51
|
Durham J, Zhang J, Humphreys IR, Pei J, Cong Q. Recent advances in predicting and modeling protein-protein interactions. Trends Biochem Sci 2023; 48:527-538. [PMID: 37061423 DOI: 10.1016/j.tibs.2023.03.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 03/03/2023] [Accepted: 03/17/2023] [Indexed: 04/17/2023]
Abstract
Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.
Collapse
Affiliation(s)
- Jesse Durham
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jing Zhang
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ian R Humphreys
- Department of Biochemistry, University of Washington, Seattle, WA, USA; Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jimin Pei
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qian Cong
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| |
Collapse
|
52
|
Zhang W, Liu M, Wang Y, Wang X, Wang R, Li S, Yu L, Zhang F, Wang C. β-Sheet Assembly Translates Conservative Single-Site Mutation into a Perturbation in Macroscopic Structure. NANO LETTERS 2023; 23:2370-2378. [PMID: 36897606 DOI: 10.1021/acs.nanolett.3c00311] [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: 06/18/2023]
Abstract
Transferring structural information from amino acid sequence to macroscale assembly is a challenging approach for designing protein quaternary structure. However, the pathway by which the slight variations in sequence result in a global perturbation effect on the assembled structure is unknown. Herein, we design two synthetic peptides, QNL-His and QNL-Arg, with one amino acid substitution and use scanning tunneling microscopy (STM) to image individual peptides in the assembled state. The submolecular resolution of STM enables us to determine the folding structure and β-sheet supramolecular organization of peptides. QNL-His and QNL-Arg differ in their β-strand length distribution in pleated β-sheet association. These structural variations lead to distinguishable outcomes in their β-sheet assembled fibrils and phase transitions. The comparison of QNL-His versus QNL-Arg structures and macroscopic properties unveils the role of assembly to amplify the structural variations associated with a single-site mutation from a single-molecule scale to a macroscopic scale.
Collapse
Affiliation(s)
- Wenbo Zhang
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
| | - Mingwei Liu
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
| | - Yang Wang
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
| | - Xin Wang
- Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14850, United States
| | - Ruonan Wang
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
| | - Shuyuan Li
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
| | - Lanlan Yu
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
| | - Feiyi Zhang
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
- Institute for Advanced Materials, Jiangsu University, Zhenjiang, Jiangsu 212013, P. R. China
| | - Chenxuan Wang
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
| |
Collapse
|
53
|
Wang JY(J, Khmelinskaia A, Sheffler W, Miranda MC, Antanasijevic A, Borst AJ, Torres SV, Shu C, Hsia Y, Nattermann U, Ellis D, Walkey C, Ahlrichs M, Chan S, Kang A, Nguyen H, Sydeman C, Sankaran B, Wu M, Bera AK, Carter L, Fiala B, Murphy M, Baker D, Ward AB, King NP. Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains. Proc Natl Acad Sci U S A 2023; 120:e2214556120. [PMID: 36888664 PMCID: PMC10089191 DOI: 10.1073/pnas.2214556120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 02/03/2023] [Indexed: 03/09/2023] Open
Abstract
Computationally designed protein nanoparticles have recently emerged as a promising platform for the development of new vaccines and biologics. For many applications, secretion of designed nanoparticles from eukaryotic cells would be advantageous, but in practice, they often secrete poorly. Here we show that designed hydrophobic interfaces that drive nanoparticle assembly are often predicted to form cryptic transmembrane domains, suggesting that interaction with the membrane insertion machinery could limit efficient secretion. We develop a general computational protocol, the Degreaser, to design away cryptic transmembrane domains without sacrificing protein stability. The retroactive application of the Degreaser to previously designed nanoparticle components and nanoparticles considerably improves secretion, and modular integration of the Degreaser into design pipelines results in new nanoparticles that secrete as robustly as naturally occurring protein assemblies. Both the Degreaser protocol and the nanoparticles we describe may be broadly useful in biotechnological applications.
Collapse
Affiliation(s)
- Jing Yang (John) Wang
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
- Graduate Program in Molecular and Cellular Biology, University of Washington, Seattle, WA98195
| | - Alena Khmelinskaia
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
- Transdisciplinary Research Area “Building Blocks of Matter and Fundamental Interactions”, University of Bonn, 53113Bonn, Germany
- Life and Medical Sciences Institute, University of Bonn, 53121Bonn, Germany
| | - William Sheffler
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Marcos C. Miranda
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Aleksandar Antanasijevic
- Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
- Scripps Consortium for HIV/AIDS Vaccine Development, The Scripps Research Institute, La Jolla, CA92037
| | - Andrew J. Borst
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Susana V. Torres
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Chelsea Shu
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Yang Hsia
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Una Nattermann
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA98195
| | - Daniel Ellis
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
- Graduate Program in Molecular and Cellular Biology, University of Washington, Seattle, WA98195
| | - Carl Walkey
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Maggie Ahlrichs
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Sidney Chan
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Hannah Nguyen
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Claire Sydeman
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Banumathi Sankaran
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley Laboratory, Berkeley, CA94720
- Berkeley Center for Structural Biology, Lawrence Berkeley Laboratory, Berkeley, CA94720
| | - Mengyu Wu
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Asim K. Bera
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Lauren Carter
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Brooke Fiala
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Michael Murphy
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Andrew B. Ward
- Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
- Scripps Consortium for HIV/AIDS Vaccine Development, The Scripps Research Institute, La Jolla, CA92037
| | - Neil P. King
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| |
Collapse
|
54
|
Kim DE, Jensen DR, Feldman D, Tischer D, Saleem A, Chow CM, Li X, Carter L, Milles L, Nguyen H, Kang A, Bera AK, Peterson FC, Volkman BF, Ovchinnikov S, Baker D. De novo design of small beta barrel proteins. Proc Natl Acad Sci U S A 2023; 120:e2207974120. [PMID: 36897987 PMCID: PMC10089152 DOI: 10.1073/pnas.2207974120] [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: 05/10/2022] [Accepted: 01/27/2023] [Indexed: 03/12/2023] Open
Abstract
Small beta barrel proteins are attractive targets for computational design because of their considerable functional diversity despite their very small size (<70 amino acids). However, there are considerable challenges to designing such structures, and there has been little success thus far. Because of the small size, the hydrophobic core stabilizing the fold is necessarily very small, and the conformational strain of barrel closure can oppose folding; also intermolecular aggregation through free beta strand edges can compete with proper monomer folding. Here, we explore the de novo design of small beta barrel topologies using both Rosetta energy-based methods and deep learning approaches to design four small beta barrel folds: Src homology 3 (SH3) and oligonucleotide/oligosaccharide-binding (OB) topologies found in nature and five and six up-and-down-stranded barrels rarely if ever seen in nature. Both approaches yielded successful designs with high thermal stability and experimentally determined structures with less than 2.4 Å rmsd from the designed models. Using deep learning for backbone generation and Rosetta for sequence design yielded higher design success rates and increased structural diversity than Rosetta alone. The ability to design a large and structurally diverse set of small beta barrel proteins greatly increases the protein shape space available for designing binders to protein targets of interest.
