1
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Albanese KI, Petrenas R, Pirro F, Naudin EA, Borucu U, Dawson WM, Scott DA, Leggett GJ, Weiner OD, Oliver TAA, Woolfson DN. Rationally seeded computational protein design of ɑ-helical barrels. Nat Chem Biol 2024:10.1038/s41589-024-01642-0. [PMID: 38902458 DOI: 10.1038/s41589-024-01642-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 05/09/2024] [Indexed: 06/22/2024]
Abstract
Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.
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Affiliation(s)
- Katherine I Albanese
- School of Chemistry, University of Bristol, Bristol, UK
- Max Planck-Bristol Centre for Minimal Biology, University of Bristol, Bristol, UK
| | | | - Fabio Pirro
- School of Chemistry, University of Bristol, Bristol, UK
| | | | - Ufuk Borucu
- School of Biochemistry, University of Bristol, Medical Sciences Building, Bristol, UK
| | | | - D Arne Scott
- Rosa Biotech, Science Creates St Philips, Bristol, UK
| | | | - Orion D Weiner
- Cardiovascular Research Institute, Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
| | | | - Derek N Woolfson
- School of Chemistry, University of Bristol, Bristol, UK.
- Max Planck-Bristol Centre for Minimal Biology, University of Bristol, Bristol, UK.
- School of Biochemistry, University of Bristol, Medical Sciences Building, Bristol, UK.
- Bristol BioDesign Institute, University of Bristol, Bristol, UK.
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2
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Xu Y, Hu X, Wang C, Liu Y, Chen Q, Liu H. De novo design of cavity-containing proteins with a backbone-centered neural network energy function. Structure 2024; 32:424-432.e4. [PMID: 38325370 DOI: 10.1016/j.str.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 10/04/2023] [Accepted: 01/11/2024] [Indexed: 02/09/2024]
Abstract
The design of small-molecule-binding proteins requires protein backbones that contain cavities. Previous design efforts were based on naturally occurring cavity-containing backbone architectures. Here, we designed diverse cavity-containing backbones without predefined architectures by introducing tailored restraints into the backbone sampling driven by SCUBA (Side Chain-Unknown Backbone Arrangement), a neural network statistical energy function. For 521 out of 5816 designs, the root-mean-square deviations (RMSDs) of the Cα atoms for the AlphaFold2-predicted structures and our designed structures are within 2.0 Å. We experimentally tested 10 designed proteins and determined the crystal structures of two of them. One closely agrees with the designed model, while the other forms a domain-swapped dimer, where the partial structures are in agreement with the designed structures. Our results indicate that data-driven methods such as SCUBA hold great potential for designing de novo proteins with tailored small-molecule-binding function.
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Affiliation(s)
- Yang Xu
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Centre for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China; MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Xiuhong Hu
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Centre for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China; MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Chenchen Wang
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Yongrui Liu
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Quan Chen
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Centre for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China; MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China; Biomedical Sciences and Health Laboratory of Anhui Province, University of Science and Technology of China, Hefei, Anhui 230027, China.
| | - Haiyan Liu
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China; Biomedical Sciences and Health Laboratory of Anhui Province, University of Science and Technology of China, Hefei, Anhui 230027, China; School of Data Science, University of Science and Technology of China, Hefei, Anhui 230027, China.
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3
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Listov D, Goverde CA, Correia BE, Fleishman SJ. Opportunities and challenges in design and optimization of protein function. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00718-y. [PMID: 38565617 DOI: 10.1038/s41580-024-00718-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.
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Affiliation(s)
- Dina Listov
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Casper A Goverde
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Sarel Jacob Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel.
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4
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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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5
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Ye S, Zhong K, Huang Y, Zhang G, Sun C, Jiang J. Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics. J Am Chem Soc 2024; 146:2663-2672. [PMID: 38240637 DOI: 10.1021/jacs.3c12258] [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: 02/01/2024]
Abstract
The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciphering protein structures from experimental amide II spectra relies on time-consuming quantum chemical calculations on tens of thousands of representative configurations in solvent water. Currently, the accurate simulation of amide II spectra for whole proteins remains a challenge. Here, we present a machine learning (ML)-based protocol designed to efficiently simulate the amide II IR spectra of various proteins with an accuracy comparable to experimental results. This protocol stands out as a cost-effective and efficient alternative for studying protein dynamics, including the identification of secondary structures and monitoring the dynamics of protein hydrogen bonding under different pH conditions and during protein folding process. Our method provides a valuable tool in the field of protein research, focusing on the study of dynamic properties of proteins, especially those related to hydrogen bonding, using amide II IR spectroscopy.
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Affiliation(s)
- Sheng Ye
- School of Artificial Intelligence, Anhui University, Hefei, Anhui 230601, People's Republic of China
| | - Kai Zhong
- Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, Groningen 9747AG, Netherlands
| | - Yan Huang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Guozhen Zhang
- Hefei National Research Center of Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
| | - Changyin Sun
- School of Artificial Intelligence, Anhui University, Hefei, Anhui 230601, People's Republic of China
| | - Jun Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
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6
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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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7
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Tsukidate T, Hespen CW, Hang HC. Small molecule modulators of immune pattern recognition receptors. RSC Chem Biol 2023; 4:1014-1036. [PMID: 38033733 PMCID: PMC10685800 DOI: 10.1039/d3cb00096f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/03/2023] [Indexed: 12/02/2023] Open
Abstract
Pattern recognition receptors (PRRs) represent a re-emerging class of therapeutic targets for vaccine adjuvants, inflammatory diseases and cancer. In this review article, we summarize exciting developments in discovery and characterization of small molecule PRR modulators, focusing on Toll-like receptors (TLRs), NOD-like receptors (NLRs) and the cGAS-STING pathway. We also highlight PRRs that are currently lacking small molecule modulators and opportunities for chemical biology and therapeutic discovery.
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Affiliation(s)
- Taku Tsukidate
- Laboratory of Chemical Biology and Microbial Pathogenesis, The Rockefeller University, New York New York 10065 USA
| | - Charles W Hespen
- Laboratory of Chemical Biology and Microbial Pathogenesis, The Rockefeller University, New York New York 10065 USA
| | - Howard C Hang
- Laboratory of Chemical Biology and Microbial Pathogenesis, The Rockefeller University, New York New York 10065 USA
- Department of Immunology and Microbiology and Department of Chemistry, Scripps Research, La Jolla California 92037 USA
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8
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David TI, Pestov NB, Korneenko TV, Barlev NA. Non-Immunoglobulin Synthetic Binding Proteins for Oncology. BIOCHEMISTRY. BIOKHIMIIA 2023; 88:1232-1247. [PMID: 37770391 DOI: 10.1134/s0006297923090043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/01/2023] [Accepted: 08/01/2023] [Indexed: 09/30/2023]
Abstract
Extensive application of technologies like phage display in screening peptide and protein combinatorial libraries has not only facilitated creation of new recombinant antibodies but has also significantly enriched repertoire of the protein binders that have polypeptide scaffolds without homology to immunoglobulins. These innovative synthetic binding protein (SBP) platforms have grown in number and now encompass monobodies/adnectins, DARPins, lipocalins/anticalins, and a variety of miniproteins such as affibodies and knottins, among others. They serve as versatile modules for developing complex affinity tools that hold promise in both diagnostic and therapeutic settings. An optimal scaffold typically has low molecular weight, minimal immunogenicity, and demonstrates resistance against various challenging conditions, including proteolysis - making it potentially suitable for peroral administration. Retaining functionality under reducing intracellular milieu is also advantageous. However, paramount to its functionality is the scaffold's ability to tolerate mutations across numerous positions, allowing for the formation of a sufficiently large target binding region. This is achieved through the library construction, screening, and subsequent expression in an appropriate system. Scaffolds that exhibit high thermodynamic stability are especially coveted by the developers of new SBPs. These are steadily making their way into clinical settings, notably as antagonists of oncoproteins in signaling pathways. This review surveys the diverse landscape of SBPs, placing particular emphasis on the inhibitors targeting the oncoprotein KRAS, and highlights groundbreaking opportunities for SBPs in oncology.
