1
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Pham C, Stogios PJ, Savchenko A, Mahadevan R. Computation-guided transcription factor biosensor specificity engineering for adipic acid detection. Comput Struct Biotechnol J 2024; 23:2211-2219. [PMID: 38817964 PMCID: PMC11137364 DOI: 10.1016/j.csbj.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 05/01/2024] [Accepted: 05/02/2024] [Indexed: 06/01/2024] Open
Abstract
Transcription factor (TF)-based biosensors that connect small-molecule sensing with readouts such as fluorescence have proven to be useful synthetic biology tools for applications in biotechnology. However, the development of specific TF-based biosensors is hindered by the limited repertoire of TFs specific for molecules of interest since current construction methods rely on a limited set of characterized TFs. In this study, we present an approach for engineering the specificity of TFs through a computation-based workflow using molecular docking that enables targeted alteration of TF ligand specificity. Using this method, we engineer the LysR family BenM TF to alter its specificity from its cognate ligand cis,cis-muconic acid to adipic acid through a single amino acid substitution identified by our computational workflow. When implemented in a cell-free system, the engineered biosensor shows higher ligand sensitivity, expanding the potential applications of this circuit. We further investigate ligand binding through molecular dynamics to analyze the substitution, elucidating the impact of modulating a single amino acid position on the mechanism of BenM ligand binding. This study represents the first application of biomolecular modeling methods for altering BenM specificity and for gaining insights into how mutations influence the structural dynamics of BenM. Such methods can potentially be applied to other TFs to alter specificity and analyze the dynamics responsible for these changes, highlighting the applicability of computational tools for informing experiments. In addition, our developed adipic acid biosensor can be applied for the identification and engineering of enzymes to produce adipic acid.
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Affiliation(s)
- Chester Pham
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario, Canada
| | - Peter J. Stogios
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario, Canada
| | - Alexei Savchenko
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario, Canada
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, Alberta, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario, Canada
- The Institute of Biomedical Engineering, University of Toronto, Ontario, Canada
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2
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Zhao M, Hu M, Han R, Ye C, Li X, Wang T, Liu Y, Xue Z, Liu K. Dynamics design of a non-natural transcription factor responding to androst-4-ene-3,17-dione. Synth Syst Biotechnol 2024; 9:436-444. [PMID: 38616975 PMCID: PMC11015099 DOI: 10.1016/j.synbio.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 03/03/2024] [Accepted: 04/02/2024] [Indexed: 04/16/2024] Open
Abstract
The production of androst-4-ene-3,17-dione (AD) by the steroidal microbial cell factory requires transcription factors (TFs) to participate in metabolic regulation. However, microbial cell factory lacks effective TFs that can respond to AD in its metabolic pathway. Additionally, finding and obtaining natural TFs that specifically respond to AD is a complex and onerous task. In this study, we devised an artificial TF that responds to AD, termed AdT, based on structure-guided molecular dynamics (MD) simulation. According to MD analysis of the conformational changes of AdT after binding to AD, an LBD in which the N- and C-termini exhibited convergence tendencies was used as a microswitch to guide the assembly of a DNA-binding domain lexA, a linker (GGGGS)2, and a transcription activation domain B42 into an artificial TF. As a proof of design, a AD biosensor was designed and constructed in yeast on the basis of the ligand-binding domain (LBD) of hormone receptor. In addition, the transcription factor activity of AdT was increased by 1.44-fold for its variant F320Y. Overall, we created non-natural TF elements for AD microbial cell factory, and expected that the design TF strategy will be applied to running in parallel to the signaling machinery of the host cell.
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Affiliation(s)
| | | | - Rumeng Han
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Chao Ye
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Xiangfei Li
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Tianwen Wang
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Yan Liu
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Zhenglian Xue
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Kun Liu
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
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3
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Leonard AC, Friedman AJ, Chayer R, Petersen BM, Woojuh J, Xing Z, Cutler SR, Kaar JL, Shirts MR, Whitehead TA. Rationalizing Diverse Binding Mechanisms to the Same Protein Fold: Insights for Ligand Recognition and Biosensor Design. ACS Chem Biol 2024; 19:1757-1772. [PMID: 39017707 DOI: 10.1021/acschembio.4c00243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
The engineering of novel protein-ligand binding interactions, particularly for complex drug-like molecules, is an unsolved problem, which could enable many practical applications of protein biosensors. In this work, we analyzed two engineered biosensors, derived from the plant hormone sensor PYR1, to recognize either the agrochemical mandipropamid or the synthetic cannabinoid WIN55,212-2. Using a combination of quantitative deep mutational scanning experiments and molecular dynamics simulations, we demonstrated that mutations at common positions can promote protein-ligand shape complementarity and revealed prominent differences in the electrostatic networks needed to complement diverse ligands. MD simulations indicate that both PYR1 protein-ligand complexes bind a single conformer of their target ligand that is close to the lowest free-energy conformer. Computational design using a fixed conformer and rigid body orientation led to new WIN55,212-2 sensors with nanomolar limits of detection. This work reveals mechanisms by which the versatile PYR1 biosensor scaffold can bind diverse ligands. This work also provides computational methods to sample realistic ligand conformers and rigid body alignments that simplify the computational design of biosensors for novel ligands of interest.
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Affiliation(s)
- Alison C Leonard
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
| | - Anika J Friedman
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
| | - Rachel Chayer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
| | - Brian M Petersen
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
| | - Janty Woojuh
- Department of Botany and Plant Sciences, University of California, Riverside, California 92521-9800, United States
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, California 92521, United States
- Center for Plant Cell Biology, University of California, Riverside, Riverside, California 92521, United States
| | - Zenan Xing
- Department of Botany and Plant Sciences, University of California, Riverside, California 92521-9800, United States
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, California 92521, United States
- Center for Plant Cell Biology, University of California, Riverside, Riverside, California 92521, United States
| | - Sean R Cutler
- Department of Botany and Plant Sciences, University of California, Riverside, California 92521-9800, United States
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, California 92521, United States
- Center for Plant Cell Biology, University of California, Riverside, Riverside, California 92521, United States
| | - Joel L Kaar
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
| | - Michael R Shirts
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
| | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
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4
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Zhou J, Huang M. Navigating the landscape of enzyme design: from molecular simulations to machine learning. Chem Soc Rev 2024; 53:8202-8239. [PMID: 38990263 DOI: 10.1039/d4cs00196f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.
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Affiliation(s)
- Jiahui Zhou
- School of Chemistry and Chemical Engineering, Queen's University, David Keir Building, Stranmillis Road, Belfast BT9 5AG, Northern Ireland, UK.
| | - Meilan Huang
- School of Chemistry and Chemical Engineering, Queen's University, David Keir Building, Stranmillis Road, Belfast BT9 5AG, Northern Ireland, UK.
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5
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Gao Y, Ang YS, Yung LYL. One-Pot Detection of Proteins Using a Two-Way Extension-Based Assay with Cas12a. ACS Sens 2024. [PMID: 39078660 DOI: 10.1021/acssensors.4c00370] [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: 07/31/2024]
Abstract
Protein biomarkers are an important class of biomarkers in disease diagnosis and are traditionally detected by enzyme-linked immunosorbent assay and mass spectrometry, which involve multiple steps and a complex workflow. In recent years, many CRISPR-Cas12a-based methods for protein detection have been developed; however, most of them have not overcome the workflow complications observed in traditional assays, limiting their applicability in point-of-care testing. In this work, we designed a single-step, one-pot, and proximity-based isothermal immunoassay integrating CRISPR Cas12a for homogeneous protein target detection with a simplified workflow and high sensitivity. Probes consisting of different binders (small molecule, aptamer, and antibody) conjugated with oligonucleotides undergo two-way extension upon binding to the protein targets, leading to downstream DNA amplification by a pair of nicking enzymes and polymerases to generate target sequences for Cas12a signal generation. We used the streptavidin-biotin model to demonstrate the design of our assay and proved that all three elements of protein detection (target protein binding, DNA amplification, and Cas12a signal generation) could coexist in one pot and proceed isothermally in a single buffer system at a low reaction volume of 10 μL. The plug-and-play applicability of our assay has been successfully demonstrated using four different protein targets, streptavidin, PDGF-BB, antidigoxigenin antibody, and IFNγ, with the limit of detection ranging from fM to pM.
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Affiliation(s)
- Yahui Gao
- Department of Chemical & Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
| | - Yan Shan Ang
- Department of Chemical & Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
| | - Lin-Yue Lanry Yung
- Department of Chemical & Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
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6
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Zhang Z, Shen W, Liu Q, Zitnik M. Efficient Generation of Protein Pockets with PocketGen. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.25.581968. [PMID: 38464121 PMCID: PMC10925136 DOI: 10.1101/2024.02.25.581968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Designing protein-binding proteins plays an important role in drug discovery. However, AI-based design of such proteins is challenging due to complex ligand-protein interactions, flexibility of ligand molecules and amino acid side chains, and sequence-structure dependencies. We introduce PocketGen, a deep generative model that produces both the residue sequence and atom structure of the protein regions where interactions with ligand molecules occur. PocketGen ensures sequence-structure consistency by using a graph transformer for structural encoding and a sequence refinement module based on a protein language model. The bilevel graph transformer captures interactions at multiple granularities across atom, residue, and ligand levels. To enhance sequence refinement, PocketGen integrates a structural adapter with the protein language model, ensuring consistency between structure-based and sequence-based predictions. Results show that PocketGen can generate high-fidelity protein pockets with superior binding affinity and structural validity. It is ten times faster than physics-based methods and achieves a 95% success rate, defined as the percentage of generated pockets with higher binding affinity than reference pockets, along with achieving an amino acid recovery rate exceeding 64%.