Collapse
Affiliation(s)
- David E. Kim
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
- HHMI, University of Washington, Seattle, WA98195
| | - Davin R. Jensen
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI53226
| | - David Feldman
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Doug Tischer
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Ayesha Saleem
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Cameron M. Chow
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Xinting Li
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Lauren Carter
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Lukas Milles
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Hannah Nguyen
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Asim K. Bera
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Francis C. Peterson
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI53226
| | - Brian F. Volkman
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI53226
| | - Sergey Ovchinnikov
- Division of Science, Faculty of Arts and Sciences, Harvard University, Cambridge, MA02138
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA02138
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
- HHMI, University of Washington, Seattle, WA98195
| |
Collapse
|
55
|
Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics. Int J Mol Sci 2023; 24:ijms24054401. [PMID: 36901832 PMCID: PMC10003049 DOI: 10.3390/ijms24054401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.
Collapse
|
56
|
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.
Collapse
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
| |
Collapse
|
57
|
Menke FS, Mazzier D, Wicher B, Allmendinger L, Kauffmann B, Maurizot V, Huc I. Molecular torsion springs: alteration of helix curvature in frustrated tertiary folds. Org Biomol Chem 2023; 21:1275-1283. [PMID: 36645374 DOI: 10.1039/d2ob02109a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The first abiotic foldamer tertiary structures have been recently reported in the form of aromatic helix-turn-helix motifs based on oligo-quinolinecarboxamides held together by intramolecular hydrogen bonds. Tertiary folds were predicted by computational modelling of the hydrogen-bonding interfaces between helices and later verified by X-ray crystallography. However, the prognosis of how the conformational preference inherent to each helix influences the tertiary structure warranted further investigation. Several new helix-turn-helix sequences were synthesised in which some hydrogen bonds have been removed. Contrary to expectations, this change did not strongly destabilise the tertiary folds. On closer inspection, a new crystal structure revealed that helices adopt their natural curvature when some hydrogen bonds are missing and undergo some spring torsion upon forming the said hydrogen bonds, thus potentially giving rise to a conformational frustration. This phenomenon sheds light on the aggregation behaviour of the helices when they are not linked by a turn unit.
Collapse
Affiliation(s)
- Friedericke S Menke
- Department of Pharmacy, Ludwig-Maximilians-University, Butenandstraße 5-13, 81377 Munich, Germany.
| | - Daniela Mazzier
- Department of Pharmacy, Ludwig-Maximilians-University, Butenandstraße 5-13, 81377 Munich, Germany.
| | - Barbara Wicher
- Department of Chemical Technology of Drugs, Poznan University of Medical Sciences, Grunwaldzka 6, 60-780 Poznan, Poland
| | - Lars Allmendinger
- Department of Pharmacy, Ludwig-Maximilians-University, Butenandstraße 5-13, 81377 Munich, Germany.
| | - Brice Kauffmann
- Institut Européen de Chimie et Biologie (UMS3011/US001), CNRS, Inserm, Université de Bordeaux, 2 rue Robert Escarpit, F-33600 Pessac, France
| | - Victor Maurizot
- CBMN (UMR 5248), Univ. Bordeaux, CNRS, Bordeaux INP, 2 rue Robert Escarpit, 33600 Pessac, France
| | - Ivan Huc
- Department of Pharmacy, Ludwig-Maximilians-University, Butenandstraße 5-13, 81377 Munich, Germany.
| |
Collapse
|
58
|
Yeh AHW, Norn C, Kipnis Y, Tischer D, Pellock SJ, Evans D, Ma P, Lee GR, Zhang JZ, Anishchenko I, Coventry B, Cao L, Dauparas J, Halabiya S, DeWitt M, Carter L, Houk KN, Baker D. De novo design of luciferases using deep learning. Nature 2023; 614:774-780. [PMID: 36813896 PMCID: PMC9946828 DOI: 10.1038/s41586-023-05696-3] [Citation(s) in RCA: 103] [Impact Index Per Article: 103.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 01/03/2023] [Indexed: 02/24/2023]
Abstract
De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds1,2, but has been limited by a lack of suitable protein structures and the complexity of native protein sequence-structure relationships. Here we describe a deep-learning-based 'family-wide hallucination' approach that generates large numbers of idealized protein structures containing diverse pocket shapes and designed sequences that encode them. We use these scaffolds to design artificial luciferases that selectively catalyse the oxidative chemiluminescence of the synthetic luciferin substrates diphenylterazine3 and 2-deoxycoelenterazine. The designed active sites position an arginine guanidinium group adjacent to an anion that develops during the reaction in a binding pocket with high shape complementarity. For both luciferin substrates, we obtain designed luciferases with high selectivity; the most active of these is a small (13.9 kDa) and thermostable (with a melting temperature higher than 95 °C) enzyme that has a catalytic efficiency on diphenylterazine (kcat/Km = 106 M-1 s-1) comparable to that of native luciferases, but a much higher substrate specificity. The creation of highly active and specific biocatalysts from scratch with broad applications in biomedicine is a key milestone for computational enzyme design, and our approach should enable generation of a wide range of luciferases and other enzymes.