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Affiliation(s)
- Temitope I David
- Institute of Biomedical Chemistry, Moscow, 119121, Russia
- Laboratory of Molecular Oncology, Phystech School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
| | - Nikolay B Pestov
- Institute of Biomedical Chemistry, Moscow, 119121, Russia.
- Laboratory of Tick-Borne Encephalitis and Other Viral Encephalitides, Chumakov Federal Scientific Center for Research and Development of Immune-and-Biological Products, Russian Academy of Sciences, Moscow, 108819, Russia
- Group of Cross-Linking Enzymes, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | - Tatyana V Korneenko
- Group of Cross-Linking Enzymes, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | - Nikolai A Barlev
- Institute of Biomedical Chemistry, Moscow, 119121, Russia
- Laboratory of Tick-Borne Encephalitis and Other Viral Encephalitides, Chumakov Federal Scientific Center for Research and Development of Immune-and-Biological Products, Russian Academy of Sciences, Moscow, 108819, Russia
- Institute of Cytology Russian Academy of Sciences, St.-Petersburg, 194064, Russia
- School of Medicine, Nazarbayev University, Astana, 010000, Kazakhstan
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9
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Cai L, Chen G, Sun L, Miao S, Shang L, Zhao Y, Sun L. Rocket-Inspired Effervescent Motors for Oral Macromolecule Delivery. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2210679. [PMID: 37120721 DOI: 10.1002/adma.202210679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 04/10/2023] [Indexed: 06/19/2023]
Abstract
Oral administration is among the most convenient ways with good patient compliance for drug delivery; while it remains a challenge to achieve desirable bioavailability of most macromolecules due to the complex gastrointestinal barriers. Here, inspired by the structure and function of rocket, a novel micromotor delivery system is presented with scaled-down rocket-like architecture and effervescent-tablets-derived fuel for efficient oral macromolecule delivery by penetrating intestinal barrier. These rocket-inspired effervescent motors (RIEMs) are composed of sharp needle tips for both loading cargoes and efficient penetrating, and tail wings for loading effervescent powders and avoiding perforation. When exposed to a water environment, the effervescent fuel generates intensive CO2 bubbles to propel the RIEMs to move at high speed. Thus, the RIEMs with their sharp tip can inject into the surrounding mucosa for effective drug release. Furthermore, benefiting from their tail-wing design, perforation can be effectively avoided during the injection process, ensuring the safety of the RIEMs in gastrointestinal active delivery. Based on these advantages, it is demonstrated that the RIEMs can efficiently move and stab into the intestinal mucosa for insulin delivery, exhibiting efficacy in regulating blood sugar glucose in a diabetic rabbit model. These features indicate that these RIEMs are versatile and valuable for clinical oral delivery of macromolecules.
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Affiliation(s)
- Lijun Cai
- Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Guopu Chen
- Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Lingyu Sun
- Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Shuangshuang Miao
- Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Luoran Shang
- Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, and the Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Yuanjin Zhao
- Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, China
- Chemistry and Biomedicine Innovation Center, Nanjing University, Nanjing, 210023, P. R. China
| | - Lingyun Sun
- Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
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10
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Watson JL, Juergens D, Bennett NR, Trippe BL, Yim J, Eisenach HE, Ahern W, Borst AJ, Ragotte RJ, Milles LF, Wicky BIM, Hanikel N, Pellock SJ, Courbet A, Sheffler W, Wang J, Venkatesh P, Sappington I, Torres SV, Lauko A, De Bortoli V, Mathieu E, Ovchinnikov S, Barzilay R, Jaakkola TS, DiMaio F, Baek M, Baker D. De novo design of protein structure and function with RFdiffusion. Nature 2023; 620:1089-1100. [PMID: 37433327 PMCID: PMC10468394 DOI: 10.1038/s41586-023-06415-8] [Citation(s) in RCA: 173] [Impact Index Per Article: 173.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 07/07/2023] [Indexed: 07/13/2023]
Abstract
There has been considerable recent progress in designing new proteins using deep-learning methods1-9. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.
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Affiliation(s)
- Joseph L Watson
- 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
| | - Nathaniel R Bennett
- 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
| | - Brian L Trippe
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Columbia University, Department of Statistics, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Jason Yim
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Helen E Eisenach
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Woody Ahern
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Andrew J Borst
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Robert J Ragotte
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lukas F Milles
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Basile I M Wicky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Nikita Hanikel
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Samuel J Pellock
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alexis Courbet
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- National Centre for Scientific Research, École Normale Supérieure rue d'Ulm, Paris, France
| | - William Sheffler
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jue Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Preetham Venkatesh
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Isaac Sappington
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Susana Vázquez Torres
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Anna Lauko
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Valentin De Bortoli
- National Centre for Scientific Research, École Normale Supérieure rue d'Ulm, Paris, France
| | - Emile Mathieu
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Sergey Ovchinnikov
- Faculty of Applied Sciences, Harvard University, Cambridge, MA, USA
- John Harvard Distinguished Science Fellowship, Harvard University, Cambridge, MA, USA
| | | | | | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Minkyung Baek
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - 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.
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11
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Zheng Y, Chen XX, Zhang DY, Wang WJ, Peng K, Li ZY, Mao ZW, Tan CP. Activation of the cGAS-STING pathway by a mitochondrial DNA-targeted emissive rhodium(iii) metallointercalator. Chem Sci 2023; 14:6890-6903. [PMID: 37389261 PMCID: PMC10306090 DOI: 10.1039/d3sc01737k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/05/2023] [Indexed: 07/01/2023] Open
Abstract
The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon (STING) pathway is a key mediator of innate immunity involved in cancer development and treatment. The roles of mitochondrial DNA (mtDNA) in cancer immunotherapy have gradually emerged. Herein, we report a highly emissive rhodium(iii) complex (Rh-Mito) as the mtDNA intercalator. Rh-Mito can specifically bind to mtDNA to cause the cytoplasmic release of mtDNA fragments to activate the cGAS-STING pathway. Moreover, Rh-Mito activates the mitochondrial retrograde signaling by disturbing the key metabolites involved in epigenetic modifications, which alters the nuclear genome methylation landscape to influence the expression of genes related to immune signaling pathways. Finally, we demonstrate that ferritin-encapsulated Rh-Mito elicits potent anticancer activities and evokes intense immune responses in vivo by intravenous injection. Overall, we report for the first time that small molecules targeting mtDNA can activate the cGAS-STING pathway, which gives insights into the development of biomacromolecule-targeted immunotherapeutic agents.
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Affiliation(s)
- Yue Zheng
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Guangzhou 510006 P. R. China
| | - Xiao-Xiao Chen
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Guangzhou 510006 P. R. China
| | - Dong-Yang Zhang
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Guangzhou 510006 P. R. China
| | - Wen-Jin Wang
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Guangzhou 510006 P. R. China
| | - Kun Peng
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Guangzhou 510006 P. R. China
| | - Zhi-Yuan Li
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Guangzhou 510006 P. R. China
| | - Zong-Wan Mao
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Guangzhou 510006 P. R. China
| | - Cai-Ping Tan
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Guangzhou 510006 P. R. China
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12
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Cai L, Wang Y, Chen Y, Chen H, Yang T, Zhang S, Guo Z, Wang X. Manganese(ii) complexes stimulate antitumor immunity via aggravating DNA damage and activating the cGAS-STING pathway. Chem Sci 2023; 14:4375-4389. [PMID: 37123182 PMCID: PMC10132258 DOI: 10.1039/d2sc06036a] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/22/2023] [Indexed: 04/05/2023] Open
Abstract
Activating the cyclic GMP-AMP synthase-stimulator of the interferon gene (cGAS-STING) pathway is a promising immunotherapeutic strategy for cancer treatment. Manganese(ii) complexes MnPC and MnPVA (P = 1,10-phenanthroline, C = chlorine, and VA = valproic acid) were found to activate the cGAS-STING pathway. The complexes not only damaged DNA, but also inhibited histone deacetylases (HDACs) and poly adenosine diphosphate-ribose polymerase (PARP) to impede the repair of DNA damage, thereby promoting the leakage of DNA fragments into cytoplasm. The DNA fragments activated the cGAS-STING pathway, which initiated an innate immune response and a two-way communication between tumor cells and neighboring immune cells. The activated cGAS-STING further increased the production of type I interferons and secretion of pro-inflammatory cytokines (TNF-α and IL-6), boosting the tumor infiltration of dendritic cells and macrophages, as well as stimulating cytotoxic T cells to kill cancer cells in vitro and in vivo. Owing to the enhanced DNA-damaging ability, MnPC and MnPVA showed more potent immunocompetence and antitumor activity than Mn2+ ions, thus demonstrating great potential as chemoimmunotherapeutic agents for cancer treatment.