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Affiliation(s)
- Zaixi Zhang
- State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, China
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Wanxiang Shen
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Qi Liu
- State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, China
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Data Science Initiative, Cambridge, MA, USA
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7
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An L, Said M, Tran L, Majumder S, Goreshnik I, Lee GR, Juergens D, Dauparas J, Anishchenko I, Coventry B, Bera AK, Kang A, Levine PM, Alvarez V, Pillai A, Norn C, Feldman D, Zorine D, Hicks DR, Li X, Sanchez MG, Vafeados DK, Salveson PJ, Vorobieva AA, Baker D. Binding and sensing diverse small molecules using shape-complementary pseudocycles. Science 2024; 385:276-282. [PMID: 39024436 DOI: 10.1126/science.adn3780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 06/03/2024] [Indexed: 07/20/2024]
Abstract
We describe an approach for designing high-affinity small molecule-binding proteins poised for downstream sensing. We use deep learning-generated pseudocycles with repeating structural units surrounding central binding pockets with widely varying shapes that depend on the geometry and number of the repeat units. We dock small molecules of interest into the most shape complementary of these pseudocycles, design the interaction surfaces for high binding affinity, and experimentally screen to identify designs with the highest affinity. We obtain binders to four diverse molecules, including the polar and flexible methotrexate and thyroxine. Taking advantage of the modular repeat structure and central binding pockets, we construct chemically induced dimerization systems and low-noise nanopore sensors by splitting designs into domains that reassemble upon ligand addition.
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Affiliation(s)
- Linna An
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Meerit Said
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Long Tran
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Chemistry, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Sagardip Majumder
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Inna Goreshnik
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Gyu Rie Lee
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - David Juergens
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Molecular Engineering, University of Washington, Seattle, WA, USA
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Asim K Bera
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Paul M Levine
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Valentina Alvarez
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Arvind Pillai
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | | | - Dmitri Zorine
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Derrick R Hicks
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Xinting Li
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - Dionne K Vafeados
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Patrick J Salveson
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Anastassia A Vorobieva
- VIB-VUB Center for Structural Biology, Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Chemistry, University of Washington, Seattle, WA, USA
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8
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Lagunes L, Briggs K, Martin-Holder P, Xu Z, Maurer D, Ghabra K, Deeds EJ. Modeling reveals the strength of weak interactions in stacked-ring assembly. Biophys J 2024; 123:1763-1780. [PMID: 38762753 PMCID: PMC11267433 DOI: 10.1016/j.bpj.2024.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/30/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024] Open
Abstract
Cells employ many large macromolecular machines for the execution and regulation of processes that are vital for cell and organismal viability. Interestingly, cells cannot synthesize these machines as functioning units. Instead, cells synthesize the molecular parts that must then assemble into the functional complex. Many important machines, including chaperones such as GroEL and proteases such as the proteasome, comprise protein rings that are stacked on top of one another. While there is some experimental data regarding how stacked-ring complexes such as the proteasome self-assemble, a comprehensive understanding of the dynamics of stacked-ring assembly is currently lacking. Here, we developed a mathematical model of stacked-trimer assembly and performed an analysis of the assembly of the stacked homomeric trimer, which is the simplest stacked-ring architecture. We found that stacked rings are particularly susceptible to a form of kinetic trapping that we term "deadlock," in which the system gets stuck in a state where there are many large intermediates that are not the fully assembled structure but that cannot productively react. When interaction affinities are uniformly strong, deadlock severely limits assembly yield. We thus predicted that stacked rings would avoid situations where all interfaces in the structure have high affinity. Analysis of available crystal structures indicated that indeed the majority-if not all-of stacked trimers do not contain uniformly strong interactions. Finally, to better understand the origins of deadlock, we developed a formal pathway analysis and showed that, when all the binding affinities are strong, many of the possible pathways are utilized. In contrast, optimal assembly strategies utilize only a small number of pathways. Our work suggests that deadlock is a critical factor influencing the evolution of macromolecular machines and provides general principles for understanding the self-assembly efficiency of existing machines.
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Affiliation(s)
- Leonila Lagunes
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, California; Institute for Quantitative and Computational Biosciences, UCLA, Los Angeles, California
| | - Koan Briggs
- Department of Physics, University of Kansas, Lawrence, Kansas
| | - Paige Martin-Holder
- Department of Molecular Immunology, Microbiology and Genetics, UCLA, Los Angeles, California
| | - Zaikun Xu
- Center for Computational Biology, University of Kansas, Lawrence, Kansas
| | - Dustin Maurer
- Center for Computational Biology, University of Kansas, Lawrence, Kansas
| | - Karim Ghabra
- Computational and Systems Biology IDP, UCLA, Los Angeles, California
| | - Eric J Deeds
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, California; Institute for Quantitative and Computational Biosciences, UCLA, Los Angeles, California; Center for Computational Biology, University of Kansas, Lawrence, Kansas.
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9
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Goverde CA, Pacesa M, Goldbach N, Dornfeld LJ, Balbi PEM, Georgeon S, Rosset S, Kapoor S, Choudhury J, Dauparas J, Schellhaas C, Kozlov S, Baker D, Ovchinnikov S, Vecchio AJ, Correia BE. Computational design of soluble and functional membrane protein analogues. Nature 2024; 631:449-458. [PMID: 38898281 PMCID: PMC11236705 DOI: 10.1038/s41586-024-07601-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 05/23/2024] [Indexed: 06/21/2024]
Abstract
De novo design of complex protein folds using solely computational means remains a substantial challenge1. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from G-protein-coupled receptors2, are not found in the soluble proteome, and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses demonstrate the high thermal stability of the designs, and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, as a proof of concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we have designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.
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Affiliation(s)
- Casper A Goverde
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Martin Pacesa
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Nicolas Goldbach
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Lars J Dornfeld
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Petra E M Balbi
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Sandrine Georgeon
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Stéphane Rosset
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Srajan Kapoor
- Department of Structural Biology, University at Buffalo, Buffalo, NY, USA
| | - Jagrity Choudhury
- Department of Structural Biology, University at Buffalo, Buffalo, NY, USA
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Christian Schellhaas
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Simon Kozlov
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Sergey Ovchinnikov
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alex J Vecchio
- Department of Structural Biology, University at Buffalo, Buffalo, NY, USA
| | - Bruno E Correia
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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10
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Wang J, Watson JL, Lisanza SL. Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models. Cold Spring Harb Perspect Biol 2024; 16:a041472. [PMID: 38438190 PMCID: PMC11216169 DOI: 10.1101/cshperspect.a041472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design as well. We review recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion. These methods have led to major improvements over previous methods in wet-lab success rates for designing protein binders, metalloproteins, enzymes, and oligomeric assemblies. These results show that structure-prediction models are a powerful foundation for developing protein-design tools and suggest that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design.
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Affiliation(s)
- Jue Wang
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, Washington 98195, USA
- DeepMind, London EC4A 3BF, United Kingdom
| | - Joseph L Watson
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
| | - Sidney L Lisanza
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, Washington 98195, USA
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11
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Chaves EJF, Coêlho DF, Cruz CHB, Moreira EG, Simões JCM, Nascimento-Filho MJ, Lins RD. Structure-based computational design of antibody mimetics: challenges and perspectives. FEBS Open Bio 2024. [PMID: 38925955 DOI: 10.1002/2211-5463.13855] [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: 03/03/2024] [Revised: 05/17/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024] Open
Abstract
The design of antibody mimetics holds great promise for revolutionizing therapeutic interventions by offering alternatives to conventional antibody therapies. Structure-based computational approaches have emerged as indispensable tools in the rational design of those molecules, enabling the precise manipulation of their structural and functional properties. This review covers the main classes of designed antigen-binding motifs, as well as alternative strategies to develop tailored ones. We discuss the intricacies of different computational protein-protein interaction design strategies, showcased by selected successful cases in the literature. Subsequently, we explore the latest advancements in the computational techniques including the integration of machine and deep learning methodologies into the design framework, which has led to an augmented design pipeline. Finally, we verse onto the current challenges that stand in the way between high-throughput computer design of antibody mimetics and experimental realization, offering a forward-looking perspective into the field and the promises it holds to biotechnology.
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Affiliation(s)
- Elton J F Chaves
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
| | - Danilo F Coêlho
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
| | - Carlos H B Cruz
- Institute of Structural and Molecular Biology, University College London, UK
| | | | - Júlio C M Simões
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
| | - Manassés J Nascimento-Filho
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
| | - Roberto D Lins
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
- Fiocruz Genomics Network, Brazil
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12
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Joshi SHN, Jenkins C, Ulaeto D, Gorochowski TE. Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0037. [PMID: 38919711 PMCID: PMC11197468 DOI: 10.34133/bdr.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/23/2024] [Indexed: 06/27/2024] Open
Abstract
Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.