Collapse
Affiliation(s)
- Andy Hsien-Wei Yeh
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
| | - Christoffer Norn
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Yakov Kipnis
- 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
| | - Doug Tischer
- 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
| | - Declan Evans
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Pengchen Ma
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USA
- School of Chemistry, Xi'an Key Laboratory of Sustainable Energy Materials Chemistry, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi'an Jiaotong University, Xi'an, China
| | - Gyu Rie Lee
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jason Z Zhang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Longxing Cao
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Samer Halabiya
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Michelle DeWitt
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lauren Carter
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - K N Houk
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
| |
Collapse
|
59
|
Niitsu A, Sugita Y. Towards de novo design of transmembrane α-helical assemblies using structural modelling and molecular dynamics simulation. Phys Chem Chem Phys 2023; 25:3595-3606. [PMID: 36647771 DOI: 10.1039/d2cp03972a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Computational de novo protein design involves iterative processes consisting of amino acid sequence design, structural modelling and scoring, and design validation by synthesis and experimental characterisation. Recent advances in protein structure prediction and modelling methods have enabled the highly efficient and accurate design of water-soluble proteins. However, the design of membrane proteins remains a major challenge. To advance membrane protein design, considering the higher complexity of membrane protein folding, stability, and dynamic interactions between water, ions, lipids, and proteins is an important task. For introducing explicit solvents and membranes to these design methods, all-atom molecular dynamics (MD) simulations of designed proteins provide useful information that cannot be obtained experimentally. In this review, we first describe two major approaches to designing transmembrane α-helical assemblies, consensus and de novo design. We further illustrate recent MD studies of membrane protein folding related to protein design, as well as advanced treatments in molecular models and conformational sampling techniques in the simulations. Finally, we discuss the possibility to introduce MD simulations after the existing static modelling and screening of design decoys as an additional step for refinement of the design, which considers membrane protein folding dynamics and interactions with explicit membranes.
Collapse
Affiliation(s)
- Ai Niitsu
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.
| | - Yuji Sugita
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan. .,Computational Biophysics Research Team, RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| |
Collapse
|
60
|
De novo protein fold design through sequence-independent fragment assembly simulations. Proc Natl Acad Sci U S A 2023; 120:e2208275120. [PMID: 36656852 PMCID: PMC9942881 DOI: 10.1073/pnas.2208275120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.
Collapse
|
61
|
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: 4] [Impact Index Per Article: 4.0] [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.
Collapse
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
| |
Collapse
|
62
|
Gerben S, Borst AJ, Hicks DR, Moczygemba I, Feldman D, Coventry B, Yang W, Bera AK, Miranda M, Kang A, Nguyen H, Baker D. Design of Diverse Asymmetric Pockets in De Novo Homo-oligomeric Proteins. Biochemistry 2023; 62:358-368. [PMID: 36627259 PMCID: PMC9850923 DOI: 10.1021/acs.biochem.2c00497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 11/28/2022] [Indexed: 01/12/2023]
Abstract
A challenge for design of protein-small-molecule recognition is that incorporation of cavities with size, shape, and composition suitable for specific recognition can considerably destabilize protein monomers. This challenge can be overcome through binding pockets formed at homo-oligomeric interfaces between folded monomers. Interfaces surrounding the central homo-oligomer symmetry axes necessarily have the same symmetry and so may not be well suited to binding asymmetric molecules. To enable general recognition of arbitrary asymmetric substrates and small molecules, we developed an approach to designing asymmetric interfaces at off-axis sites on homo-oligomers, analogous to those found in native homo-oligomeric proteins such as glutamine synthetase. We symmetrically dock curved helical repeat proteins such that they form pockets at the asymmetric interface of the oligomer with sizes ranging from several angstroms, appropriate for binding a single ion, to up to more than 20 Å across. Of the 133 proteins tested, 84 had soluble expression in E. coli, 47 had correct oligomeric states in solution, 35 had small-angle X-ray scattering (SAXS) data largely consistent with design models, and 8 had negative-stain electron microscopy (nsEM) 2D class averages showing the structures coming together as designed. Both an X-ray crystal structure and a cryogenic electron microscopy (cryoEM) structure are close to the computational design models. The nature of these proteins as homo-oligomers allows them to be readily built into higher-order structures such as nanocages, and the asymmetric pockets of these structures open rich possibilities for small-molecule binder design free from the constraints associated with monomer destabilization.
Collapse
Affiliation(s)
- Stacey
R Gerben
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - Andrew J Borst
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - Derrick R Hicks
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - Isabelle Moczygemba
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - David Feldman
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - Brian Coventry
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - Wei Yang
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - Asim K. Bera
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - Marcos Miranda
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - Alex Kang
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - Hannah Nguyen
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - David Baker
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
- Howard
Hughes Medical Institute, University of
Washington, Seattle, Washington 98195, United States
| |
Collapse
|
63
|
Van Thillo T, Van Deuren V, Dedecker P. Smart genetically-encoded biosensors for the chemical monitoring of living systems. Chem Commun (Camb) 2023; 59:520-534. [PMID: 36519509 DOI: 10.1039/d2cc05363b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Genetically-encoded biosensors provide the all-optical and non-invasive visualization of dynamic biochemical events within living systems, which has allowed the discovery of profound new insights. Twenty-five years of biosensor development has steadily improved their performance and has provided us with an ever increasing biosensor repertoire. In this feature article, we present recent advances made in biosensor development and provide a perspective on the future direction of the field.
Collapse
Affiliation(s)
- Toon Van Thillo
- Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Leuven, Belgium.
| | - Vincent Van Deuren
- Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Leuven, Belgium.
| | - Peter Dedecker
- Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Leuven, Belgium.
| |
Collapse
|
64
|
Mufassirin MMM, Newton MAH, Sattar A. Artificial intelligence for template-free protein structure prediction: a comprehensive review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10350-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
65
|
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
| |
Collapse
|
66
|
Said M, Kang CS, Wang S, Sheffler W, Salveson PJ, Bera AK, Kang A, Nguyen H, Ballard R, Li X, Bai H, Stewart L, Levine P, Baker D. Exploration of Structured Symmetric Cyclic Peptides as Ligands for Metal-Organic Frameworks. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2022; 34:9736-9744. [PMID: 36397834 PMCID: PMC9648172 DOI: 10.1021/acs.chemmater.2c02597] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/20/2022] [Indexed: 06/16/2023]
Abstract
Despite remarkable advances in the assembly of highly structured coordination polymers and metal-organic frameworks, the rational design of such materials using more conformationally flexible organic ligands such as peptides remains challenging. In an effort to make the design of such materials fully programmable, we first developed a computational design method for generating metal-mediated 3D frameworks using rigid and symmetric peptide macrocycles with metal-coordinating sidechains. We solved the structures of six crystalline networks involving conformationally constrained 6 to 12 residue cyclic peptides with C2, C3, and S2 internal symmetry and three different types of metals (Zn2+, Co2+, or Cu2+) by single-crystal X-ray diffraction, which reveals how the peptide sequences, backbone symmetries, and metal coordination preferences drive the assembly of the resulting structures. In contrast to smaller ligands, these peptides associate through peptide-peptide interactions without full coordination of the metals, contrary to one of the assumptions underlying our computational design method. The cyclic peptides are the largest peptidic ligands reported to form crystalline coordination polymers with transition metals to date, and while more work is required to develop methods for fully programming their crystal structures, the combination of high chemical diversity with synthetic accessibility makes them attractive building blocks for engineering a broader set of new crystalline materials for use in applications such as sensing, asymmetric catalysis, and chiral separation.