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Affiliation(s)
- Linxiang Cai
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University Nanjing 210023 P. R. China +86 25 89684549 +86 2589684549
| | - Ying Wang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University Nanjing 210023 P. R. China +86 25 89684549 +86 2589684549
| | - Yayu Chen
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University Nanjing 210023 P. R. China +86 25 89684549 +86 2589684549
| | - Hanhua Chen
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University Nanjing 210023 P. R. China +86 25 89684549 +86 2589684549
| | - Tao Yang
- State Key Laboratory of Coordination Chemistry, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 P. R. China
| | - Shuren Zhang
- State Key Laboratory of Coordination Chemistry, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 P. R. China
| | - Zijian Guo
- State Key Laboratory of Coordination Chemistry, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 P. R. China
| | - Xiaoyong Wang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University Nanjing 210023 P. R. China +86 25 89684549 +86 2589684549
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13
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Wang X, Xu K, Tan Y, Liu S, Zhou J. Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes. Int J Mol Sci 2023; 24:3827. [PMID: 36835238 PMCID: PMC9964944 DOI: 10.3390/ijms24043827] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/03/2023] [Accepted: 02/03/2023] [Indexed: 02/17/2023] Open
Abstract
Food enzymes have an important role in the improvement of certain food characteristics, such as texture improvement, elimination of toxins and allergens, production of carbohydrates, enhancing flavor/appearance characteristics. Recently, along with the development of artificial meats, food enzymes have been employed to achieve more diverse functions, especially in converting non-edible biomass to delicious foods. Reported food enzyme modifications for specific applications have highlighted the significance of enzyme engineering. However, using direct evolution or rational design showed inherent limitations due to the mutation rates, which made it difficult to satisfy the stability or specific activity needs for certain applications. Generating functional enzymes using de novo design, which highly assembles naturally existing enzymes, provides potential solutions for screening desired enzymes. Here, we describe the functions and applications of food enzymes to introduce the need for food enzymes engineering. To illustrate the possibilities of using de novo design for generating diverse functional proteins, we reviewed protein modelling and de novo design methods and their implementations. The future directions for adding structural data for de novo design model training, acquiring diversified training data, and investigating the relationship between enzyme-substrate binding and activity were highlighted as challenges to overcome for the de novo design of food enzymes.
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Affiliation(s)
- Xinglong Wang
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Kangjie Xu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Yameng Tan
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Song Liu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Jingwen Zhou
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
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14
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Kordes S, Beck J, Shanmugaratnam S, Flecks M, Höcker B. Physics-based approach to extend a de novo TIM barrel with rationally designed helix-loop-helix motifs. Protein Eng Des Sel 2023; 36:gzad012. [PMID: 37707513 DOI: 10.1093/protein/gzad012] [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: 07/31/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023] Open
Abstract
Computational protein design promises the ability to build tailor-made proteins de novo. While a range of de novo proteins have been constructed so far, the majority of these designs have idealized topologies that lack larger cavities which are necessary for the incorporation of small molecule binding sites or enzymatic functions. One attractive target for enzyme design is the TIM-barrel fold, due to its ubiquity in nature and capability to host versatile functions. With the successful de novo design of a 4-fold symmetric TIM barrel, sTIM11, an idealized, minimalistic scaffold was created. In this work, we attempted to extend this de novo TIM barrel by incorporating a helix-loop-helix motif into its βα-loops by applying a physics-based modular design approach using Rosetta. Further diversification was performed by exploiting the symmetry of the scaffold to integrate two helix-loop-helix motifs into the scaffold. Analysis with AlphaFold2 and biochemical characterization demonstrate the formation of additional α-helical secondary structure elements supporting the successful extension as intended.
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Affiliation(s)
- Sina Kordes
- Department of Biochemistry, University of Bayreuth, Bayreuth 95447, Germany
| | - Julian Beck
- Department of Biochemistry, University of Bayreuth, Bayreuth 95447, Germany
| | | | - Merle Flecks
- Department of Biochemistry, University of Bayreuth, Bayreuth 95447, Germany
| | - Birte Höcker
- Department of Biochemistry, University of Bayreuth, Bayreuth 95447, Germany
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15
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Yu M, Yang W, Yue W, Chen Y. Targeted Cancer Immunotherapy: Nanoformulation Engineering and Clinical Translation. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2204335. [PMID: 36257824 PMCID: PMC9762307 DOI: 10.1002/advs.202204335] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/17/2022] [Indexed: 05/09/2023]
Abstract
With the rapid growth of advanced nanoengineering strategies, there are great implications for therapeutic immunostimulators formulated in nanomaterials to combat cancer. It is crucial to direct immunostimulators to the right tissue and specific immune cells at the right time, thereby orchestrating the desired, potent, and durable immune response against cancer. The flexibility of nanoformulations in size, topology, softness, and multifunctionality allows precise regulation of nano-immunological activities for enhanced therapeutic effect. To grasp the modulation of immune response, research efforts are needed to understand the interactions of immune cells at lymph organs and tumor tissues, where the nanoformulations guide the immunostimulators to function on tissue specific subsets of immune cells. In this review, recent advanced nanoformulations targeting specific subset of immune cells, such as dendritic cells (DCs), T cells, monocytes, macrophages, and natural killer (NK) cells are summarized and discussed, and clinical development of nano-paradigms for targeted cancer immunotherapy is highlighted. Here the focus is on the targeting nanoformulations that can passively or actively target certain immune cells by overcoming the physiobiological barriers, instead of directly injecting into tissues. The opportunities and remaining obstacles for the clinical translation of immune cell targeting nanoformulations in cancer therapy are also discussed.
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Affiliation(s)
- Meihua Yu
- Materdicine LabSchool of Life SciencesShanghai UniversityShanghai200444P. R. China
| | - Wei Yang
- Department of UrologyXinhua HospitalSchool of MedicineShanghai Jiaotong University1665 Kongjiang RoadShanghai200092P. R. China
| | - Wenwen Yue
- Shanghai Engineering Research Center of Ultrasound Diagnosis and TreatmentDepartment of Medical UltrasoundShanghai Tenth People's HospitalUltrasound Research and Education InstituteTongji University Cancer CenterTongji University School of MedicineShanghai200072P. R. China
| | - Yu Chen
- Materdicine LabSchool of Life SciencesShanghai UniversityShanghai200444P. R. China
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16
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Linsky TW, Noble K, Tobin AR, Crow R, Carter L, Urbauer JL, Baker D, Strauch EM. Sampling of structure and sequence space of small protein folds. Nat Commun 2022; 13:7151. [PMID: 36418330 PMCID: PMC9684540 DOI: 10.1038/s41467-022-34937-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 11/10/2022] [Indexed: 11/24/2022] Open
Abstract
Nature only samples a small fraction of the sequence space that can fold into stable proteins. Furthermore, small structural variations in a single fold, sometimes only a few amino acids, can define a protein's molecular function. Hence, to design proteins with novel functionalities, such as molecular recognition, methods to control and sample shape diversity are necessary. To explore this space, we developed and experimentally validated a computational platform that can design a wide variety of small protein folds while sampling shape diversity. We designed and evaluated stability of about 30,000 de novo protein designs of eight different folds. Among these designs, about 6,200 stable proteins were identified, including some predicted to have a first-of-its-kind minimalized thioredoxin fold. Obtained data revealed protein folding rules for structural features such as helix-connecting loops. Beyond serving as a resource for protein engineering, this massive and diverse dataset also provides training data for machine learning. We developed an accurate classifier to predict the stability of our designed proteins. The methods and the wide range of protein shapes provide a basis for designing new protein functions without compromising stability.