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Affiliation(s)
| | - Christopher Jenkins
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - David Ulaeto
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - Thomas E. Gorochowski
- School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, UK
- BrisEngBio,
School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
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13
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Jankowski W, Surov SS, Hernandez NE, Rawal A, Battistel M, Freedberg D, Ovanesov MV, Sauna ZE. Engineering and evaluation of FXa bypassing agents that restore hemostasis following Apixaban associated bleeding. Nat Commun 2024; 15:3912. [PMID: 38724509 PMCID: PMC11082157 DOI: 10.1038/s41467-024-48278-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 04/26/2024] [Indexed: 05/12/2024] Open
Abstract
Direct oral anticoagulants (DOACs) targeting activated factor Xa (FXa) are used to prevent or treat thromboembolic disorders. DOACs reversibly bind to FXa and inhibit its enzymatic activity. However, DOAC treatment carries the risk of anticoagulant-associated bleeding. Currently, only one specific agent, andexanet alfa, is approved to reverse the anticoagulant effects of FXa-targeting DOACs (FXaDOACs) and control life-threatening bleeding. However, because of its mechanism of action, andexanet alfa requires a cumbersome dosing schedule, and its use is associated with the risk of thrombosis. Here, we present the computational design, engineering, and evaluation of FXa-variants that exhibit anticoagulation reversal activity in the presence of FXaDOACs. Our designs demonstrate low DOAC binding affinity, retain FXa-enzymatic activity and reduce the DOAC-associated bleeding by restoring hemostasis in mice treated with apixaban. Importantly, the FXaDOACs reversal agents we designed, unlike andexanet alfa, do not inhibit TFPI, and consequently, may have a safer thrombogenic profile.
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Affiliation(s)
- Wojciech Jankowski
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation & Research, US FDA, Silver Spring, MD, USA
| | - Stepan S Surov
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation & Research, US FDA, Silver Spring, MD, USA
| | - Nancy E Hernandez
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation & Research, US FDA, Silver Spring, MD, USA
| | - Atul Rawal
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation & Research, US FDA, Silver Spring, MD, USA
| | - Marcos Battistel
- Laboratory of Bacterial Polysaccharides, Division of Bacterial, Parasitic and Allergenic Products, Office of Vaccines Research and Review, Center for Biologics Evaluation & Research, US FDA, Silver Spring, MD, USA
| | - Daron Freedberg
- Laboratory of Bacterial Polysaccharides, Division of Bacterial, Parasitic and Allergenic Products, Office of Vaccines Research and Review, Center for Biologics Evaluation & Research, US FDA, Silver Spring, MD, USA
| | - Mikhail V Ovanesov
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation & Research, US FDA, Silver Spring, MD, USA
| | - Zuben E Sauna
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation & Research, US FDA, Silver Spring, MD, USA.
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14
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Krishna R, Wang J, Ahern W, Sturmfels P, Venkatesh P, Kalvet I, Lee GR, Morey-Burrows FS, Anishchenko I, Humphreys IR, McHugh R, Vafeados D, Li X, Sutherland GA, Hitchcock A, Hunter CN, Kang A, Brackenbrough E, Bera AK, Baek M, DiMaio F, Baker D. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 2024; 384:eadl2528. [PMID: 38452047 DOI: 10.1126/science.adl2528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/27/2024] [Indexed: 03/09/2024]
Abstract
Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.
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Affiliation(s)
- Rohith Krishna
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Jue Wang
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Woody Ahern
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA
| | - Pascal Sturmfels
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA
| | - Preetham Venkatesh
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
| | - Indrek Kalvet
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
| | - Gyu Rie Lee
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
| | | | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Ian R Humphreys
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Ryan McHugh
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
| | - Dionne Vafeados
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Xinting Li
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | | | - Andrew Hitchcock
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - C Neil Hunter
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Alex Kang
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Evans Brackenbrough
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Asim K Bera
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Minkyung Baek
- School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
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15
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Rakotoharisoa RV, Seifinoferest B, Zarifi N, Miller JDM, Rodriguez JM, Thompson MC, Chica RA. Design of Efficient Artificial Enzymes Using Crystallographically Enhanced Conformational Sampling. J Am Chem Soc 2024; 146:10001-10013. [PMID: 38532610 DOI: 10.1021/jacs.4c00677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing the catalytic efficiency of de novo enzymes by several orders of magnitude without relying on directed evolution and high-throughput screening. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (kcat/KM 125 M-1 s-1) and KE70 (kcat/KM 57 M-1 s-1), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM values improved by 100-250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models, with catalytic contacts present as designed and transition-state root-mean-square deviations of ≤0.65 Å. Our work shows how ensemble-based design can generate efficient artificial enzymes by exploiting the true conformational ensemble to design improved active sites.
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Affiliation(s)
- Rojo V Rakotoharisoa
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Behnoush Seifinoferest
- Department of Chemistry and Biochemistry, University of California Merced, Merced, California 95343, United States
| | - Niayesh Zarifi
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Jack D M Miller
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Joshua M Rodriguez
- Department of Chemistry and Biochemistry, University of California Merced, Merced, California 95343, United States
| | - Michael C Thompson
- Department of Chemistry and Biochemistry, University of California Merced, Merced, California 95343, United States
| | - Roberto A Chica
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
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16
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Lu L, Gou X, Tan SK, Mann SI, Yang H, Zhong X, Gazgalis D, Valdiviezo J, Jo H, Wu Y, Diolaiti ME, Ashworth A, Polizzi NF, DeGrado WF. De novo design of drug-binding proteins with predictable binding energy and specificity. Science 2024; 384:106-112. [PMID: 38574125 PMCID: PMC11290694 DOI: 10.1126/science.adl5364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/28/2024] [Indexed: 04/06/2024]
Abstract
The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule-binding proteins with tuned interaction energies is feasible entirely from computation.
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Affiliation(s)
- Lei Lu
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Xuxu Gou
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Sophia K Tan
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Samuel I Mann
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
- Department of Chemistry, University of California, Riverside, CA 92521, USA
| | - Hyunjun Yang
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Xiaofang Zhong
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
| | - Dimitrios Gazgalis
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - Jesús Valdiviezo
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - Hyunil Jo
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Yibing Wu
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Morgan E Diolaiti
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Alan Ashworth
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Nicholas F Polizzi
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - William F DeGrado
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
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17
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Zheng T, Zhang C. Engineering strategies and challenges of endolysin as an antibacterial agent against Gram-negative bacteria. Microb Biotechnol 2024; 17:e14465. [PMID: 38593316 PMCID: PMC11003714 DOI: 10.1111/1751-7915.14465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/09/2024] [Accepted: 03/21/2024] [Indexed: 04/11/2024] Open
Abstract
Bacteriophage endolysin is a novel antibacterial agent that has attracted much attention in the prevention and control of drug-resistant bacteria due to its unique mechanism of hydrolysing peptidoglycans. Although endolysin exhibits excellent bactericidal effects on Gram-positive bacteria, the presence of the outer membrane of Gram-negative bacteria makes it difficult to lyse them extracellularly, thus limiting their application field. To enhance the extracellular activity of endolysin and facilitate its crossing through the outer membrane of Gram-negative bacteria, researchers have adopted physical, chemical, and molecular methods. This review summarizes the characterization of endolysin targeting Gram-negative bacteria, strategies for endolysin modification, and the challenges and future of engineering endolysin against Gram-negative bacteria in clinical applications, to promote the application of endolysin in the prevention and control of Gram-negative bacteria.
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Affiliation(s)
- Tianyu Zheng
- Bathurst Future Agri‐Tech InstituteQingdao Agricultural UniversityQingdaoChina
| | - Can Zhang
- College of Veterinary MedicineQingdao Agricultural UniversityQingdaoChina
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18
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Leonard AC, Friedman AJ, Chayer R, Petersen BM, Kaar J, Shirts MR, Whitehead TA. Rationalizing diverse binding mechanisms to the same protein fold: insights for ligand recognition and biosensor design. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.25.586677. [PMID: 38586024 PMCID: PMC10996623 DOI: 10.1101/2024.03.25.586677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
The engineering of novel protein-ligand binding interactions, particularly for complex drug-like molecules, is an unsolved problem which could enable many practical applications of protein biosensors. In this work, we analyzed two engineer ed biosensors, derived from the plant hormone sensor PYR1, to recognize either the agrochemical mandipropamid or the synthetic cannabinoid WIN55,212-2. Using a combination of quantitative deep mutational scanning experiments and molecular dynamics simulations, we demonstrated that mutations at common positions can promote protein-ligand shape complementarity and revealed prominent differences in the electrostatic networks needed to complement diverse ligands. MD simulations indicate that both PYR1 protein-ligand complexes bind a single conformer of their target ligand that is close to the lowest free energy conformer. Computational design using a fixed conformer and rigid body orientation led to new WIN55,212-2 sensors with nanomolar limits of detection. This work reveals mechanisms by which the versatile PYR1 biosensor scaffold can bind diverse ligands. This work also provides computational methods to sample realistic ligand conformers and rigid body alignments that simplify the computational design of biosensors for novel ligands of interest.
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19
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Goverde CA, Pacesa M, Goldbach N, Dornfeld LJ, Balbi PEM, Georgeon S, Rosset S, Kapoor S, Choudhury J, Dauparas J, Schellhaas C, Kozlov S, Baker D, Ovchinnikov S, Vecchio AJ, Correia BE. Computational design of soluble functional analogues of integral membrane proteins. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.09.540044. [PMID: 38496615 PMCID: PMC10942269 DOI: 10.1101/2023.05.09.540044] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
De novo design of complex protein folds using solely computational means remains a significant challenge. Here, we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from GPCRs, are not found in the soluble proteome and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses reveal high thermal stability of the designs and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, standing as a proof-of-concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.