Collapse
Affiliation(s)
- Meerit
Y. Said
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
| | - Christine S. Kang
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
| | - Shunzhi Wang
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
| | - William Sheffler
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
| | - Patrick J. Salveson
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
| | - Asim K. Bera
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
| | - Alex Kang
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
| | - Hannah Nguyen
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
| | - Ryanne Ballard
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
| | - Xinting Li
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
| | - Hua Bai
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
| | - Lance Stewart
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
| | - Paul Levine
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
| | - David Baker
- Institute
for Protein Design, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Department
of Biochemistry, University of Washington, 4000 15th Avenue NE, Seattle, Washington 98195, United States
- Howard
Hughes Medical Institute, University of
Washington, Seattle, Washington 98195, United States
| |
Collapse
|
67
|
Kipnis Y, Chaib AO, Vorobieva AA, Cai G, Reggiano G, Basanta B, Kumar E, Mittl PR, Hilvert D, Baker D. Design and optimization of enzymatic activity in a de novo β-barrel scaffold. Protein Sci 2022; 31:e4405. [PMID: 36305767 PMCID: PMC9601869 DOI: 10.1002/pro.4405] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/11/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022]
Abstract
While native scaffolds offer a large diversity of shapes and topologies for enzyme engineering, their often unpredictable behavior in response to sequence modification makes de novo generated scaffolds an exciting alternative. Here we explore the customization of the backbone and sequence of a de novo designed eight stranded β‐barrel protein to create catalysts for a retro‐aldolase model reaction. We show that active and specific catalysts can be designed in this fold and use directed evolution to further optimize activity and stereoselectivity. Our results support previous suggestions that different folds have different inherent amenability to evolution and this property could account, in part, for the distribution of natural enzymes among different folds.
Collapse
Affiliation(s)
- Yakov Kipnis
- Department of BiochemistryUniversity of WashingtonSeattleUSA,Institute for Protein DesignUniversity of WashingtonSeattleUSA,Howard Hughes Medical InstituteUniversity of WashingtonSeattleUSA
| | | | - Anastassia A. Vorobieva
- Department of BiochemistryUniversity of WashingtonSeattleUSA,Institute for Protein DesignUniversity of WashingtonSeattleUSA,Howard Hughes Medical InstituteUniversity of WashingtonSeattleUSA,VIB‐VUB Center for Structural BiologyVlaams Instituut voor BiotechnologieBrusselsBelgium,Structural Biology BrusselsVrije Universiteit BrusselBrusselsBelgium
| | - Guangyang Cai
- Department of BiochemistryUniversity of WashingtonSeattleUSA,Institute for Protein DesignUniversity of WashingtonSeattleUSA
| | - Gabriella Reggiano
- Department of BiochemistryUniversity of WashingtonSeattleUSA,Institute for Protein DesignUniversity of WashingtonSeattleUSA
| | - Benjamin Basanta
- Department of BiochemistryUniversity of WashingtonSeattleUSA,Institute for Protein DesignUniversity of WashingtonSeattleUSA
| | - Eshan Kumar
- Department of BiochemistryUniversity of WashingtonSeattleUSA,Institute for Protein DesignUniversity of WashingtonSeattleUSA
| | - Peer R.E. Mittl
- Department of BiochemistryUniversity of ZurichZurichSwitzerland
| | - Donald Hilvert
- Laboratory of Organic ChemistryETH ZurichZurichSwitzerland
| | - David Baker
- Department of BiochemistryUniversity of WashingtonSeattleUSA,Institute for Protein DesignUniversity of WashingtonSeattleUSA,Howard Hughes Medical InstituteUniversity of WashingtonSeattleUSA
| |
Collapse
|
68
|
Biological Role of the 3β-Corner Structural Motif in Proteins. Processes (Basel) 2022. [DOI: 10.3390/pr10112159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023] Open
Abstract
In this study, we analyze the occurrence of the unique structural motif, the 3β-corner, belonging to the Structural Classification of Proteins (SCOP) folds, in proteins of various origins. We further assess the structural and functional role of this motif as well as the clustering of the biological functions of proteins in which it occurs. It has been shown previously that the 3β-corner occurs with different probabilities in all beta proteins, alpha and beta proteins (α + β and α/β), and alpha classes occur most often in the composition of β-proteins. The 3β-corner is often found as a building block in protein structures, such as β-barrels, -sandwiches, and -sheets/-layers.
Collapse
|
69
|
Qiao D, Chen Y, Tan H, Zhou R, Feng J. De novo design of transmembrane nanopores. Sci China Chem 2022. [DOI: 10.1007/s11426-022-1354-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
70
|
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.
Collapse
|
71
|
Chu AE, Fernandez D, Liu J, Eguchi RR, Huang PS. De Novo Design of a Highly Stable Ovoid TIM Barrel: Unlocking Pocket Shape towards Functional Design. BIODESIGN RESEARCH 2022; 2022:9842315. [PMID: 37850141 PMCID: PMC10521652 DOI: 10.34133/2022/9842315] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/26/2022] [Indexed: 10/19/2023] Open
Abstract
The ability to finely control the structure of protein folds is an important prerequisite to functional protein design. The TIM barrel fold is an important target for these efforts as it is highly enriched for diverse functions in nature. Although a TIM barrel protein has been designed de novo, the ability to finely alter the curvature of the central beta barrel and the overall architecture of the fold remains elusive, limiting its utility for functional design. Here, we report the de novo design of a TIM barrel with ovoid (twofold) symmetry, drawing inspiration from natural beta and TIM barrels with ovoid curvature. We use an autoregressive backbone sampling strategy to implement our hypothesis for elongated barrel curvature, followed by an iterative enrichment sequence design protocol to obtain sequences which yield a high proportion of successfully folding designs. Designed sequences are highly stable and fold to the designed barrel curvature as determined by a 2.1 Å resolution crystal structure. The designs show robustness to drastic mutations, retaining high melting temperatures even when multiple charged residues are buried in the hydrophobic core or when the hydrophobic core is ablated to alanine. As a scaffold with a greater capacity for hosting diverse hydrogen bonding networks and installation of binding pockets or active sites, the ovoid TIM barrel represents a major step towards the de novo design of functional TIM barrels.