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Affiliation(s)
- Thomas W Linsky
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Kyle Noble
- Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, GA, 30602, USA
| | - Autumn R Tobin
- Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, GA, 30602, USA
| | - Rachel Crow
- Department of Microbiology, University of Washington, Seattle, WA, 98195, USA
| | - Lauren Carter
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Jeffrey L Urbauer
- Department of Chemistry, University of Georgia, Athens, GA, 30602, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, 98195, USA
| | - Eva-Maria Strauch
- Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, GA, 30602, USA.
- Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA.
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17
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Liu H, Chen Q. Computational protein design with data‐driven approaches: Recent developments and perspectives. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Haiyan Liu
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui China
- Biomedical Sciences and Health Laboratory of Anhui Province University of Science and Technology of China Hefei Anhui China
- School of Data Science University of Science and Technology of China Hefei Anhui China
| | - Quan Chen
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui China
- Biomedical Sciences and Health Laboratory of Anhui Province University of Science and Technology of China Hefei Anhui China
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18
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Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation. Proc Natl Acad Sci U S A 2022; 119:e2122676119. [PMID: 36191185 PMCID: PMC9564214 DOI: 10.1073/pnas.2122676119] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Designing entirely new protein structures remains challenging because we do not fully understand the biophysical determinants of folding stability. Yet, some protein folds are easier to design than others. Previous work identified the 43-residue ɑββɑ fold as especially challenging: The best designs had only a 2% success rate, compared to 39 to 87% success for other simple folds [G. J. Rocklin et al., Science 357, 168-175 (2017)]. This suggested the ɑββɑ fold would be a useful model system for gaining a deeper understanding of folding stability determinants and for testing new protein design methods. Here, we designed over 10,000 new ɑββɑ proteins and found over 3,000 of them to fold into stable structures using a high-throughput protease-based assay. NMR, hydrogen-deuterium exchange, circular dichroism, deep mutational scanning, and scrambled sequence control experiments indicated that our stable designs fold into their designed ɑββɑ structures with exceptional stability for their small size. Our large dataset enabled us to quantify the influence of universal stability determinants including nonpolar burial, helix capping, and buried unsatisfied polar atoms, as well as stability determinants unique to the ɑββɑ topology. Our work demonstrates how large-scale design and test cycles can solve challenging design problems while illuminating the biophysical determinants of folding.
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19
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Peñas-Utrilla D, Marcos E. Identifying well-folded de novo proteins in the new era of accurate structure prediction. Front Mol Biosci 2022; 9:991380. [PMID: 36275629 PMCID: PMC9581288 DOI: 10.3389/fmolb.2022.991380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 09/20/2022] [Indexed: 11/29/2022] Open
Abstract
Computational de novo protein design tailors proteins for target structures and oligomerisation states with high stability, which allows overcoming many limitations of natural proteins when redesigned for new functions. Despite significant advances in the field over the past decade, it remains challenging to predict sequences that will fold as stable monomers in solution or binders to a particular protein target; thereby requiring substantial experimental resources to identify proteins with the desired properties. To overcome this, here we leveraged the large amount of design data accumulated in the last decade, and the breakthrough in protein structure prediction from last year to investigate on improved ways of selecting promising designs before experimental testing. We collected de novo proteins from previous studies, 518 designed as monomers of different folds and 2112 as binders against the Botulinum neurotoxin, and analysed their structures with AlphaFold2, RoseTTAFold and fragment quality descriptors in combination with other properties related to surface interactions. These features showed high complementarity in rationalizing the experimental results, which allowed us to generate quite accurate machine learning models for predicting well-folded monomers and binders with a small set of descriptors. Cross-validating designs with varied orthogonal computational techniques should guide us for identifying design imperfections, rescuing designs and making more robust design selections before experimental testing.
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20
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Yadahalli S, Jayanthi LP, Gosavi S. A Method for Assessing the Robustness of Protein Structures by Randomizing Packing Interactions. Front Mol Biosci 2022; 9:849272. [PMID: 35832734 PMCID: PMC9271847 DOI: 10.3389/fmolb.2022.849272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/27/2022] [Indexed: 12/02/2022] Open
Abstract
Many single-domain proteins are not only stable and water-soluble, but they also populate few to no intermediates during folding. This reduces interactions between partially folded proteins, misfolding, and aggregation, and makes the proteins tractable in biotechnological applications. Natural proteins fold thus, not necessarily only because their structures are well-suited for folding, but because their sequences optimize packing and fit their structures well. In contrast, folding experiments on the de novo designed Top7 suggest that it populates several intermediates. Additionally, in de novo protein design, where sequences are designed for natural and new non-natural structures, tens of sequences still need to be tested before success is achieved. Both these issues may be caused by the specific scaffolds used in design, i.e., some protein scaffolds may be more tolerant to packing perturbations and varied sequences. Here, we report a computational method for assessing the response of protein structures to packing perturbations. We then benchmark this method using designed proteins and find that it can identify scaffolds whose folding gets disrupted upon perturbing packing, leading to the population of intermediates. The method can also isolate regions of both natural and designed scaffolds that are sensitive to such perturbations and identify contacts which when present can rescue folding. Overall, this method can be used to identify protein scaffolds that are more amenable to whole protein design as well as to identify protein regions which are sensitive to perturbations and where further mutations should be avoided during protein engineering.
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21
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Abstract
The ability to design efficient enzymes from scratch would have a profound effect on chemistry, biotechnology and medicine. Rapid progress in protein engineering over the past decade makes us optimistic that this ambition is within reach. The development of artificial enzymes containing metal cofactors and noncanonical organocatalytic groups shows how protein structure can be optimized to harness the reactivity of nonproteinogenic elements. In parallel, computational methods have been used to design protein catalysts for diverse reactions on the basis of fundamental principles of transition state stabilization. Although the activities of designed catalysts have been quite low, extensive laboratory evolution has been used to generate efficient enzymes. Structural analysis of these systems has revealed the high degree of precision that will be needed to design catalysts with greater activity. To this end, emerging protein design methods, including deep learning, hold particular promise for improving model accuracy. Here we take stock of key developments in the field and highlight new opportunities for innovation that should allow us to transition beyond the current state of the art and enable the robust design of biocatalysts to address societal needs.
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22
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Fontecilla-Camps JC, Volbeda A. Quinolinate Synthase: An Example of the Roles of the Second and Outer Coordination Spheres in Enzyme Catalysis. Chem Rev 2022; 122:12110-12131. [PMID: 35536891 DOI: 10.1021/acs.chemrev.1c00869] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The activation energy barrier of biochemical reactions is normally lowered by an enzyme catalyst, which directly helps the weakening of the bond(s) to be broken. In many metalloenzymes, this is a first coordination sphere effect. Besides having a direct catalytic action, enzymes can fix their reactive groups and substrates so that they are optimally positioned and also modify the water activity in the system. They can either activate substrates prior to their reaction or bind preactivated substrates, thereby drastically reducing local entropic effects. The latter type is well represented by some bisubstrate reactions, where they have been defined as "entropic traps". These can be described as "second coordination sphere" processes, but enzymes can also control the reactivity beyond this point through local conformational changes belonging to an "outer coordinate sphere" that can be modulated by substrate binding. We have chosen the [4Fe-4S] cluster-dependent enzyme quinolinate synthase to illustrate each one of these processes. In addition, this very old metalloenzyme shows low in vitro substrate binding specificity, atypical reactivity that produces dead-end products, and a unique modulation of its active site volume.
