
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6
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Allosteric drugs and mutations: chances, challenges, and necessity. Curr Opin Struct Biol 2020; 62:149-157. [DOI: 10.1016/j.sbi.2020.01.010] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 01/16/2020] [Indexed: 12/22/2022]
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7
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Khowsathit J, Bazzoli A, Cheng H, Karanicolas J. Computational Design of an Allosteric Antibody Switch by Deletion and Rescue of a Complex Structural Constellation. ACS CENTRAL SCIENCE 2020; 6:390-403. [PMID: 32232139 PMCID: PMC7099597 DOI: 10.1021/acscentsci.9b01065] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Indexed: 05/08/2023]
Abstract
Therapeutic monoclonal antibodies have transformed medicine, especially with regards to treating cancers and disorders of the immune system. More than 50 antibody-derived drugs have already reached the clinic, the majority of which target cytokines or cell-surface receptors. Unfortunately, many of these targets have pleiotropic functions: they serve multiple different roles, and often not all of these roles are disease-related. This can be problematic because antibodies act throughout the body, and systemic neutralization of such targets can lead to safety concerns. To address this, we have developed a strategy whereby an antibody's ability to recognize its antigen is modulated by a second layer of control, relying on addition of an exogenous small molecule. In previous studies, we began to explore this idea by introducing a deactivating tryptophan-to-glycine mutation in the domain-domain interface of a single-chain variable fragment (scFv), and then restoring activity by adding back indole to fit the designed cavity. Here, we now describe a novel computational strategy for enumerating larger cavities that can be formed by simultaneously introducing multiple adjacent large-to-small mutations; we then carry out a complementary virtual screen to identify druglike compounds to match each candidate cavity. We first demonstrate the utility of this strategy in a fluorescein-binding single-chain variable fragment (scFv) and experimentally characterize a triple mutant with reduced antigen-binding (Rip-3) that can be rescued using a complementary ligand (Stitch-3). Because our design is built upon conserved residues in the antibody framework, we then show that the same mutation/ligand pair can also be used to modulate antigen-binding in an scFv build from a completely unrelated framework. This set of residues is present in many therapeutic antibodies as well, suggesting that this mutation/ligand pair may serve as a general starting point for introducing ligand-dependence into many clinically relevant antibodies.
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Affiliation(s)
- Jittasak Khowsathit
- Program
in Molecular Therapeutics, Fox Chase Cancer
Center, Philadelphia, Pennsylvania 19111, United States
- Department of Molecular
Biosciences and Center for Computational Biology, University
of Kansas, Lawrence, Kansas 66045, United
States
| | - Andrea Bazzoli
- Department of Molecular
Biosciences and Center for Computational Biology, University
of Kansas, Lawrence, Kansas 66045, United
States
| | - Hong Cheng
- Program
in Molecular Therapeutics, Fox Chase Cancer
Center, Philadelphia, Pennsylvania 19111, United States
| | - John Karanicolas
- Program
in Molecular Therapeutics, Fox Chase Cancer
Center, Philadelphia, Pennsylvania 19111, United States
- Department of Molecular
Biosciences and Center for Computational Biology, University
of Kansas, Lawrence, Kansas 66045, United
States
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8
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Ghanbarpour A, Pinger C, Esmatpour Salmani R, Assar Z, Santos EM, Nosrati M, Pawlowski K, Spence D, Vasileiou C, Jin X, Borhan B, Geiger JH. Engineering the hCRBPII Domain-Swapped Dimer into a New Class of Protein Switches. J Am Chem Soc 2019; 141:17125-17132. [PMID: 31557439 DOI: 10.1021/jacs.9b04664] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Protein conformational switches or allosteric proteins play a key role in the regulation of many essential biological pathways. Nonetheless, the implementation of protein conformational switches in protein design applications has proven challenging, with only a few known examples that are not derivatives of naturally occurring allosteric systems. We have discovered that the domain-swapped (DS) dimer of hCRBPII undergoes a large and robust conformational change upon retinal binding, making it a potentially powerful template for the design of protein conformational switches. Atomic resolution structures of the apo- and holo-forms illuminate a simple, mechanical movement involving sterically driven torsion angle flipping of two residues that drive the motion. We further demonstrate that the conformational "readout" can be altered by addition of cross-domain disulfide bonds, also visualized at atomic resolution. Finally, as a proof of principle, we have created an allosteric metal binding site in the DS dimer, where ligand binding results in a reversible 5-fold loss of metal binding affinity. The high resolution structure of the metal-bound variant illustrates a well-formed metal binding site at the interface of the two domains of the DS dimer and confirms the design strategy for allosteric regulation.