Collapse
Affiliation(s)
- Alexander E. Chu
- Biophysics Program, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Daniel Fernandez
- Program in Chemistry, Engineering, And Medicine for Human Health (ChEM-H), Stanford University, Stanford, CA, USA
- Stanford ChEM-H, Macromolecular Structure Knowledge Center, Stanford University, Stanford, CA, USA
| | - Jingjia Liu
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Raphael R. Eguchi
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Stanford ChEM-H, Macromolecular Structure Knowledge Center, Stanford University, Stanford, CA, USA
- Department of Biochemistry, Stanford University, Stanford, CA, USA
| | - Po-Ssu Huang
- Biophysics Program, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Stanford ChEM-H, Macromolecular Structure Knowledge Center, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
| |
Collapse
|
72
|
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.
Collapse
|
73
|
Chidyausiku TM, Mendes SR, Klima JC, Nadal M, Eckhard U, Roel-Touris J, Houliston S, Guevara T, Haddox HK, Moyer A, Arrowsmith CH, Gomis-Rüth FX, Baker D, Marcos E. De novo design of immunoglobulin-like domains. Nat Commun 2022; 13:5661. [PMID: 36192397 PMCID: PMC9530121 DOI: 10.1038/s41467-022-33004-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/17/2022] [Indexed: 11/21/2022] Open
Abstract
Antibodies, and antibody derivatives such as nanobodies, contain immunoglobulin-like (Ig) β-sandwich scaffolds which anchor the hypervariable antigen-binding loops and constitute the largest growing class of drugs. Current engineering strategies for this class of compounds rely on naturally existing Ig frameworks, which can be hard to modify and have limitations in manufacturability, designability and range of action. Here, we develop design rules for the central feature of the Ig fold architecture—the non-local cross-β structure connecting the two β-sheets—and use these to design highly stable Ig domains de novo, confirm their structures through X-ray crystallography, and show they can correctly scaffold functional loops. Our approach opens the door to the design of antibody-like scaffolds with tailored structures and superior biophysical properties. The immunoglobulin domain framework of antibodies has been a long standing design challenge. Here, the authors describe design rules for tailoring these domains and show they can be accurately designed, de novo, with high stability and the ability to scaffold functional loops.
Collapse
Affiliation(s)
- Tamuka M Chidyausiku
- 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.,Novartis Institutes for BioMedical Research Inc., San Diego, CA, 92121, USA
| | - Soraia R Mendes
- Proteolysis Laboratory, Department of Structural and Molecular Biology, Molecular Biology Institute of Barcelona (IBMB-CSIC), Baldiri Reixac 15, 08028, Barcelona, Spain
| | - Jason C Klima
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA.,Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA.,Encodia, Inc., San Diego, CA, 92121, USA
| | - Marta Nadal
- Protein Design and Modeling Lab, Department of Structural and Molecular Biology, Molecular Biology Institute of Barcelona (IBMB-CSIC), Baldiri Reixac 15, 08028, Barcelona, Spain
| | - Ulrich Eckhard
- Proteolysis Laboratory, Department of Structural and Molecular Biology, Molecular Biology Institute of Barcelona (IBMB-CSIC), Baldiri Reixac 15, 08028, Barcelona, Spain
| | - Jorge Roel-Touris
- Protein Design and Modeling Lab, Department of Structural and Molecular Biology, Molecular Biology Institute of Barcelona (IBMB-CSIC), Baldiri Reixac 15, 08028, Barcelona, Spain
| | - Scott Houliston
- Structural Genomics Consortium, University of Toronto, Toronto, ON, M5G 1L7, Canada.,Princess Margaret Cancer Centre and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada
| | - Tibisay Guevara
- Proteolysis Laboratory, Department of Structural and Molecular Biology, Molecular Biology Institute of Barcelona (IBMB-CSIC), Baldiri Reixac 15, 08028, Barcelona, Spain
| | - Hugh K Haddox
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Adam Moyer
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Cheryl H Arrowsmith
- Structural Genomics Consortium, University of Toronto, Toronto, ON, M5G 1L7, Canada.,Princess Margaret Cancer Centre and Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada
| | - F Xavier Gomis-Rüth
- Proteolysis Laboratory, Department of Structural and Molecular Biology, Molecular Biology Institute of Barcelona (IBMB-CSIC), Baldiri Reixac 15, 08028, Barcelona, Spain.
| | - 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.
| | - Enrique Marcos
- Protein Design and Modeling Lab, Department of Structural and Molecular Biology, Molecular Biology Institute of Barcelona (IBMB-CSIC), Baldiri Reixac 15, 08028, Barcelona, Spain.
| |
Collapse
|
74
|
Sokolov AI, Baleeva NS, Baranov MS. Halogen-Containing 4-Hydroxybenzylidene-Rhodanines as Fast Protein Fluorogens. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2022. [DOI: 10.1134/s1068162022050247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
75
|
Qing R, Hao S, Smorodina E, Jin D, Zalevsky A, Zhang S. Protein Design: From the Aspect of Water Solubility and Stability. Chem Rev 2022; 122:14085-14179. [PMID: 35921495 PMCID: PMC9523718 DOI: 10.1021/acs.chemrev.1c00757] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Indexed: 12/13/2022]
Abstract
Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.
Collapse
Affiliation(s)
- Rui Qing
- State
Key Laboratory of Microbial Metabolism, School of Life Sciences and
Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- The
David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Shilei Hao
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- Key
Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China
| | - Eva Smorodina
- Department
of Immunology, University of Oslo and Oslo
University Hospital, Oslo 0424, Norway
| | - David Jin
- Avalon GloboCare
Corp., Freehold, New Jersey 07728, United States
| | - Arthur Zalevsky
- Laboratory
of Bioinformatics Approaches in Combinatorial Chemistry and Biology, Shemyakin−Ovchinnikov Institute of Bioorganic
Chemistry RAS, Moscow 117997, Russia
| | - Shuguang Zhang
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| |
Collapse
|
76
|
Wang G, Bai Y, Cui J, Zong Z, Gao Y, Zheng Z. Computer-Aided Drug Design Boosts RAS Inhibitor Discovery. Molecules 2022; 27:molecules27175710. [PMID: 36080477 PMCID: PMC9457765 DOI: 10.3390/molecules27175710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/13/2022] [Accepted: 08/26/2022] [Indexed: 11/16/2022] Open
Abstract
The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.