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Affiliation(s)
| | - Anne Volbeda
- Université Grenoble Alpes, CEA, CNRS, IBS, Metalloproteins Unit, F-38000 Grenoble, France
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23
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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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24
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Krivacic C, Kundert K, Pan X, Pache RA, Liu L, Conchúir SO, Jeliazkov JR, Gray JJ, Thompson MC, Fraser JS, Kortemme T. Accurate positioning of functional residues with robotics-inspired computational protein design. Proc Natl Acad Sci U S A 2022; 119:e2115480119. [PMID: 35254891 PMCID: PMC8931229 DOI: 10.1073/pnas.2115480119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/27/2022] [Indexed: 11/18/2022] Open
Abstract
SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein's preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.
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Affiliation(s)
- Cody Krivacic
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Kale Kundert
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Biophysics Graduate Program, University of California, San Francisco, CA 94158
| | - Xingjie Pan
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Roland A. Pache
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Lin Liu
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Shane O Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | | | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Michael C. Thompson
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - James S. Fraser
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Biophysics Graduate Program, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
| | - Tanja Kortemme
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Biophysics Graduate Program, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
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25
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Zhou J, Cui X, Xie Y, Zhang M, Gao J, Zhou X, Ding J, Cen S. Identification of Ziyuglycoside II from natural products library as a novel STING agonist. ChemMedChem 2022; 17:e202100719. [PMID: 35293138 DOI: 10.1002/cmdc.202100719] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/11/2022] [Indexed: 11/05/2022]
Abstract
Given the emerging pivotal roles of STING (stimulator of interferon genes) in host pathogen defense and immune-oncology, STING is regarded as a promising target for drug development. CDNs (cyclic dinucleotides) are the first-generation STING agonists. However, their poor metabolic stability and membrane permeability utterly limits therapeutic applications. By contrast, small molecule STING agonists show superiority of properties such as molecular weight, polar character, and delivery diversity. The quest for the potent small molecular agonist of human STING remains ongoing. In our study, through an IRF/IFN pathway-targeted cell-based screen of natural products library, we identified a small-molecular STING agonist Ziyuglycoside II, termed as ST12, with potent stimulation of IRF/IFN pathway and NF-κB pathway. Furthermore, its binding to the C-terminal domain of human STING detected by bio-layer interferometry technique, indicating that ST12 is a human STING agonist. Further tanimoto similarity analyze with existing small-molecule STING agonists indicates that ST12 represents a lead compound with a novel core-structure for the further optimization. Insert abstract text here.
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Affiliation(s)
- Jinming Zhou
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences, Immunology, Nanwei Road, 100050, Beijing, CHINA
| | - Xiangling Cui
- Institute of Medicinal biotechnology, Medicinal chemistry, CHINA
| | - Yongli Xie
- Institute of Medicinal biotechnology, Medicinal chemistry, CHINA
| | - Min Zhang
- Zhejiang Normal University, College of Chemistry and Life Science, CHINA
| | - Jieke Gao
- Zhejiang Normal University, College of Chemistry and Life Science, CHINA
| | - Xujun Zhou
- Zhejiang Normal University, College of Chemistry and Life Science, CHINA
| | - Jiwei Ding
- Institute of Medicinal Biotechnology, Medicinal chemistry, CHINA
| | - Shan Cen
- Institute of Medicinal Biotechnology, Immune, CHINA
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26
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A backbone-centred energy function of neural networks for protein design. Nature 2022; 602:523-528. [PMID: 35140398 DOI: 10.1038/s41586-021-04383-5] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 12/23/2021] [Indexed: 12/29/2022]
Abstract
A protein backbone structure is designable if a substantial number of amino acid sequences exist that autonomously fold into it1,2. It has been suggested that the designability of backbones is governed mainly by side chain-independent or side chain type-insensitive molecular interactions3-5, indicating an approach for designing new backbones (ready for amino acid selection) based on continuous sampling and optimization of the backbone-centred energy surface. However, a sufficiently comprehensive and precise energy function has yet to be established for this purpose. Here we show that this goal is met by a statistical model named SCUBA (for Side Chain-Unknown Backbone Arrangement) that uses neural network-form energy terms. These terms are learned with a two-step approach that comprises kernel density estimation followed by neural network training and can analytically represent multidimensional, high-order correlations in known protein structures. We report the crystal structures of nine de novo proteins whose backbones were designed to high precision using SCUBA, four of which have novel, non-natural overall architectures. By eschewing use of fragments from existing protein structures, SCUBA-driven structure design facilitates far-reaching exploration of the designable backbone space, thus extending the novelty and diversity of the proteins amenable to de novo design.
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27
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Boral A, Khamaru M, Mitra D. Designing synthetic transcription factors: A structural perspective. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 130:245-287. [PMID: 35534109 DOI: 10.1016/bs.apcsb.2021.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In this chapter, we discuss different design strategies of synthetic proteins, especially synthetic transcription factors. Design and engineering of synthetic transcription factors is particularly relevant for the need-based manipulation of gene expression. With recent advances in structural biology techniques and with the emergence of other precision biochemical/physical tools, accurate knowledge on structure-function relations is increasingly becoming available. Besides discussing the underlying principles of design, we go through individual cases, especially those involving four major groups of transcription factors-basic leucine zippers, zinc fingers, helix-turn-helix and homeodomains. We further discuss how synthetic biology can come together with structural biology to alter the genetic blueprint of life.
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Affiliation(s)
- Aparna Boral
- Department of Life Sciences, Presidency University, Kolkata, West Bengal, India
| | - Madhurima Khamaru
- Department of Life Sciences, Presidency University, Kolkata, West Bengal, India
| | - Devrani Mitra
- Department of Life Sciences, Presidency University, Kolkata, West Bengal, India.
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28
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Zhao L, Zhang J, Zhang Y, Ye S, Zhang G, Chen X, Jiang B, Jiang J. Accurate Machine Learning Prediction of Protein Circular Dichroism Spectra with Embedded Density Descriptors. JACS AU 2021; 1:2377-2384. [PMID: 34977905 PMCID: PMC8715543 DOI: 10.1021/jacsau.1c00449] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Indexed: 05/08/2023]
Abstract
A data-driven approach to simulate circular dichroism (CD) spectra is appealing for fast protein secondary structure determination, yet the challenge of predicting electric and magnetic transition dipole moments poses a substantial barrier for the goal. To address this problem, we designed a new machine learning (ML) protocol in which ordinary pure geometry-based descriptors are replaced with alternative embedded density descriptors and electric and magnetic transition dipole moments are successfully predicted with an accuracy comparable to first-principle calculation. The ML model is able to not only simulate protein CD spectra nearly 4 orders of magnitude faster than conventional first-principle simulation but also obtain CD spectra in good agreement with experiments. Finally, we predicted a series of CD spectra of the Trp-cage protein associated with continuous changes of protein configuration along its folding path, showing the potential of our ML model for supporting real-time CD spectroscopy study of protein dynamics.