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20
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Aslam S, Zulfiqar F, Hameed W, Qureshi S, Zaroon, Bashir H. Fusion proteins development strategies and their role as cancer therapeutic agents. Biotechnol Appl Biochem 2024; 71:81-95. [PMID: 37822167 DOI: 10.1002/bab.2523] [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: 02/28/2023] [Accepted: 10/01/2023] [Indexed: 10/13/2023]
Abstract
Cancer continues to be leading cause of morbidity and mortality despite decades of research and advancement in chemotherapy. Most tumors can be reduced via standard oncology treatments, such as chemotherapy, radiotherapy, and surgical resection, and they frequently recur. Significant progress has been made since targeted cancer therapy inception in creation of medications that exhibit improved tumor-selective action. Particularly in preclinical and clinical investigations, fusion proteins have shown strong activity and improved treatment outcomes for a number of human cancers. Synergistically combining many proteins into one complex allows the creation of synthetic fusion proteins with enhanced characteristics or new capabilities. Signal transduction pathways are important for onset, development, and spread of cancer. As result, signaling molecules are desirable targets for cancer therapies, and significant effort has been made into developing fusion proteins that would act as inhibitors of these pathways. A wide range of biotechnological and medicinal applications are made possible by fusion of protein domains that improves bioactivities or creates new functional combinations. Such proteins may function as immune effectors cell recruiters to tumors or as decoy receptors for various ligands. In this review article, we have outlined the standard methods for creating fusion proteins and covered the applications of fusion proteins in treatment of cancer. This article also highlights the role of fusion proteins in targeting the signaling pathways involved in cancer for effective treatment.
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Affiliation(s)
- Shakira Aslam
- Center for Applied Molecular Biology, University of the Punjab, Lahore, Pakistan
| | | | - Warda Hameed
- King Edward Medical University, Lahore, Pakistan
| | - Shahnila Qureshi
- Center for Applied Molecular Biology, University of the Punjab, Lahore, Pakistan
| | - Zaroon
- Center for Applied Molecular Biology, University of the Punjab, Lahore, Pakistan
| | - Hamid Bashir
- Center for Applied Molecular Biology, University of the Punjab, Lahore, Pakistan
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21
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Kortemme T. De novo protein design-From new structures to programmable functions. Cell 2024; 187:526-544. [PMID: 38306980 PMCID: PMC10990048 DOI: 10.1016/j.cell.2023.12.028] [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: 10/27/2023] [Revised: 12/03/2023] [Accepted: 12/19/2023] [Indexed: 02/04/2024]
Abstract
Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will discuss the state of the field of de novo protein design at the juncture of physics-based modeling approaches and AI. New protein folds and higher-order assemblies can be designed with considerable experimental success rates, and difficult problems requiring tunable control over protein conformations and precise shape complementarity for molecular recognition are coming into reach. Emerging approaches incorporate engineering principles-tunability, controllability, and modularity-into the design process from the beginning. Exciting frontiers lie in deconstructing cellular functions with de novo proteins and, conversely, constructing synthetic cellular signaling from the ground up. As methods improve, many more challenges are unsolved.
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Affiliation(s)
- Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
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22
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Teng F, Cui T, Zhou L, Gao Q, Zhou Q, Li W. Programmable synthetic receptors: the next-generation of cell and gene therapies. Signal Transduct Target Ther 2024; 9:7. [PMID: 38167329 PMCID: PMC10761793 DOI: 10.1038/s41392-023-01680-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/22/2023] [Accepted: 10/11/2023] [Indexed: 01/05/2024] Open
Abstract
Cell and gene therapies hold tremendous promise for treating a range of difficult-to-treat diseases. However, concerns over the safety and efficacy require to be further addressed in order to realize their full potential. Synthetic receptors, a synthetic biology tool that can precisely control the function of therapeutic cells and genetic modules, have been rapidly developed and applied as a powerful solution. Delicately designed and engineered, they can be applied to finetune the therapeutic activities, i.e., to regulate production of dosed, bioactive payloads by sensing and processing user-defined signals or biomarkers. This review provides an overview of diverse synthetic receptor systems being used to reprogram therapeutic cells and their wide applications in biomedical research. With a special focus on four synthetic receptor systems at the forefront, including chimeric antigen receptors (CARs) and synthetic Notch (synNotch) receptors, we address the generalized strategies to design, construct and improve synthetic receptors. Meanwhile, we also highlight the expanding landscape of therapeutic applications of the synthetic receptor systems as well as current challenges in their clinical translation.
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Affiliation(s)
- Fei Teng
- University of Chinese Academy of Sciences, Beijing, 101408, China.
| | - Tongtong Cui
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
| | - Li Zhou
- University of Chinese Academy of Sciences, Beijing, 101408, China
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
| | - Qingqin Gao
- University of Chinese Academy of Sciences, Beijing, 101408, China
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
| | - Qi Zhou
- University of Chinese Academy of Sciences, Beijing, 101408, China.
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Wei Li
- University of Chinese Academy of Sciences, Beijing, 101408, China.
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
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23
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Lu L, Gou X, Tan SK, Mann SI, Yang H, Zhong X, Gazgalis D, Valdiviezo J, Jo H, Wu Y, Diolaiti ME, Ashworth A, Polizzi NF, DeGrado WF. De novo design of drug-binding proteins with predictable binding energy and specificity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.23.573178. [PMID: 38187746 PMCID: PMC10769398 DOI: 10.1101/2023.12.23.573178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The de novo design of small-molecule-binding proteins has seen exciting recent progress; however, the ability to achieve exquisite affinity for binding small molecules while tuning specificity has not yet been demonstrated directly from computation. Here, we develop a computational procedure that results in the highest affinity binders to date with predetermined relative affinities, targeting a series of PARP1 inhibitors. Two of four designed proteins bound with affinities ranging from < 5 nM to low μM, in a predictable manner. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free-energy calculations performed directly on the designed models are in excellent agreement with the experimentally measured affinities, suggesting that the de novo design of small-molecule-binding proteins with tuned interaction energies is now feasible entirely from computation. We expect these methods to open many opportunities in biomedicine, including rapid sensor development, antidote design, and drug delivery vehicles.
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Affiliation(s)
- Lei Lu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Xuxu Gou
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Sophia K Tan
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Samuel I. Mann
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Hyunjun Yang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | | | - Dimitrios Gazgalis
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Jesús Valdiviezo
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Hyunil Jo
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yibing Wu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Morgan E. Diolaiti
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Alan Ashworth
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | | | - William F. DeGrado
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
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24
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An L, Said M, Tran L, Majumder S, Goreshnik I, Lee GR, Juergens D, Dauparas J, Anishchenko I, Coventry B, Bera AK, Kang A, Levine PM, Alvarez V, Pillai A, Norn C, Feldman D, Zorine D, Hicks DR, Li X, Sanchez MG, Vafeados DK, Salveson PJ, Vorobieva AA, Baker D. De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.20.572602. [PMID: 38187589 PMCID: PMC10769206 DOI: 10.1101/2023.12.20.572602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
A general method for designing proteins to bind and sense any small molecule of interest would be widely useful. Due to the small number of atoms to interact with, binding to small molecules with high affinity requires highly shape complementary pockets, and transducing binding events into signals is challenging. Here we describe an integrated deep learning and energy based approach for designing high shape complementarity binders to small molecules that are poised for downstream sensing applications. We employ deep learning generated psuedocycles with repeating structural units surrounding central pockets; depending on the geometry of the structural unit and repeat number, these pockets span wide ranges of sizes and shapes. For a small molecule target of interest, we extensively sample high shape complementarity pseudocycles to generate large numbers of customized potential binding pockets; the ligand binding poses and the interacting interfaces are then optimized for high affinity binding. We computationally design binders to four diverse molecules, including for the first time polar flexible molecules such as methotrexate and thyroxine, which are expressed at high levels and have nanomolar affinities straight out of the computer. Co-crystal structures are nearly identical to the design models. Taking advantage of the modular repeating structure of pseudocycles and central location of the binding pockets, we constructed low noise nanopore sensors and chemically induced dimerization systems by splitting the binders into domains which assemble into the original pseudocycle pocket upon target molecule addition.
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Affiliation(s)
- Linna An
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Meerit Said
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Long Tran
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Sagardip Majumder
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Inna Goreshnik
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Gyu Rie Lee
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - David Juergens
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justas Dauparas
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ivan Anishchenko
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Brian Coventry
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Asim K. Bera
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alex Kang
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Paul M. Levine
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Valentina Alvarez
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Arvind Pillai
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - David Feldman
- BioInnovation Institute, DK2200 Copenhagen N, Denmark
| | - Dmitri Zorine
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Derrick R. Hicks
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Xinting Li
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - Dionne K. Vafeados
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Patrick J. Salveson
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - David Baker
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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25
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Xi C, Diao J, Moon TS. Advances in ligand-specific biosensing for structurally similar molecules. Cell Syst 2023; 14:1024-1043. [PMID: 38128482 PMCID: PMC10751988 DOI: 10.1016/j.cels.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 08/23/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.
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Affiliation(s)
- Chenggang Xi
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Jinjin Diao
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Tae Seok Moon
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO, USA.
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26
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Khakzad H, Igashov I, Schneuing A, Goverde C, Bronstein M, Correia B. A new age in protein design empowered by deep learning. Cell Syst 2023; 14:925-939. [PMID: 37972559 DOI: 10.1016/j.cels.2023.10.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/22/2023] [Accepted: 10/11/2023] [Indexed: 11/19/2023]
Abstract
The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins.