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Affiliation(s)
- Alireza Ghanbarpour
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Cody Pinger
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Rahele Esmatpour Salmani
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Zahra Assar
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Elizabeth M Santos
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Meisam Nosrati
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Kathryn Pawlowski
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Dana Spence
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Chrysoula Vasileiou
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Xiangshu Jin
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Babak Borhan
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - James H Geiger
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
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9
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Berezovsky IN. Towards descriptor of elementary functions for protein design. Curr Opin Struct Biol 2019; 58:159-165. [PMID: 31352188 DOI: 10.1016/j.sbi.2019.06.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 06/18/2019] [Indexed: 11/18/2022]
Abstract
We review studies of the protein evolution that help to formulate rules for protein design. Acknowledging the fundamental importance of Dayhoff's provision on the emergence of functional proteins from short peptides, we discuss multiple evidences of the omnipresent partitioning of protein globules into structural/functional units, using which greatly facilitates the engineering and design efforts. Closed loops and elementary functional loops, which are descendants of ancient ring-like peptides that formed fist protein domains in agreement with Dayhoff's hypothesis, can be considered as basic units of protein structure and function. We argue that future developments in protein design approaches should consider descriptors of the elementary functions, which will help to complement designed scaffolds with functional signatures and flexibility necessary for their functions.
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Affiliation(s)
- Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), 30 Biopolis Street, #07-01, Matrix 138671, Singapore; Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579, Singapore.
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10
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Keedy DA. Journey to the center of the protein: allostery from multitemperature multiconformer X-ray crystallography. Acta Crystallogr D Struct Biol 2019; 75:123-137. [PMID: 30821702 PMCID: PMC6400254 DOI: 10.1107/s2059798318017941] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 12/19/2018] [Indexed: 02/08/2023] Open
Abstract
Proteins inherently fluctuate between conformations to perform functions in the cell. For example, they sample product-binding, transition-state-stabilizing and product-release states during catalysis, and they integrate signals from remote regions of the structure for allosteric regulation. However, there is a lack of understanding of how these dynamic processes occur at the basic atomic level. This gap can be at least partially addressed by combining variable-temperature (instead of traditional cryogenic temperature) X-ray crystallography with algorithms for modeling alternative conformations based on electron-density maps, in an approach called multitemperature multiconformer X-ray crystallography (MMX). Here, the use of MMX to reveal alternative conformations at different sites in a protein structure and to estimate the degree of energetic coupling between them is discussed. These insights can suggest testable hypotheses about allosteric mechanisms. Temperature is an easily manipulated experimental parameter, so the MMX approach is widely applicable to any protein that yields well diffracting crystals. Moreover, the general principles of MMX are extensible to other perturbations such as pH, pressure, ligand concentration etc. Future work will explore strategies for leveraging X-ray data across such perturbation series to more quantitatively measure how different parts of a protein structure are coupled to each other, and the consequences thereof for allostery and other aspects of protein function.
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Affiliation(s)
- Daniel A. Keedy
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, USA
- Department of Chemistry and Biochemistry, City College of New York, New York, USA
- PhD Programs in Chemistry and Biochemistry, The Graduate Center of the City University of New York, New York, USA
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11
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Highly active enzymes by automated combinatorial backbone assembly and sequence design. Nat Commun 2018; 9:2780. [PMID: 30018322 PMCID: PMC6050298 DOI: 10.1038/s41467-018-05205-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Accepted: 06/13/2018] [Indexed: 12/05/2022] Open
Abstract
Automated design of enzymes with wild-type-like catalytic properties has been a long-standing but elusive goal. Here, we present a general, automated method for enzyme design through combinatorial backbone assembly. Starting from a set of homologous yet structurally diverse enzyme structures, the method assembles new backbone combinations and uses Rosetta to optimize the amino acid sequence, while conserving key catalytic residues. We apply this method to two unrelated enzyme families with TIM-barrel folds, glycoside hydrolase 10 (GH10) xylanases and phosphotriesterase-like lactonases (PLLs), designing 43 and 34 proteins, respectively. Twenty-one GH10 and seven PLL designs are active, including designs derived from templates with <25% sequence identity. Moreover, four designs are as active as natural enzymes in these families. Atomic accuracy in a high-activity GH10 design is further confirmed by crystallographic analysis. Thus, combinatorial-backbone assembly and design may be used to generate stable, active, and structurally diverse enzymes with altered selectivity or activity. Computationally designed enzymes often show lower activity or stability than their natural counterparts. Here, the authors present an evolution-inspired method for automated enzyme design, creating stable enzymes with accurate active site architectures and wild-type-like activities.
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12
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Setiawan D, Brender J, Zhang Y. Recent advances in automated protein design and its future challenges. Expert Opin Drug Discov 2018; 13:587-604. [PMID: 29695210 DOI: 10.1080/17460441.2018.1465922] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.
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Affiliation(s)
- Dani Setiawan
- a Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , MI , USA
| | - Jeffrey Brender
- b Radiation Biology Branch , Center for Cancer Research, National Cancer Institute - NIH , Bethesda , MD , USA
| | - Yang Zhang
- a Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , MI , USA.,c Department of Biological Chemistry , University of Michigan , Ann Arbor , MI , USA
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