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Affiliation(s)
- Luyuan Zhao
- Hefei
National Laboratory for Physical Sciences at the Microscale, Collaborative
Innovation Center of Chemistry for Energy Materials, School of Chemistry
and Materials Science, University of Science
and Technology of China, Hefei, Anhui 230026, P. R. China
| | - Jinxiao Zhang
- Guangxi
Key Laboratory of Electrochemical and Magneto-chemical Functional
Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin 541006, P. R. China
| | - Yaolong Zhang
- Hefei
National Laboratory for Physical Sciences at the Microscale, Collaborative
Innovation Center of Chemistry for Energy Materials, School of Chemistry
and Materials Science, University of Science
and Technology of China, Hefei, Anhui 230026, P. R. China
| | - Sheng Ye
- School
of Artificial Intelligence, Anhui University, Hefei, Anhui 230601, P. R. China
| | - Guozhen Zhang
- Hefei
National Laboratory for Physical Sciences at the Microscale, Collaborative
Innovation Center of Chemistry for Energy Materials, School of Chemistry
and Materials Science, University of Science
and Technology of China, Hefei, Anhui 230026, P. R. China
| | - Xin Chen
- Gusu
Laboratory of Materials, Suzhou, Jiangsu 215123, P. R. China
| | - Bin Jiang
- Hefei
National Laboratory for Physical Sciences at the Microscale, Collaborative
Innovation Center of Chemistry for Energy Materials, School of Chemistry
and Materials Science, University of Science
and Technology of China, Hefei, Anhui 230026, P. R. China
| | - Jun Jiang
- Hefei
National Laboratory for Physical Sciences at the Microscale, Collaborative
Innovation Center of Chemistry for Energy Materials, School of Chemistry
and Materials Science, University of Science
and Technology of China, Hefei, Anhui 230026, P. R. China
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Pan X, Kortemme T. De novo protein fold families expand the designable ligand binding site space. PLoS Comput Biol 2021; 17:e1009620. [PMID: 34807909 PMCID: PMC8648124 DOI: 10.1371/journal.pcbi.1009620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 12/06/2021] [Accepted: 11/08/2021] [Indexed: 11/19/2022] Open
Abstract
A major challenge in designing proteins de novo to bind user-defined ligands with high affinity is finding backbones structures into which a new binding site geometry can be engineered with high precision. Recent advances in methods to generate protein fold families de novo have expanded the space of accessible protein structures, but it is not clear to what extend de novo proteins with diverse geometries also expand the space of designable ligand binding functions. We constructed a library of 25,806 high-quality ligand binding sites and developed a fast protocol to place (“match”) these binding sites into both naturally occurring and de novo protein families with two fold topologies: Rossman and NTF2. Each matching step involves engineering new binding site residues into each protein “scaffold”, which is distinct from the problem of comparing already existing binding pockets. 5,896 and 7,475 binding sites could be matched to the Rossmann and NTF2 fold families, respectively. De novo designed Rossman and NTF2 protein families can support 1,791 and 678 binding sites that cannot be matched to naturally existing structures with the same topologies, respectively. While the number of protein residues in ligand binding sites is the major determinant of matching success, ligand size and primary sequence separation of binding site residues also play important roles. The number of matched binding sites are power law functions of the number of members in a fold family. Our results suggest that de novo sampling of geometric variations on diverse fold topologies can significantly expand the space of designable ligand binding sites for a wealth of possible new protein functions. De novo design of proteins that can bind to novel and highly diverse user-defined small molecule ligands could have broad biomedical and synthetic biology applications. Because ligand binding site geometries need to be accommodated by protein backbone scaffolds at high accuracy, the diversity of scaffolds is a major limitation for designing new ligand binding functions. Advances in computational protein structure design methods have significantly increased the number of accessible stable scaffold structures. Understanding how many new ligand binding sites can be designed into the de novo scaffolds is important for engineering novel ligand binding proteins. To answer this question, we constructed a large library of ligand binding sites from the Protein Data Bank (PDB). We tested the number of ligand binding sites that can be designed into de novo scaffolds and naturally existing scaffolds with the same fold topologies. The results showed that de novo scaffolds significantly expanded the potential ligand binding space of their respective fold topologies. We also identified factors that affect difficulties of binding site accommodation, as well as the relationship between the number of scaffolds and the accessible ligand binding site space. We believe our findings will benefit future method development and applications of ligand binding protein design.
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Affiliation(s)
- Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, United States of America
- * E-mail: (XP); (TK)
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, United States of America
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California, United States of America
- Chan Zuckerberg Biohub, San Francisco, California, United States of America
- * E-mail: (XP); (TK)
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31
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Reinkemeier CD, Lemke EA. Dual film-like organelles enable spatial separation of orthogonal eukaryotic translation. Cell 2021; 184:4886-4903.e21. [PMID: 34433013 PMCID: PMC8480389 DOI: 10.1016/j.cell.2021.08.001] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 05/03/2021] [Accepted: 08/02/2021] [Indexed: 11/18/2022]
Abstract
Engineering new functionality into living eukaryotic systems by enzyme evolution or de novo protein design is a formidable challenge. Cells do not rely exclusively on DNA-based evolution to generate new functionality but often utilize membrane encapsulation or formation of membraneless organelles to separate distinct molecular processes that execute complex operations. Applying this principle and the concept of two-dimensional phase separation, we develop film-like synthetic organelles that support protein translation on the surfaces of various cellular membranes. These sub-resolution synthetic films provide a path to make functionally distinct enzymes within the same cell. We use these film-like organelles to equip eukaryotic cells with dual orthogonal expanded genetic codes that enable the specific reprogramming of distinct translational machineries with single-residue precision. The ability to spatially tune the output of translation within tens of nanometers is not only important for synthetic biology but has implications for understanding the function of membrane-associated protein condensation in cells. 2D phase separation was utilized to design orthogonal enzymes Film-like organelles maintained distinct suppressor tRNA microenvironments Dual film-like synthetic organelles enabled orthogonal translation in eukaryotes Cells were equipped with two expanded genetic codes in addition to the canonical one
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Affiliation(s)
- Christopher D Reinkemeier
- Biocentre, Departments of Biology and Chemistry, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch-Weg 17, 55128 Mainz, Germany; Institute of Molecular Biology gGmbH, Ackermannweg 4, 55128 Mainz, Germany; Structural and Computational Biology Unit and Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Edward A Lemke
- Biocentre, Departments of Biology and Chemistry, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch-Weg 17, 55128 Mainz, Germany; Institute of Molecular Biology gGmbH, Ackermannweg 4, 55128 Mainz, Germany; Structural and Computational Biology Unit and Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.
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32
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Alberstein RG, Guo AB, Kortemme T. Design principles of protein switches. Curr Opin Struct Biol 2021; 72:71-78. [PMID: 34537489 DOI: 10.1016/j.sbi.2021.08.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/03/2021] [Accepted: 08/06/2021] [Indexed: 01/14/2023]
Abstract
Protein switches perform essential roles in many biological processes and are exciting targets for de novo protein design, which aims to produce proteins of arbitrary shape and functionality. However, the biophysical requirements for switch function - multiple conformational states, fine-tuned energetics, and stimuli-responsiveness - pose a formidable challenge for design by computation (or intuition). A variety of methods have been developed toward tackling this challenge, usually taking inspiration from the wealth of sequence and structural information available for naturally occurring protein switches. More recently, modular switches have been designed computationally, and new methods have emerged for sampling unexplored structure space, providing promising new avenues toward the generation of purpose-built switches and de novo signaling systems for cellular engineering.
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Affiliation(s)
- Robert G Alberstein
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Amy B Guo
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA; UC Berkeley-UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, CA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA; UC Berkeley-UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, CA, USA; Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.
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33
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The register shift rules for βαβ-motifs for de novo protein design. PLoS One 2021; 16:e0256895. [PMID: 34460870 PMCID: PMC8405016 DOI: 10.1371/journal.pone.0256895] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 08/17/2021] [Indexed: 11/19/2022] Open
Abstract
A wide range of de novo design of αβ-proteins has been achieved based on the design rules, which describe secondary structure lengths and loop torsion patterns favorable for design target topologies. This paper proposes design rules for register shifts in βαβ-motifs, which have not been reported previously, but are necessary for determining a target structure of de novo design of αβ-proteins. By analyzing naturally occurring protein structures in a database, we found preferences for register shifts in βαβ-motifs, and derived the following empirical rules: (1) register shifts must not be negative regardless of torsion types for a constituent loop in βαβ-motifs; (2) preferred register shifts strongly depend on the loop torsion types. To explain these empirical rules by physical interactions, we conducted physics-based simulations for systems mimicking a βαβ-motif that contains the most frequently observed loop type in the database. We performed an exhaustive conformational sampling of the loop region, imposing the exclusion volume and hydrogen bond satisfaction condition. The distributions of register shifts obtained from the simulations agreed well with those of the database analysis, indicating that the empirical rules are a consequence of physical interactions, rather than an evolutionary sampling bias. Our proposed design rules will serve as a guide to making appropriate target structures for the de novo design of αβ-proteins.