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Affiliation(s)
- Hamed Khakzad
- Université de Lorraine, CNRS, Inria, LORIA, 54000 Nancy, France; École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Ilia Igashov
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Arne Schneuing
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Casper Goverde
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | | | - Bruno Correia
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
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27
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Lee GR, Pellock SJ, Norn C, Tischer D, Dauparas J, Anischenko I, Mercer JAM, Kang A, Bera A, Nguyen H, Goreshnik I, Vafeados D, Roullier N, Han HL, Coventry B, Haddox HK, Liu DR, Yeh AHW, Baker D. Small-molecule binding and sensing with a designed protein family. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.565201. [PMID: 37961294 PMCID: PMC10635051 DOI: 10.1101/2023.11.01.565201] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Despite transformative advances in protein design with deep learning, the design of small-molecule-binding proteins and sensors for arbitrary ligands remains a grand challenge. Here we combine deep learning and physics-based methods to generate a family of proteins with diverse and designable pocket geometries, which we employ to computationally design binders for six chemically and structurally distinct small-molecule targets. Biophysical characterization of the designed binders revealed nanomolar to low micromolar binding affinities and atomic-level design accuracy. The bound ligands are exposed at one edge of the binding pocket, enabling the de novo design of chemically induced dimerization (CID) systems; we take advantage of this to create a biosensor with nanomolar sensitivity for cortisol. Our approach provides a general method to design proteins that bind and sense small molecules for a wide range of analytical, environmental, and biomedical applications.
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28
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Rakotoharisoa RV, Seifinoferest B, Zarifi N, Miller JD, Rodriguez JM, Thompson MC, Chica RA. Design of efficient artificial enzymes using crystallographically-enhanced conformational sampling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.564846. [PMID: 37961474 PMCID: PMC10635043 DOI: 10.1101/2023.11.01.564846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing catalytic efficiency of de novo enzymes to a level comparable to their natural counterparts without relying on directed evolution. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (kcat/KM 125 M-1 s-1) and KE70 (kcat/KM 57 M-1 s-1), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM values improved by 100-250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models. Our work shows how computational design can generate efficient artificial enzymes by exploiting the true conformational ensemble to more effectively stabilize the transition state.
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Affiliation(s)
- Rojo V. Rakotoharisoa
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5
| | - Behnoush Seifinoferest
- Department of Chemistry and Biochemistry, University of California Merced, Merced, California 95343, United States
| | - Niayesh Zarifi
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5
| | - Jack D.M. Miller
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5
| | - Joshua M. Rodriguez
- Department of Chemistry and Biochemistry, University of California Merced, Merced, California 95343, United States
| | - Michael C. Thompson
- Department of Chemistry and Biochemistry, University of California Merced, Merced, California 95343, United States
| | - Roberto A. Chica
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5
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29
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Kosugi T, Iida T, Tanabe M, Iino R, Koga N. Design of allosteric sites into rotary motor V 1-ATPase by restoring lost function of pseudo-active sites. Nat Chem 2023; 15:1591-1598. [PMID: 37414880 PMCID: PMC10624635 DOI: 10.1038/s41557-023-01256-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 05/26/2023] [Indexed: 07/08/2023]
Abstract
Allostery produces concerted functions of protein complexes by orchestrating the cooperative work between the constituent subunits. Here we describe an approach to create artificial allosteric sites in protein complexes. Certain protein complexes contain subunits with pseudo-active sites, which are believed to have lost functions during evolution. Our hypothesis is that allosteric sites in such protein complexes can be created by restoring the lost functions of pseudo-active sites. We used computational design to restore the lost ATP-binding ability of the pseudo-active site in the B subunit of a rotary molecular motor, V1-ATPase. Single-molecule experiments with X-ray crystallography analyses revealed that binding of ATP to the designed allosteric site boosts this V1's activity compared with the wild-type, and the rotation rate can be tuned by modulating ATP's binding affinity. Pseudo-active sites are widespread in nature, and our approach shows promise as a means of programming allosteric control over concerted functions of protein complexes.
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Affiliation(s)
- Takahiro Kosugi
- Research Center of Integrative Molecular Systems (CIMoS), Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Department of Structural Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan.
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Japan.
| | - Tatsuya Iida
- Department of Life and Coordination-Complex Molecular Science, Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan
- Department of Functional Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan
| | - Mikio Tanabe
- Structural Biology Research Center, Institute of Materials Structure Science, High Energy Accelerator Research Organization (KEK), Tsukuba, Japan
| | - Ryota Iino
- Department of Life and Coordination-Complex Molecular Science, Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan
- Department of Functional Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan
| | - Nobuyasu Koga
- Research Center of Integrative Molecular Systems (CIMoS), Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Department of Structural Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan.
- Institute for Protein Research (IPR), Osaka University, Suita, Japan.
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30
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Kriegel M, Muller YA. De novo prediction of explicit water molecule positions by a novel algorithm within the protein design software MUMBO. Sci Rep 2023; 13:16680. [PMID: 37794104 PMCID: PMC10550942 DOI: 10.1038/s41598-023-43659-w] [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: 08/01/2023] [Accepted: 09/26/2023] [Indexed: 10/06/2023] Open
Abstract
By mediating interatomic interactions, water molecules play a major role in protein-protein, protein-DNA and protein-ligand interfaces, significantly affecting affinity and specificity. This notwithstanding, explicit water molecules are usually not considered in protein design software because of high computational costs. To challenge this situation, we analyzed the binding characteristics of 60,000 waters from high resolution crystal structures and used the observed parameters to implement the prediction of water molecules in the protein design and side chain-packing software MUMBO. To reduce the complexity of the problem, we incorporated water molecules through the solvation of rotamer pairs instead of relying on solvated rotamer libraries. Our validation demonstrates the potential of our algorithm by achieving recovery rates of 67% for bridging water molecules and up to 86% for fully coordinated waters. The efficacy of our algorithm is highlighted further by the prediction of 3 different proteinligand complexes. Here, 91% of water-mediated interactions between protein and ligand are correctly predicted. These results suggest that the new algorithm could prove highly beneficial for structure-based protein design, particularly for the optimization of ligand-binding pockets or protein-protein interfaces.
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Affiliation(s)
- Mark Kriegel
- Division of Biotechnology, Department of Biology, 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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31
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Li S, Li Z, Tan GY, Xin Z, Wang W. In vitro allosteric transcription factor-based biosensing. Trends Biotechnol 2023; 41:1080-1095. [PMID: 36967257 DOI: 10.1016/j.tibtech.2023.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/15/2023] [Accepted: 03/01/2023] [Indexed: 06/18/2023]
Abstract
A biosensor is an analytical device that converts a biological response into a measurable output signal. Bacterial allosteric transcription factors (aTFs) have been utilized as a novel class of recognition elements for in vitro biosensing, which circumvents the limitations of aTF-based whole-cell biosensors (WCBs) and helps to meet the increasing requirement of small-molecule biosensors for diverse applications. In this review, we summarize the recent advances related to the configuration of aTF-based biosensors in vitro. Particularly, we evaluate the advantages of aTFs for in vitro biosensing and highlight their great potential for the establishment of robust and easy-to-implement biosensing strategies. We argue that key technical innovations and generalizable workflows will enhance the pipeline for facile construction of diverse aTF-based small-molecule biosensors.
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Affiliation(s)
- Shanshan Li
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Zilong Li
- State Key Laboratory of Microbial Resources, Institute of Microbiology, CAS, Beijing 100101, PR China
| | - Gao-Yi Tan
- State Key Laboratory of Bioreactor Engineering and School of Biotechnology, East China University of Science and Technology, Shanghai 200237, PR China
| | - Zhenguo Xin
- State Key Laboratory of Microbial Resources, Institute of Microbiology, CAS, Beijing 100101, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Weishan Wang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, CAS, Beijing 100101, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China.
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32
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Min H, Sun T, Cui W, Han Z, Yao P, Cheng P, Shi W. Cage-Based Metal-Organic Framework as an Artificial Energy Receptor for Highly Sensitive Detection of Serotonin. Inorg Chem 2023. [PMID: 37224141 DOI: 10.1021/acs.inorgchem.3c01025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Artificial synthetic receptors toward functional biomolecules can serve as models to provide insights into understanding the high binding affinity of biological receptors to biomolecules for revealing their law of life activities. The exploration of serotonin receptors, which can guide drug design or count as diagnostic reagents for patients with carcinoid tumors, is of great value for clinical medicine but is highly challenging due to complex biological analysis. Herein, we report a cage-based metal-organic framework (NKU-67-Eu) as an artificial chemical receptor with well-matched energy levels for serotonin. The energy transfer back from the analyte to the framework enables NKU-67-Eu to recognize serotonin with excellent neurotransmitter selectivity in human plasma and an ultra-low limit of detection of 36 nM. Point-of-care visual detection is further realized by the colorimetry change of NKU-67-Eu toward serotonin with a smartphone camera.