Collapse
Affiliation(s)
- Ge Wang
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Yuhao Bai
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Jiarui Cui
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Zirui Zong
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Yuan Gao
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Zhen Zheng
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Correspondence:
| |
Collapse
|
77
|
Listov D, Lipsh‐Sokolik R, Rosset S, Yang C, Correia BE, Fleishman SJ. Assessing and enhancing foldability in designed proteins. Protein Sci 2022; 31:e4400. [PMID: 36040259 PMCID: PMC9375437 DOI: 10.1002/pro.4400] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/09/2022] [Accepted: 07/11/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Dina Listov
- Department of Biomolecular Sciences Weizmann Institute of Science Rehovot Israel
| | | | - Stéphane Rosset
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland
| | - Che Yang
- 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
| | | |
Collapse
|
78
|
Chauhan VM, Pantazes RJ. MutDock: A computational docking approach for fixed-backbone protein scaffold design. Front Mol Biosci 2022; 9:933400. [PMID: 36106019 PMCID: PMC9465448 DOI: 10.3389/fmolb.2022.933400] [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: 05/20/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Despite the successes of antibodies as therapeutic binding proteins, they still face production and design challenges. Alternative binding scaffolds of smaller size have been developed to overcome these issues. A subset of these alternative scaffolds recognizes target molecules through mutations to a set of surface resides, which does not alter their backbone structures. While the computational design of antibodies for target epitopes has been explored in depth, the same has not been done for alternative scaffolds. The commonly used dock-and-mutate approach for binding proteins, including antibodies, is limited because it uses a constant sequence and structure representation of the scaffold. Docking fixed-backbone scaffolds with a varied group of surface amino acids increases the chances of identifying superior starting poses that can be improved with subsequent mutations. In this work, we have developed MutDock, a novel computational approach that simultaneously docks and mutates fixed backbone scaffolds for binding a target epitope by identifying a minimum number of hydrogen bonds. The approach is broadly divided into two steps. The first step uses pairwise distance alignment of hydrogen bond-forming areas of scaffold residues and compatible epitope atoms. This step considers both native and mutated rotamers of scaffold residues. The second step mutates clashing variable interface residues and thermodynamically unfavorable residues to create additional strong interactions. MutDock was used to dock two scaffolds, namely, Affibodies and DARPins, with ten randomly selected antigens. The energies of the docked poses were minimized and binding energies were compared with docked poses from ZDOCK and HADDOCK. The top MutDock poses consisted of higher and comparable binding energies than the top ZDOCK and HADDOCK poses, respectively. This work contributes to the discovery of novel binders based on smaller-sized, fixed-backbone protein scaffolds.
Collapse
|
79
|
Magi Meconi G, Sasselli IR, Bianco V, Onuchic JN, Coluzza I. Key aspects of the past 30 years of protein design. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2022; 85:086601. [PMID: 35704983 DOI: 10.1088/1361-6633/ac78ef] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.
Collapse
Affiliation(s)
- Giulia Magi Meconi
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | - Ivan R Sasselli
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | | | - Jose N Onuchic
- Center for Theoretical Biological Physics, Department of Physics & Astronomy, Department of Chemistry, Department of Biosciences, Rice University, Houston, TX 77251, United States of America
| | - Ivan Coluzza
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, Bld. Martina Casiano, UPV/EHU Science Park, Barrio Sarriena s/n, 48940 Leioa, Spain
- Basque Foundation for Science, Ikerbasque, 48009, Bilbao, Spain
| |
Collapse
|
80
|
Tan X, Lv C, Chen H. Advances of nanopore-based sensing techniques for contaminants evaluation of food and agricultural products. Crit Rev Food Sci Nutr 2022; 63:10866-10879. [PMID: 35687354 DOI: 10.1080/10408398.2022.2085238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Food safety assurance systems are becoming more stringent in response to the growing food safety problems. Rapid, sensitive, and reliable detection technology is a prerequisite for the establishment of food safety assurance systems. Nanopore technology has been taken as one of the emerging technology capable of dealing with the detection of harmful contaminants as efficiently as possible due to the advantage of label-free, high-throughput, amplification-free, and rapid detection features. Start with the history of nanopore techniques, this review introduced the underlying knowledge of detection mechanism of nanopore-based sensing techniques. Meanwhile, sensing interfaces for the construction of nanopore sensors are comprehensively summarized. Moreover, this review covers the current advances of nanopore techniques in the application of food safety screening. Currently, the establishment of nanopore sensing devices is mainly based on the blocking current phenomenon. Sensing interfaces including biological nanopores, solid-state nanopores, DNA origami, and de novo designed nanopores can be used in the manufacture of sensing devices. Food harmful substances, including heavy metals, veterinary drugs, pesticide residues, food toxins, and other harmful substances can be quickly determined by nanopore-based sensors. Moreover, the combination of nanopore techniques with advanced materials has become one of the most effective methods to improve sensing properties.
Collapse
Affiliation(s)
- Xiaoyi Tan
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
| | - Chenyan Lv
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
| | - Hai Chen
- College of Food Science, Southwest University, Chongqing, China
| |
Collapse
|
81
|
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.
Collapse
|
82
|
Marchand A, Van Hall-Beauvais AK, Correia BE. Computational design of novel protein–protein interactions – An overview on methodological approaches and applications. Curr Opin Struct Biol 2022; 74:102370. [DOI: 10.1016/j.sbi.2022.102370] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/21/2022] [Accepted: 02/28/2022] [Indexed: 11/27/2022]
|
83
|
Eguchi RR, Choe CA, Huang PS. Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation. PLoS Comput Biol 2022; 18:e1010271. [PMID: 35759518 PMCID: PMC9269947 DOI: 10.1371/journal.pcbi.1010271] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 07/08/2022] [Accepted: 06/01/2022] [Indexed: 12/26/2022] Open
Abstract
While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation—an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used with Rosetta to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model’s generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model. Many essential biochemical processes are governed by protein-protein interactions (PPIs), and our ability to make binding proteins that modulate PPIs is crucial to the creation of therapeutics and the study of cell-signaling. One critical aspect of PPI design is to capture protein conformational flexibility. Deep generative models are a class of mathematical models that are able to synthesize novel data from a finite set of training examples. Here, we make advances in computational protein design methodology by developing a deep generative model that creates protein backbones adopting the immunoglobulin fold, which is found in natural binding proteins such as antibodies. While generative models have been powerful in tasks such as image generation, using them to create proteins has remained a challenge. We solve this problem with a new model that allows for the direct generation of novel 3D molecules and show that they are of high chemical accuracy. Generated structures work well with existing protein design methods such as Rosetta, providing access to a large collection of novel immunoglobulin structures. Finally, we present a new protein design framework, called “generative design,” that shows how deep generative models such as ours can be applied to virtually any protein design problem.