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34
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Woolfson DN. A Brief History of De Novo Protein Design: Minimal, Rational, and Computational. J Mol Biol 2021; 433:167160. [PMID: 34298061 DOI: 10.1016/j.jmb.2021.167160] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 07/07/2021] [Accepted: 07/12/2021] [Indexed: 12/26/2022]
Abstract
Protein design has come of age, but how will it mature? In the 1980s and the 1990s, the primary motivation for de novo protein design was to test our understanding of the informational aspect of the protein-folding problem; i.e., how does protein sequence determine protein structure and function? This necessitated minimal and rational design approaches whereby the placement of each residue in a design was reasoned using chemical principles and/or biochemical knowledge. At that time, though with some notable exceptions, the use of computers to aid design was not widespread. Over the past two decades, the tables have turned and computational protein design is firmly established. Here, I illustrate this progress through a timeline of de novo protein structures that have been solved to atomic resolution and deposited in the Protein Data Bank. From this, it is clear that the impact of rational and computational design has been considerable: More-complex and more-sophisticated designs are being targeted with many being resolved to atomic resolution. Furthermore, our ability to generate and manipulate synthetic proteins has advanced to a point where they are providing realistic alternatives to natural protein functions for applications both in vitro and in cells. Also, and increasingly, computational protein design is becoming accessible to non-specialists. This all begs the questions: Is there still a place for minimal and rational design approaches? And, what challenges lie ahead for the burgeoning field of de novo protein design as a whole?
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Affiliation(s)
- Derek N Woolfson
- School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK; School of Biochemistry, University of Bristol, Biomedical Sciences Building, University Walk, Bristol BS8 1TD, UK; Bristol BioDesign Institute, University of Bristol, Life Sciences Building, Tyndall Avenue, Bristol BS8 1TQ, UK.
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35
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Zhou Q, Zhou Y, Li T, Ge Z. Nanoparticle-Mediated STING Agonist Delivery for Enhanced Cancer Immunotherapy. Macromol Biosci 2021; 21:e2100133. [PMID: 34117839 DOI: 10.1002/mabi.202100133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 05/17/2021] [Indexed: 12/12/2022]
Abstract
Stimulator of interferon genes (STING) are located in the endoplasmic reticulum of cells, which have been demonstrated to show considerable potentials to achieve efficient antitumor immunity by inducing various pro-inflammatory cytokines and chemokines, such as type I interferons. A variety of STING agonists have been prepared for STING activation, and many of them have been promoted to preclinical trials or clinical applications for the immunotherapy of cancers. However, the intrinsic disadvantages of the small molecule STING agonists can limit the in vivo application and final therapeutic efficacy due to low bioavailability of targeting tissues. Moreover, a cascade of physiological barriers for in vivo STING activation also limit the accumulation of STING agonists in targeting tissues. Drug delivery systems play an important role to improve the STING activation efficiency. In recent years, a variety of nanoparticle-mediated STING agonist delivery systems have been engineered and exploited to address the challenges related to the in vivo STING activation, including liposomes, polymeric micelles, polymersomes, and so on. In this review article, the progresses concerning STING agonists and related delivery systems in recent years will be summarized and discussed.
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Affiliation(s)
- Qinghao Zhou
- CAS Key Laboratory of Soft Matter Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Yu Zhou
- CAS Key Laboratory of Soft Matter Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Taiyuan Li
- CAS Key Laboratory of Soft Matter Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Zhishen Ge
- CAS Key Laboratory of Soft Matter Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui, 230026, China
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36
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Xu J, Mcpartlon M, Li J. Improved protein structure prediction by deep learning irrespective of co-evolution information. NAT MACH INTELL 2021; 3:601-609. [PMID: 34368623 PMCID: PMC8340610 DOI: 10.1038/s42256-021-00348-5] [Citation(s) in RCA: 104] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Predicting the tertiary structure of a protein from its primary sequence has been greatly improved by integrating deep learning and co-evolutionary analysis, as shown in CASP13 and CASP14. We describe our latest study of this idea, analyzing the efficacy of network size and co-evolution data and its performance on both natural and designed proteins. We show that a large ResNet (convolutional residual neural networks) can predict structures of correct folds for 26 out of 32 CASP13 free-modeling (FM) targets and L/5 long-range contacts with precision over 80%. When co-evolution is not used ResNet still can predict structures of correct folds for 18 CASP13 FM targets, greatly exceeding previous methods that do not use co-evolution either. Even with only primary sequence ResNet can predict structures of correct folds for all tested human-designed proteins. In addition, ResNet may fare better for the designed proteins when trained without co-evolution than with co-evolution. These results suggest that ResNet does not simply denoise co-evolution signals, but instead may learn important protein sequence-structure relationship. This has important implications on protein design and engineering especially when co-evolutionary data is unavailable.
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Affiliation(s)
- Jinbo Xu
- Toyota Technological Institute at Chicago
| | - Matthew Mcpartlon
- Department of Computer Science, University of Chicago.,Toyota Technological Institute at Chicago
| | - Jin Li
- Department of Computer Science, University of Chicago.,Toyota Technological Institute at Chicago
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37
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Meinen BA, Bahl CD. Breakthroughs in computational design methods open up new frontiers for de novo protein engineering. Protein Eng Des Sel 2021; 34:6243354. [DOI: 10.1093/protein/gzab007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/16/2021] [Accepted: 03/08/2021] [Indexed: 02/03/2023] Open
Abstract
Abstract
Proteins catalyze the majority of chemical reactions in organisms, and harnessing this power has long been the focus of the protein engineering field. Computational protein design aims to create new proteins and functions in silico, and in doing so, accelerate the process, reduce costs and enable more sophisticated engineering goals to be accomplished. Challenges that very recently seemed impossible are now within reach thanks to several landmark advances in computational protein design methods. Here, we summarize these new methods, with a particular emphasis on de novo protein design advancements occurring within the past 5 years.
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Affiliation(s)
- Ben A Meinen
- Institute for Protein Innovation, Harvard Institutes of Medicine 4 Blackfan Circle, Room 941 Boston, MA 02115-5701 Boston, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Harvard Institutes of Medicine 4 Blackfan Circle, Room 941 Boston, MA 02115-5701 Boston, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
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38
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Weinberg ZY, Hilburger CE, Kim M, Cao L, Khalid M, Elmes S, Diwanji D, Hernandez E, Lopez J, Schaefer K, Smith AM, Zhou F, Kumar GR, Ott M, Baker D, El-Samad H. Sentinel cells enable genetic detection of SARS-CoV-2 Spike protein. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.04.20.440678. [PMID: 33907743 PMCID: PMC8077567 DOI: 10.1101/2021.04.20.440678] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The COVID-19 pandemic has demonstrated the need for exploring different diagnostic and therapeutic modalities to tackle future viral threats. In this vein, we propose the idea of sentinel cells, cellular biosensors capable of detecting viral antigens and responding to them with customizable responses. Using SARS-CoV-2 as a test case, we developed a live cell sensor (SARSNotch) using a de novo-designed protein binder against the SARS-CoV-2 Spike protein. SARSNotch is capable of driving custom genetically-encoded payloads in immortalized cell lines or in primary T lymphocytes in response to purified SARS-CoV-2 Spike or in the presence of Spike-expressing cells. Furthermore, SARSNotch is functional in a cellular system used in directed evolution platforms for development of better binders or therapeutics. In keeping with the rapid dissemination of scientific knowledge that has characterized the incredible scientific response to the ongoing pandemic, we extend an open invitation for others to make use of and improve SARSNotch sentinel cells in the hopes of unlocking the potential of the next generation of smart antiviral therapeutics.