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Affiliation(s)
- Hui Min
- Department of Chemistry, Key Laboratory of Advanced Energy Materials Chemistry (MOE), and Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
| | - Tiankai Sun
- Department of Chemistry, Key Laboratory of Advanced Energy Materials Chemistry (MOE), and Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
| | - Wenyue Cui
- Department of Chemistry, Key Laboratory of Advanced Energy Materials Chemistry (MOE), and Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
| | - Zongsu Han
- Department of Chemistry, Key Laboratory of Advanced Energy Materials Chemistry (MOE), and Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
| | - Peiyu Yao
- Department of Emergency, Tianjin Union Medical Center, Tianjin 300121, China
| | - Peng Cheng
- Department of Chemistry, Key Laboratory of Advanced Energy Materials Chemistry (MOE), and Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Key Laboratory of Renewable Energy Conversion and Storage Center (RECAST), College of Chemistry, Nankai University, Tianjin 300071, China
| | - Wei Shi
- Department of Chemistry, Key Laboratory of Advanced Energy Materials Chemistry (MOE), and Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
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33
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Kalita P, Tripathi T, Padhi AK. Computational Protein Design for COVID-19 Research and Emerging Therapeutics. ACS CENTRAL SCIENCE 2023; 9:602-613. [PMID: 37122454 PMCID: PMC10042144 DOI: 10.1021/acscentsci.2c01513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Indexed: 05/03/2023]
Abstract
As the world struggles with the ongoing COVID-19 pandemic, unprecedented obstacles have continuously been traversed as new SARS-CoV-2 variants continually emerge. Infectious disease outbreaks are unavoidable, but the knowledge gained from the successes and failures will help create a robust health management system to deal with such pandemics. Previously, scientists required years to develop diagnostics, therapeutics, or vaccines; however, we have seen that, with the rapid deployment of high-throughput technologies and unprecedented scientific collaboration worldwide, breakthrough discoveries can be accelerated and insights broadened. Computational protein design (CPD) is a game-changing new technology that has provided alternative therapeutic strategies for pandemic management. In addition to the development of peptide-based inhibitors, miniprotein binders, decoys, biosensors, nanobodies, and monoclonal antibodies, CPD has also been used to redesign native SARS-CoV-2 proteins and human ACE2 receptors. We discuss how novel CPD strategies have been exploited to develop rationally designed and robust COVID-19 treatment strategies.
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Affiliation(s)
- Parismita Kalita
- Molecular
and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong 793022, India
| | - Timir Tripathi
- Molecular
and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong 793022, India
- Regional
Director’s Office, Indira Gandhi
National Open University, Regional Centre Kohima, Kenuozou, Kohima 797001, India
| | - Aditya K. Padhi
- Laboratory
for Computational Biology & Biomolecular Design, School of Biochemical
Engineering, Indian Institute of Technology
(BHU), Varanasi 221005, Uttar Pradesh, India
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34
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Love AC, Caldwell DR, Kolbaba-Kartchner B, Townsend KM, Halbers LP, Yao Z, Brennan CK, Ivanic J, Hadjian T, Mills JH, Schnermann MJ, Prescher JA. Red-Shifted Coumarin Luciferins for Improved Bioluminescence Imaging. J Am Chem Soc 2023; 145:3335-3345. [PMID: 36745536 PMCID: PMC10519142 DOI: 10.1021/jacs.2c07220] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Multicomponent bioluminescence imaging in vivo requires an expanded collection of tissue-penetrant probes. Toward this end, we generated a new class of near-infrared (NIR) emitting coumarin luciferin analogues (CouLuc-3s). The scaffolds were easily accessed from commercially available dyes. Complementary mutant luciferases for the CouLuc-3 analogues were also identified. The brightest probes enabled sensitive imaging in vivo. The CouLuc-3 scaffolds are also orthogonal to popular bioluminescent reporters and can be used for multicomponent imaging applications. Collectively, this work showcases a new set of bioluminescent tools that can be readily implemented for multiplexed imaging in a variety of biological settings.
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Affiliation(s)
- Anna C Love
- Department of Chemistry, University of California, Irvine, Irvine, California 92697, United States
| | - Donald R Caldwell
- Chemical Biology Laboratory, Cancer for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland 21702, United States
| | - Bethany Kolbaba-Kartchner
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, United States
- The Biodesign Center for Molecular Design and Biomimetics, Arizona State University, Tempe, Arizona 85281, United States
| | - Katherine M Townsend
- Department of Chemistry, University of California, Irvine, Irvine, California 92697, United States
| | - Lila P Halbers
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, United States
| | - Zi Yao
- Department of Chemistry, University of California, Irvine, Irvine, California 92697, United States
| | - Caroline K Brennan
- Department of Chemistry, University of California, Irvine, Irvine, California 92697, United States
| | - Joseph Ivanic
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland 21702, United States
| | - Tanya Hadjian
- Department of Chemistry, University of California, Irvine, Irvine, California 92697, United States
| | - Jeremy H Mills
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, United States
- The Biodesign Center for Molecular Design and Biomimetics, Arizona State University, Tempe, Arizona 85281, United States
| | - Martin J Schnermann
- Chemical Biology Laboratory, Cancer for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland 21702, United States
| | - Jennifer A Prescher
- Department of Chemistry, University of California, Irvine, Irvine, California 92697, United States
- Department of Molecular Biology & Biochemistry, University of California, Irvine, Irvine, California 92697, United States
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, United States
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35
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Xu Q, Wang F, Jiao W, Zhang M, Xing G, Feng H, Sun X, Hu M, Zhang G. Virtual Screening-Based Peptides Targeting Spike Protein to Inhibit Porcine Epidemic Diarrhea Virus (PEDV) Infection. Viruses 2023; 15:v15020381. [PMID: 36851595 PMCID: PMC9965349 DOI: 10.3390/v15020381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/14/2023] [Accepted: 01/21/2023] [Indexed: 01/31/2023] Open
Abstract
Due to the rapid mutation of porcine epidemic diarrhea virus (PEDV), existing vaccines cannot provide sufficient immune protection for pigs. Therefore, it is urgent to design the affinity peptides for the prevention and control of this disease. In this study, we made use of a molecular docking technology for virtual screening of affinity peptides that specifically recognized the PEDV S1 C-terminal domain (CTD) protein for the first time. Experimentally, the affinity, cross-reactivity and sensitivity of the peptides were identified by an enzyme-linked immunosorbent assay (ELISA) and a surface plasmon resonance (SPR) test, separately. Subsequently, Cell Counting Kit-8 (CCK-8), quantitative real-time PCR (qRT-PCR), Western blot and indirect immunofluorescence were used to further study the antiviral effect of different concentrations of peptide 110766 in PEDV. Our results showed that the P/N value of peptide 110766 at 450 nm reached 167, with a KD value of 216 nM. The cytotoxic test indicated that peptide 110766 was not toxic to vero cells. Results of the absolute quantitative PCR revealed that different concentrations (3.125 μM, 6.25 μM, 12.5 μM, 25 μM, 50 μM, 100 μM, 200 μM) of peptide 110766 could significantly reduce the viral load of PEDV compared with the virus group (p < 0.0001). Similarly, results of Western blot and indirect immunofluorescence also suggested that the antiviral effect of peptide 110766 at 3.125 is still significant. Based on the above research, high-affinity peptide 110766 binding to the PEDV S1-CTD protein was attained by a molecular docking technology. Therefore, designing, screening, and identifying affinity peptides can provide a new method for the development of antiviral drugs for PEDV.
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Affiliation(s)
- Qian Xu
- Department of Preventive Veterinary Medicine, College of Veterinary Medicine, Northwest A&F University, Yang ling, Xianyang 712100, China
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, 116# Huayuan Road, Zhengzhou 450002, China
| | - Fangyu Wang
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, 116# Huayuan Road, Zhengzhou 450002, China
| | - Wenqiang Jiao
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, 116# Huayuan Road, Zhengzhou 450002, China
| | - Mengting Zhang
- Department of Preventive Veterinary Medicine, College of Veterinary Medicine, Northwest A&F University, Yang ling, Xianyang 712100, China
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, 116# Huayuan Road, Zhengzhou 450002, China
| | - Guangxu Xing
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, 116# Huayuan Road, Zhengzhou 450002, China
| | - Hua Feng
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, 116# Huayuan Road, Zhengzhou 450002, China
| | - Xuefeng Sun
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, 116# Huayuan Road, Zhengzhou 450002, China
| | - Man Hu
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, 116# Huayuan Road, Zhengzhou 450002, China
| | - Gaiping Zhang
- Department of Preventive Veterinary Medicine, College of Veterinary Medicine, Northwest A&F University, Yang ling, Xianyang 712100, China
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, 116# Huayuan Road, Zhengzhou 450002, China
- Longhu Modern Immunology Laboratory, Zhengzhou 450046, China
- School of Advanced Agricultural Sciences, Peking University, Beijing 100871, China
- Jiangsu Co-Innovation Center for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou 225009, China
- Correspondence:
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36
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De novo protein fold design through sequence-independent fragment assembly simulations. Proc Natl Acad Sci U S A 2023; 120:e2208275120. [PMID: 36656852 PMCID: PMC9942881 DOI: 10.1073/pnas.2208275120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.
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37
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Chemically inducible split protein regulators for mammalian cells. Nat Chem Biol 2023; 19:64-71. [PMID: 36163385 DOI: 10.1038/s41589-022-01136-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 08/08/2022] [Indexed: 12/31/2022]
Abstract
Chemically inducible systems represent valuable synthetic biology tools that enable the external control of biological processes. However, their translation to therapeutic applications has been limited because of unfavorable ligand characteristics or the immunogenicity of xenogeneic protein domains. To address these issues, we present a strategy for engineering inducible split protein regulators (INSPIRE) in which ligand-binding proteins of human origin are split into two fragments that reassemble in the presence of a cognate physiological ligand or clinically approved drug. We show that the INSPIRE platform can be used for dynamic, orthogonal and multiplex control of gene expression in mammalian cells. Furthermore, we demonstrate the functionality of a glucocorticoid-responsive INSPIRE platform in vivo and apply it for perturbing an endogenous regulatory network. INSPIRE presents a generalizable approach toward designing small-molecule responsive systems that can be implemented for the construction of new sensors, regulatory networks and therapeutic applications.