Collapse
Affiliation(s)
- Raphael R. Eguchi
- Department of Biochemistry, Stanford University, Stanford, California, United States of America
- Department of Statistics, Stanford University, Stanford, California, United States of America
| | - Christian A. Choe
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Po-Ssu Huang
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
- * E-mail:
| |
Collapse
|
84
|
Computational design of transmembrane proteins. Curr Opin Struct Biol 2022; 74:102381. [DOI: 10.1016/j.sbi.2022.102381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/28/2022] [Accepted: 03/17/2022] [Indexed: 11/03/2022]
|
85
|
The Structural Rule Distinguishing a Superfold: A Case Study of Ferredoxin Fold and the Reverse Ferredoxin Fold. Molecules 2022; 27:molecules27113547. [PMID: 35684484 PMCID: PMC9181952 DOI: 10.3390/molecules27113547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/24/2022] [Accepted: 05/28/2022] [Indexed: 01/27/2023] Open
Abstract
Superfolds are folds commonly observed among evolutionarily unrelated multiple superfamilies of proteins. Since discovering superfolds almost two decades ago, structural rules distinguishing superfolds from the other ordinary folds have been explored but remained elusive. Here, we analyzed a typical superfold, the ferredoxin fold, and the fold which reverses the N to C terminus direction from the ferredoxin fold as a case study to find the rule to distinguish superfolds from the other folds. Though all the known structural characteristics for superfolds apply to both the ferredoxin fold and the reverse ferredoxin fold, the reverse fold has been found only in a single superfamily. The database analyses in the present study revealed the structural preferences of αβ- and βα-units; the preferences separate two α-helices in the ferredoxin fold, preventing their collision and stabilizing the fold. In contrast, in the reverse ferredoxin fold, the preferences bring two helices near each other, inducing structural conflict. The Rosetta folding simulations suggested that the ferredoxin fold is physically much more realizable than the reverse ferredoxin fold. Therefore, we propose that minimal structural conflict or minimal frustration among secondary structures is the rule to distinguish a superfold from ordinary folds. Intriguingly, the database analyses revealed that a most stringent structural rule in proteins, the right-handedness of the βαβ-unit, is broken in a set of structures to prevent the frustration, suggesting the proposed rule of minimum frustration among secondary structural units is comparably strong as the right-handedness rule of the βαβ-unit.
Collapse
|
86
|
Molina RS, Rix G, Mengiste AA, Alvarez B, Seo D, Chen H, Hurtado J, Zhang Q, Donato García-García J, Heins ZJ, Almhjell PJ, Arnold FH, Khalil AS, Hanson AD, Dueber JE, Schaffer DV, Chen F, Kim S, Ángel Fernández L, Shoulders MD, Liu CC. In vivo hypermutation and continuous evolution. NATURE REVIEWS. METHODS PRIMERS 2022; 2:37. [PMID: 37073402 PMCID: PMC10108624 DOI: 10.1038/s43586-022-00130-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Rosana S. Molina
- Department of Biomedical Engineering, University of California, Irvine, CA 92617, USA
| | - Gordon Rix
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697, USA
| | - Amanuella A. Mengiste
- Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
| | - Beatriz Alvarez
- Department of Microbial Biotechnology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Darwin 3, Campus UAM Cantoblanco, 28049 Madrid, Spain
| | - Daeje Seo
- Department of Chemistry, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea
| | - Haiqi Chen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Juan Hurtado
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
| | - Qiong Zhang
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
| | - Jorge Donato García-García
- Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Av. General Ramon Corona 2514, Nuevo Mexico, C.P. 45138, Zapopan, Jalisco, Mexico
| | - Zachary J. Heins
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Patrick J. Almhjell
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Frances H. Arnold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Ahmad S. Khalil
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA
| | - Andrew D. Hanson
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - John E. Dueber
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
- Innovative Genomics Institute, University of California Berkeley and San Francisco, Berkeley, CA, USA
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - David V. Schaffer
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
- Innovative Genomics Institute, University of California Berkeley and San Francisco, Berkeley, CA, USA
- Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Fei Chen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seokhee Kim
- Department of Chemistry, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea
| | - Luis Ángel Fernández
- Department of Microbial Biotechnology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Darwin 3, Campus UAM Cantoblanco, 28049 Madrid, Spain
| | - Matthew D. Shoulders
- Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
| | - Chang C. Liu
- Department of Biomedical Engineering, University of California, Irvine, CA 92617, USA
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697, USA
- Department of Chemistry, University of California, Irvine, CA 92617, USA
| |
Collapse
|
87
|
Ding W, Nakai K, Gong H. Protein design via deep learning. Brief Bioinform 2022; 23:bbac102. [PMID: 35348602 PMCID: PMC9116377 DOI: 10.1093/bib/bbac102] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/26/2022] [Accepted: 03/01/2022] [Indexed: 12/11/2022] Open
Abstract
Proteins with desired functions and properties are important in fields like nanotechnology and biomedicine. De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.
Collapse
Affiliation(s)
- Wenze Ding
- School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
- School of Future Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
| | - Kenta Nakai
- Institute of Medical Science, the University of Tokyo, Tokyo 1088639, Japan
| | - Haipeng Gong
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
| |
Collapse
|
88
|
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.
Collapse
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
| |
Collapse
|
89
|
Cao L, Coventry B, Goreshnik I, Huang B, Sheffler W, Park JS, Jude KM, Marković I, Kadam RU, Verschueren KHG, Verstraete K, Walsh STR, Bennett N, Phal A, Yang A, Kozodoy L, DeWitt M, Picton L, Miller L, Strauch EM, DeBouver ND, Pires A, Bera AK, Halabiya S, Hammerson B, Yang W, Bernard S, Stewart L, Wilson IA, Ruohola-Baker H, Schlessinger J, Lee S, Savvides SN, Garcia KC, Baker D. Design of protein-binding proteins from the target structure alone. Nature 2022; 605:551-560. [PMID: 35332283 PMCID: PMC9117152 DOI: 10.1038/s41586-022-04654-9] [Citation(s) in RCA: 152] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 03/15/2022] [Indexed: 12/03/2022]
Abstract
The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge1-5. Here we describe a general solution to this problem that starts with a broad exploration of the vast space of possible binding modes to a selected region of a protein surface, and then intensifies the search in the vicinity of the most promising binding modes. We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and, following experimental optimization, bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of five of the binder-target complexes, and all five closely match the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein-protein interactions, and should guide improvements of both. Our approach enables the targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications.