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Affiliation(s)
- Zara Y. Weinberg
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA
| | - Claire E. Hilburger
- The UC Berkeley-UCSF Graduate Program in Bioengineering, UC Berkeley, Berkeley, CA
| | - Matthew Kim
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA
| | - Longxing Cao
- Department of Biochemistry, University of Washington, Seattle, WA
- Institute for Protein Design, University of Washington, Seattle, WA
| | - Mir Khalid
- Gladstone Institute of Virology, San Francisco, CA
| | - Sarah Elmes
- Laboratory for Cell Analysis, University of California, San Francisco, CA
| | - Devan Diwanji
- Cardiovascular Research Institute, University of California San Francisco, CA
- Medical Scientist Training Program, University of California San Francisco, CA
| | - Evelyn Hernandez
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA
| | - Jocelyne Lopez
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA
| | - Kaitlin Schaefer
- Department of Pharmacology, University of California, San Francisco, CA
| | - Amber M. Smith
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA
| | - Fengbo Zhou
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA
| | - QCRG Structural Biology Consortium
- Quantitative Biosciences Institute (QBI) Coronavirus Research Group Structural Biology Consortium, University of California San, Francisco, San Francisco, CA
| | | | - Melanie Ott
- Gladstone Institute of Virology, San Francisco, CA
- Department of Medicine, University of California San, Francisco, San Francisco, CA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA
- Institute for Protein Design, University of Washington, Seattle, WA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA
| | - Hana El-Samad
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA
- Cell Design Initiative, University of California, San Francisco, CA
- Chan-Zuckerberg Biohub, San Francisco, CA
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Bottom-up de novo design of functional proteins with complex structural features. Nat Chem Biol 2021; 17:492-500. [PMID: 33398169 DOI: 10.1038/s41589-020-00699-x] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 10/26/2020] [Indexed: 01/28/2023]
Abstract
De novo protein design has enabled the creation of new protein structures. However, the design of functional proteins has proved challenging, in part due to the difficulty of transplanting structurally complex functional sites to available protein structures. Here, we used a bottom-up approach to build de novo proteins tailored to accommodate structurally complex functional motifs. We applied the bottom-up strategy to successfully design five folds for four distinct binding motifs, including a bifunctionalized protein with two motifs. Crystal structures confirmed the atomic-level accuracy of the computational designs. These de novo proteins were functional as components of biosensors to monitor antibody responses and as orthogonal ligands to modulate synthetic signaling receptors in engineered mammalian cells. Our work demonstrates the potential of bottom-up approaches to accommodate complex structural motifs, which will be essential to endow de novo proteins with elaborate biochemical functions, such as molecular recognition or catalysis.
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40
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Gidley F, Parmeggiani F. Repeat proteins: designing new shapes and functions for solenoid folds. Curr Opin Struct Biol 2021; 68:208-214. [PMID: 33721772 DOI: 10.1016/j.sbi.2021.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/31/2021] [Accepted: 02/01/2021] [Indexed: 10/21/2022]
Abstract
The modular nature of repeat proteins has inspired the design of regular and completely novel sequences and structures. Research in the past years has provided a broad set of design approaches and new repeat proteins that have found applications in molecular recognition, taking advantage of the natural ability of some of these families to bind proteins, peptides and nucleic acids. Here, we provide an overview on the recent trends in design of repeat proteins, particularly solenoid folds, and their applications. By exploiting the intrinsic modularity of repeats, new architectures have been designed that combine different types of repeat, are easily scalable by changing the number of repeats and can be quickly generated by using existing modular building blocks.
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Affiliation(s)
- Frances Gidley
- School of Chemistry, School of Biochemistry, Bristol Biodesign Institute, University of Bristol, United Kingdom
| | - Fabio Parmeggiani
- School of Chemistry, School of Biochemistry, Bristol Biodesign Institute, University of Bristol, United Kingdom.
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41
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Stam MJ, Wood CW. DE-STRESS: a user-friendly web application for the evaluation of protein designs. Protein Eng Des Sel 2021; 34:gzab029. [PMID: 34908138 PMCID: PMC8672653 DOI: 10.1093/protein/gzab029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 10/11/2021] [Accepted: 10/25/2021] [Indexed: 11/16/2022] Open
Abstract
De novo protein design is a rapidly growing field, and there are now many interesting and useful examples of designed proteins in the literature. However, most designs could be classed as failures when characterised in the lab, usually as a result of low expression, misfolding, aggregation or lack of function. This high attrition rate makes protein design unreliable and costly. It is possible that some of these failures could be caught earlier in the design process if it were quick and easy to generate information and a set of high-quality metrics regarding designs, which could be used to make reproducible and data-driven decisions about which designs to characterise experimentally. We present DE-STRESS (DEsigned STRucture Evaluation ServiceS), a web application for evaluating structural models of designed and engineered proteins. DE-STRESS has been designed to be simple, intuitive to use and responsive. It provides a wealth of information regarding designs, as well as tools to help contextualise the results and formally describe the properties that a design requires to be fit for purpose.
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Affiliation(s)
- Michael J Stam
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
| | - Christopher W Wood
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FF, UK
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42
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Structure and function of naturally evolved de novo proteins. Curr Opin Struct Biol 2021; 68:175-183. [PMID: 33567396 DOI: 10.1016/j.sbi.2020.11.010] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/16/2020] [Accepted: 11/27/2020] [Indexed: 01/05/2023]
Abstract
Comparative evolutionary genomics has revealed that novel protein coding genes can emerge randomly from non-coding DNA. While most of the myriad of transcripts which continuously emerge vanish rapidly, some attain regulatory regions, become translated and survive. More surprisingly, sequence properties of de novo proteins are almost indistinguishable from randomly obtained sequences, yet de novo proteins may gain functions and integrate into eukaryotic cellular networks quite easily. We here discuss current knowledge on de novo proteins, their structures, functions and evolution. Since the existence of de novo proteins seems at odds with decade-long attempts to construct proteins with novel structures and functions from scratch, we suggest that a better understanding of de novo protein evolution may fuel new strategies for protein design.
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Abstract
While native proteins cover diverse structural spaces and achieve various biological events, not many of them can directly serve human needs. One reason is that the native proteins usually contain idiosyncrasies evolved for their native functions but disfavoring engineering requirements. To overcome this issue, one strategy is to create de novo proteins which are designed to possess improved stability, high environmental tolerance, and enhanced engineering potential. Compared to other protein engineering strategies, in silico design of de novo proteins has significantly expanded the protein structural and sequence spaces, reduced wet lab workload, and incorporated engineered features in a guided and efficient manner. In the Baker laboratory we have been applying a design pipeline that uses the blueprint builder to design different folds of de novo proteins, and have successfully obtained libraries of de novo proteins with improved stability and engineering potential. In this article, we will use the design of de novo β-barrel proteins as an example to describe the principles and basic procedures of the blueprint builder-based design pipeline. © 2020 Wiley Periodicals LLC. Basic Protocol 1: The construction of blueprints Alternate Protocol: Build blueprints based on existing protein .pdb files Basic Protocol 2: De novo protein design pipeline using the blueprint builder.
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Affiliation(s)
- Linna An
- Institute for Protein Design, University of Washington, Seattle, Washington
| | - Gyu Rie Lee
- Institute for Protein Design, University of Washington, Seattle, Washington
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44
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Pan X, Kortemme T. Recent advances in de novo protein design: Principles, methods, and applications. J Biol Chem 2021; 296:100558. [PMID: 33744284 PMCID: PMC8065224 DOI: 10.1016/j.jbc.2021.100558] [Citation(s) in RCA: 90] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.
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Affiliation(s)
- Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA.
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA; Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California, USA.
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