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38
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Beltrán J, Steiner PJ, Bedewitz M, Wei S, Peterson FC, Li Z, Hughes BE, Hartley Z, Robertson NR, Medina-Cucurella AV, Baumer ZT, Leonard AC, Park SY, Volkman BF, Nusinow DA, Zhong W, Wheeldon I, Cutler SR, Whitehead TA. Rapid biosensor development using plant hormone receptors as reprogrammable scaffolds. Nat Biotechnol 2022; 40:1855-1861. [PMID: 35726092 PMCID: PMC9750858 DOI: 10.1038/s41587-022-01364-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 05/17/2022] [Indexed: 01/14/2023]
Abstract
A general method to generate biosensors for user-defined molecules could provide detection tools for a wide range of biological applications. Here, we describe an approach for the rapid engineering of biosensors using PYR1 (Pyrabactin Resistance 1), a plant abscisic acid (ABA) receptor with a malleable ligand-binding pocket and a requirement for ligand-induced heterodimerization, which facilitates the construction of sense-response functions. We applied this platform to evolve 21 sensors with nanomolar to micromolar sensitivities for a range of small molecules, including structurally diverse natural and synthetic cannabinoids and several organophosphates. X-ray crystallography analysis revealed the mechanistic basis for new ligand recognition by an evolved cannabinoid receptor. We demonstrate that PYR1-derived receptors are readily ported to various ligand-responsive outputs, including enzyme-linked immunosorbent assay (ELISA)-like assays, luminescence by protein-fragment complementation and transcriptional circuits, all with picomolar to nanomolar sensitivity. PYR1 provides a scaffold for rapidly evolving new biosensors for diverse sense-response applications.
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Affiliation(s)
- Jesús Beltrán
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA
| | - Paul J Steiner
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Matthew Bedewitz
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Shuang Wei
- Department of Biochemistry, University of California, Riverside, Riverside, CA, USA
| | - Francis C Peterson
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Zongbo Li
- Department of Chemistry, University of California, Riverside, Riverside, CA, USA
| | - Brigid E Hughes
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Zachary Hartley
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA
| | - Nicholas R Robertson
- Department of Bioengineering, University of California, Riverside, Riverside, USA
| | | | - Zachary T Baumer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Alison C Leonard
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Sang-Youl Park
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
| | - Brian F Volkman
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Wenwan Zhong
- Department of Chemistry, University of California, Riverside, Riverside, CA, USA
| | - Ian Wheeldon
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA.
- Department of Chemical and Environmental Engineering, University of California, Riverside, Riverside, CA, USA.
| | - Sean R Cutler
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA.
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA.
- Center for Plant Cell Biology, University of California, Riverside, Riverside, CA, USA.
| | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA.
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Liu Y, Yuan G, Hassan MM, Abraham PE, Mitchell JC, Jacobson D, Tuskan GA, Khakhar A, Medford J, Zhao C, Liu CJ, Eckert CA, Doktycz MJ, Tschaplinski TJ, Yang X. Biological and Molecular Components for Genetically Engineering Biosensors in Plants. BIODESIGN RESEARCH 2022; 2022:9863496. [PMID: 37850147 PMCID: PMC10521658 DOI: 10.34133/2022/9863496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 10/08/2022] [Indexed: 10/19/2023] Open
Abstract
Plants adapt to their changing environments by sensing and responding to physical, biological, and chemical stimuli. Due to their sessile lifestyles, plants experience a vast array of external stimuli and selectively perceive and respond to specific signals. By repurposing the logic circuitry and biological and molecular components used by plants in nature, genetically encoded plant-based biosensors (GEPBs) have been developed by directing signal recognition mechanisms into carefully assembled outcomes that are easily detected. GEPBs allow for in vivo monitoring of biological processes in plants to facilitate basic studies of plant growth and development. GEPBs are also useful for environmental monitoring, plant abiotic and biotic stress management, and accelerating design-build-test-learn cycles of plant bioengineering. With the advent of synthetic biology, biological and molecular components derived from alternate natural organisms (e.g., microbes) and/or de novo parts have been used to build GEPBs. In this review, we summarize the framework for engineering different types of GEPBs. We then highlight representative validated biological components for building plant-based biosensors, along with various applications of plant-based biosensors in basic and applied plant science research. Finally, we discuss challenges and strategies for the identification and design of biological components for plant-based biosensors.
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Affiliation(s)
- Yang Liu
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Guoliang Yuan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Md Mahmudul Hassan
- Department of Genetics and Plant Breeding, Patuakhali Science and Technology University, Dumki, Patuakhali, 8602, Bangladesh
| | - Paul E. Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Julie C. Mitchell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Gerald A. Tuskan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Arjun Khakhar
- Department of Biology, Colorado State University, Fort Collins, Colorado 80523, USA
| | - June Medford
- Department of Biology, Colorado State University, Fort Collins, Colorado 80523, USA
| | - Cheng Zhao
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Chang-Jun Liu
- Biology Department, Brookhaven National Laboratory, Upton, New York 11973, USA
| | - Carrie A. Eckert
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Mitchel J. Doktycz
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Timothy J. Tschaplinski
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Xiaohan Yang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
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40
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Lee B, Wang T. A Modular Scaffold for Controlling Transcriptional Activation via Homomeric Protein-Protein Interactions. ACS Synth Biol 2022; 11:3198-3206. [PMID: 36215660 DOI: 10.1021/acssynbio.2c00501] [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: 01/24/2023]
Abstract
Protein-protein interactions (PPIs) have been extensively utilized in synthetic biology to construct artificial gene networks. However, synthetic regulation of gene expression by PPIs in E. coli has largely relied upon repressors, and existing PPI-controlled transcriptional activators have generally been employed with heterodimeric interactions. Here we report a highly modular, PPI-dependent transcriptional activator, cCadC, that was designed to be compatible with homomeric interactions. We describe the process of engineering cCadC from a transmembrane protein into a soluble cytosolic regulator. We then show that gene transcription by cCadC can be controlled by homomeric PPIs and furthermore discriminates between dimeric and higher-order interactions. Finally, we demonstrate that cCadC activity can be placed under small molecule regulation using chemically induced dimerization or ligand dependent stabilization. This work should greatly expand the scope of PPIs that can be employed in artificial gene circuits in E. coli and complements the existing repertoire of tools for transcriptional regulation in synthetic biology.
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Affiliation(s)
- ByungUk Lee
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Tina Wang
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
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41
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Qing R, Hao S, Smorodina E, Jin D, Zalevsky A, Zhang S. Protein Design: From the Aspect of Water Solubility and Stability. Chem Rev 2022; 122:14085-14179. [PMID: 35921495 PMCID: PMC9523718 DOI: 10.1021/acs.chemrev.1c00757] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Indexed: 12/13/2022]
Abstract
Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.
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Affiliation(s)
- Rui Qing
- State
Key Laboratory of Microbial Metabolism, School of Life Sciences and
Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- The
David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Shilei Hao
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- Key
Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China
| | - Eva Smorodina
- Department
of Immunology, University of Oslo and Oslo
University Hospital, Oslo 0424, Norway
| | - David Jin
- Avalon GloboCare
Corp., Freehold, New Jersey 07728, United States
| | - Arthur Zalevsky
- Laboratory
of Bioinformatics Approaches in Combinatorial Chemistry and Biology, Shemyakin−Ovchinnikov Institute of Bioorganic
Chemistry RAS, Moscow 117997, Russia
| | - Shuguang Zhang
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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42
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Soleymani F, Paquet E, Viktor H, Michalowski W, Spinello D. Protein-protein interaction prediction with deep learning: A comprehensive review. Comput Struct Biotechnol J 2022; 20:5316-5341. [PMID: 36212542 PMCID: PMC9520216 DOI: 10.1016/j.csbj.2022.08.070] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 11/15/2022] Open
Abstract
Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.
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Affiliation(s)
- Farzan Soleymani
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON, Canada
| | - Eric Paquet
- National Research Council, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada
| | - Herna Viktor
- School of Electrical Engineering and Computer Science, University of Ottawa, ON, Canada
| | | | - Davide Spinello
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON, Canada
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43
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Wang F, Hu M, Li N, Sun X, Xing G, Zheng G, Jin Q, Liu Y, Cui C, Zhang G. Precise Assembly of Multiple Antigens on Nanoparticles with Specially Designed Affinity Peptides. ACS APPLIED MATERIALS & INTERFACES 2022; 14:39843-39857. [PMID: 35998372 DOI: 10.1021/acsami.2c10684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Antigen proteins, assembled on nanoparticles, can be recognized by antigen-presenting cells effectively to enhance antigen immunogenicity. The ability to simultaneously display multiantigens on the same nanoparticle could have numerous applications but remained technical challenges. Here, we described a method for precise assembly of multiple antigens on nanoparticles with specially designed affinity peptides. First, we designed and screened affinity peptides with high affinity and specificity, which could respectively target the key amino acid residues of classical swine fever virus (CSFV) E2 protein or porcine circovirus type 2 capsid protein (PCV2 Cap) accurately. Then, we conjugated the antigen proteins to poly(lactic acid-glycolic acid) copolymer (PLGA) and Gram-positive enhancer matrix (GEM) nanoparticles through the peptides and perfectly assembled two kinds of multiantigen display nanoparticles with different particle sizes. Subsequently, the immunological properties of the assembled nanoparticles were tested. The results showed that the antigen display nanoparticles could promote the maturation, phagocytosis, and proinflammatory effects of antigen-presenting cells (APCs). Besides, compared with the antigen proteins, multiantigen display nanoparticles could induce much higher levels of antibodies and neutralizing antibodies in mice. This strategy may provide a technical support for the study of protein structure and the research and development of polyvalent vaccines.
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Affiliation(s)
- Fangyu Wang
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, Henan 450000, China
| | - Man Hu
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, Henan 450000, China
| | - Ning Li
- College of Food Science and Technology, Henan Agricultural University, Zhengzhou, Henan 450000, China
| | - Xuefeng Sun
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, Henan 450000, China
| | - Guangxu Xing
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, Henan 450000, China
| | - Guanmin Zheng
- Public Health and Preventive Medicine Teaching and Research Center, Henan University of Chinese Medicine, Zhengzhou, Henan 450000, China
| | - Qianyue Jin
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, Henan 450000, China
| | - Yunchao Liu
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, Henan 450000, China
| | - Chenxu Cui
- College of Food Science and Technology, Henan Agricultural University, Zhengzhou, Henan 450000, China
| | - Gaiping Zhang
- Key Laboratory for Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, Henan 450000, China
- School of Advanced Agricultural Sciences, Peking University, Beijing 100871, China
- Jiangsu Co-Innovation Center for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, Jiangsu 225009, China
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44
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Liu YJ. Understanding the complete bioluminescence cycle from a multiscale computational perspective: A review. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY C: PHOTOCHEMISTRY REVIEWS 2022. [DOI: 10.1016/j.jphotochemrev.2022.100537] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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45
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Andon JS, Lee B, Wang T. Enzyme directed evolution using genetically encodable biosensors. Org Biomol Chem 2022; 20:5891-5906. [PMID: 35437559 DOI: 10.1039/d2ob00443g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Directed evolution has been remarkably successful in identifying enzyme variants with new or improved properties, such as altered substrate scope or novel reactivity. Genetically encodable biosensors (GEBs), which convert the concentration of a small molecule ligand into an easily detectable output signal, have seen increasing application to enzyme directed evolution in the last decade. GEBs enable the use of high-throughput methods to assess enzyme activity of very large libraries, which can accelerate the search for variants with desirable activity. Here, we review different classes of GEBs and their properties in the context of enzyme evolution, how GEBs have been integrated into directed evolution workflows, and recent examples of enzyme evolution efforts utilizing GEBs. Finally, we discuss the advantages, challenges, and opportunities for using GEBs in the directed evolution of enzymes.
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Affiliation(s)
- James S Andon
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA.
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - ByungUk Lee
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA.
| | - Tina Wang
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA.
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Mandal R, Pham P, Hilty C. Screening of Protein-Ligand Binding Using a SABRE Hyperpolarized Reporter. Anal Chem 2022; 94:11375-11381. [PMID: 35921650 DOI: 10.1021/acs.analchem.2c02250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Hyperpolarization through signal amplification by reversible exchange (SABRE) provides a facile means to enhance nuclear magnetic resonance (NMR) signals of small molecules containing an N-heterocycle or other binding site for a polarization transfer catalyst. A purpose-designed reporter ligand, which is capable of binding both to a target protein and to the catalyst, makes the sensitivity enhancement by this technique compatible with the measurement of a range of biomolecular interactions. The 1H polarization of the reporter ligand 4-amidinopyridine, which is targeting trypsin, is used to screen ligands that are not themselves hyperpolarizable by SABRE. The respective protein-ligand dissociation constants (KD) are determined by an observed change in the R2 relaxation rate of the reporter. A calculation of expected signal changes indicates that the accessible ligand KD values extend over several orders of magnitude, while the concentrations of target proteins and ligands can be reduced considering the sensitivity gains from hyperpolarization. In general, the design of a single, weakly binding ligand for a target protein enables the use of SABRE hyperpolarization for ligand screening or other biophysical studies involving macromolecular interactions.
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Affiliation(s)
- Ratnamala Mandal
- Department of Chemistry, Texas A&M University, 3255 TAMU, College Station, Texas 77843, United States
| | - Pierce Pham
- Department of Chemistry, Texas A&M University, 3255 TAMU, College Station, Texas 77843, United States
| | - Christian Hilty
- Department of Chemistry, Texas A&M University, 3255 TAMU, College Station, Texas 77843, United States
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Peluso P, Chankvetadze B. Recognition in the Domain of Molecular Chirality: From Noncovalent Interactions to Separation of Enantiomers. Chem Rev 2022; 122:13235-13400. [PMID: 35917234 DOI: 10.1021/acs.chemrev.1c00846] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
It is not a coincidence that both chirality and noncovalent interactions are ubiquitous in nature and synthetic molecular systems. Noncovalent interactivity between chiral molecules underlies enantioselective recognition as a fundamental phenomenon regulating life and human activities. Thus, noncovalent interactions represent the narrative thread of a fascinating story which goes across several disciplines of medical, chemical, physical, biological, and other natural sciences. This review has been conceived with the awareness that a modern attitude toward molecular chirality and its consequences needs to be founded on multidisciplinary approaches to disclose the molecular basis of essential enantioselective phenomena in the domain of chemical, physical, and life sciences. With the primary aim of discussing this topic in an integrated way, a comprehensive pool of rational and systematic multidisciplinary information is provided, which concerns the fundamentals of chirality, a description of noncovalent interactions, and their implications in enantioselective processes occurring in different contexts. A specific focus is devoted to enantioselection in chromatography and electromigration techniques because of their unique feature as "multistep" processes. A second motivation for writing this review is to make a clear statement about the state of the art, the tools we have at our disposal, and what is still missing to fully understand the mechanisms underlying enantioselective recognition.
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Affiliation(s)
- Paola Peluso
- Istituto di Chimica Biomolecolare ICB, CNR, Sede secondaria di Sassari, Traversa La Crucca 3, Regione Baldinca, Li Punti, I-07100 Sassari, Italy
| | - Bezhan Chankvetadze
- Institute of Physical and Analytical Chemistry, School of Exact and Natural Sciences, Tbilisi State University, Chavchavadze Avenue 3, 0179 Tbilisi, Georgia
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Xu H, Palpant T, Weinberger C, Shaw DE. Characterizing Receptor Flexibility to Predict Mutations That Lead to Human Adaptation of Influenza Hemagglutinin. J Chem Theory Comput 2022; 18:4995-5005. [PMID: 35815857 PMCID: PMC9367001 DOI: 10.1021/acs.jctc.1c01044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
![]()
A key step in the
emergence of human pandemic influenza strains
has been a switch in binding preference of the viral glycoprotein
hemagglutinin (HA) from avian to human sialic acid (SA) receptors.
The conformation of the bound SA varies substantially with HA sequence,
and crystallographic evidence suggests that the bound SA is flexible,
making it difficult to predict which mutations are responsible for
changing HA-binding preference. We performed molecular dynamics (MD)
simulations of SA analogues binding to various HAs and observed a
dynamic equilibrium among structurally diverse receptor conformations,
including conformations that have not been experimentally observed.
Using one such novel conformation, we predicted—and experimentally
confirmed—a set of mutations that substantially increased an
HA’s affinity for a human SA analogue. This prediction could
not have been inferred from the existing crystal structures, suggesting
that MD-generated HA–SA conformational ensembles could help
researchers predict human-adaptive mutations, aiding surveillance
of emerging pandemic threats.
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Affiliation(s)
- Huafeng Xu
- D. E. Shaw Research, New York, New York 10036, United States
| | - Timothy Palpant
- D. E. Shaw Research, New York, New York 10036, United States
| | - Cody Weinberger
- D. E. Shaw Research, New York, New York 10036, United States
| | - David E Shaw
- D. E. Shaw Research, New York, New York 10036, United States.,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032, United States
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49
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De novo design of a transcription factor for a progesterone biosensor. Biosens Bioelectron 2022; 203:113897. [DOI: 10.1016/j.bios.2021.113897] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 12/12/2021] [Accepted: 12/15/2021] [Indexed: 12/12/2022]
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Kretschmer S, Kortemme T. Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2022; 110:659-674. [PMID: 36531560 PMCID: PMC9754107 DOI: 10.1109/jproc.2022.3157898] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Synthetic biology approaches living systems with an engineering perspective and promises to deliver solutions to global challenges in healthcare and sustainability. A critical component is the design of biomolecular circuits with programmable input-output behaviors. Such circuits typically rely on a sensor module that recognizes molecular inputs, which is coupled to a functional output via protein-level circuits or regulating the expression of a target gene. While gene expression outputs can be customized relatively easily by exchanging the target genes, sensing new inputs is a major limitation. There is a limited repertoire of sensors found in nature, and there are often difficulties with interfacing them with engineered circuits. Computational protein design could be a key enabling technology to address these challenges, as it allows for the engineering of modular and tunable sensors that can be tailored to the circuit's application. In this article, we review recent computational approaches to design protein-based sensors for small-molecule inputs with particular focus on those based on the widely used Rosetta software suite. Furthermore, we review mechanisms that have been harnessed to couple ligand inputs to functional outputs. Based on recent literature, we illustrate how the combination of protein design and synthetic biology enables new sensors for diverse applications ranging from biomedicine to metabolic engineering. We conclude with a perspective on how strategies to address frontiers in protein design and cellular circuit design may enable the next generation of sense-response networks, which may increasingly be assembled from de novo components to display diverse and engineerable input-output behaviors.
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Affiliation(s)
- Simon Kretschmer
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA 94158 USA, and affiliated with the California Quantitative Biosciences Institute (QBI) at UCSF, San Francisco, CA 94158 USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA 94158 USA, and affiliated with the California Quantitative Biosciences Institute (QBI) at UCSF, San Francisco, CA 94158 USA
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