Collapse
Affiliation(s)
- Longxing Cao
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA
| | - Inna Goreshnik
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Buwei Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - William Sheffler
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Joon Sung Park
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | - Kevin M Jude
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Iva Marković
- VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
| | - Rameshwar U Kadam
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Koen H G Verschueren
- VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
| | - Kenneth Verstraete
- VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
| | - Scott Thomas Russell Walsh
- Chemical Biology Laboratory, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
- J.A.M.E.S. Farm, Clarksville, MD, USA
| | - Nathaniel Bennett
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA
| | - Ashish Phal
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Aerin Yang
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Lisa Kozodoy
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Michelle DeWitt
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lora Picton
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Lauren Miller
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Eva-Maria Strauch
- Deptartment of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, GA, USA
| | - Nicholas D DeBouver
- UCB Pharma, Bainbridge Island, WA, USA
- Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA
| | - Allison Pires
- Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA
- Seattle Children's Center for Global Infectious Disease Research, Seattle, WA, USA
| | - Asim K Bera
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Samer Halabiya
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Bradley Hammerson
- Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA
| | - Wei Yang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Steffen Bernard
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Lance Stewart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ian A Wilson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
- The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Hannele Ruohola-Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Joseph Schlessinger
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | - Sangwon Lee
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | - Savvas N Savvides
- VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
| | - K Christopher Garcia
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
| |
Collapse
|
90
|
Hostetter ER, Keyes JR, Poon I, Nguyen JP, Nite JM, Jimenez Hoyos CA, Smith CA. Prediction of Fluorophore Brightness in Designed Mini Fluorescence Activating Proteins. J Chem Theory Comput 2022; 18:3190-3203. [PMID: 35417158 DOI: 10.1021/acs.jctc.1c00748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The de novo computational design of proteins with predefined three-dimensional structure is becoming much more routine due to advancements both in force fields and algorithms. However, creating designs with functions beyond folding is more challenging. In that regard, the recent design of small beta barrel proteins that activate the fluorescence of an exogenous small molecule chromophore (DFHBI) is noteworthy. These proteins, termed mini fluorescence activating proteins (mFAPs), have been shown to increase the brightness of the chromophore more than 100-fold upon binding to the designed ligand pocket. The design process created a large library of variants with different brightness levels but gave no rational explanation for why one variant was brighter than another. Here, we use quantum mechanics and molecular dynamics simulations to investigate how molecular flexibility in the ground and excited states influences brightness. We show that the ability of the protein to resist dihedral angle rotation of the chromophore is critical for predicting brightness. Our simulations suggest that the mFAP/DFHBI complex has a rough energy landscape, requiring extensive ground-state sampling to achieve converged predictions of excited-state kinetics. While computationally demanding, this roughness suggests that mFAP protein function can be enhanced by reshaping the energy landscape toward conformations that better resist DFHBI bond rotation.
Collapse
Affiliation(s)
- Emma R Hostetter
- Department of Chemistry, Wesleyan University, 52 Lawn Ave, Middletown, Connecticut 06459, United States
| | - Jeffrey R Keyes
- Department of Chemistry, Wesleyan University, 52 Lawn Ave, Middletown, Connecticut 06459, United States
| | - Ivy Poon
- Department of Chemistry, Wesleyan University, 52 Lawn Ave, Middletown, Connecticut 06459, United States
| | - Justin P Nguyen
- Department of Chemistry, Wesleyan University, 52 Lawn Ave, Middletown, Connecticut 06459, United States
| | - Jacob M Nite
- Department of Chemistry, Wesleyan University, 52 Lawn Ave, Middletown, Connecticut 06459, United States
| | -
- Department of Chemistry, Wesleyan University, 52 Lawn Ave, Middletown, Connecticut 06459, United States
| | - Carlos A Jimenez Hoyos
- Department of Chemistry, Wesleyan University, 52 Lawn Ave, Middletown, Connecticut 06459, United States
| | - Colin A Smith
- Department of Chemistry, Wesleyan University, 52 Lawn Ave, Middletown, Connecticut 06459, United States
| |
Collapse
|
91
|
Rottinghaus AG, Xi C, Amrofell MB, Yi H, Moon TS. Engineering ligand-specific biosensors for aromatic amino acids and neurochemicals. Cell Syst 2022; 13:204-214.e4. [PMID: 34767760 PMCID: PMC8930536 DOI: 10.1016/j.cels.2021.10.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 08/08/2021] [Accepted: 10/20/2021] [Indexed: 01/09/2023]
Abstract
Microbial biosensors have diverse applications in metabolic engineering and medicine. Specific and accurate quantification of chemical concentrations allows for adaptive regulation of enzymatic pathways and temporally precise expression of diagnostic reporters. Although biosensors should differentiate structurally similar ligands with distinct biological functions, such specific sensors are rarely found in nature and challenging to create. Using E. coli Nissle 1917, a generally regarded as safe microbe, we characterized two biosensor systems that promiscuously recognize aromatic amino acids or neurochemicals. To improve the sensors' selectivity and sensitivity, we applied rational protein engineering by identifying and mutagenizing amino acid residues and successfully demonstrated the ligand-specific biosensors for phenylalanine, tyrosine, phenylethylamine, and tyramine. Additionally, our approach revealed insights into the uncharacterized structure of the FeaR regulator, including critical residues in ligand binding. These results lay the groundwork for developing kinetically adaptive microbes for diverse applications. A record of this paper's transparent peer review process is included in the supplemental information.
Collapse
Affiliation(s)
- Austin G Rottinghaus
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Chenggang Xi
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Matthew B Amrofell
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Hyojeong Yi
- 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.
| |
Collapse
|
92
|
Lin CY, Romei MG, Mathews II, Boxer SG. Energetic Basis and Design of Enzyme Function Demonstrated Using GFP, an Excited-State Enzyme. J Am Chem Soc 2022; 144:3968-3978. [PMID: 35200017 PMCID: PMC9014791 DOI: 10.1021/jacs.1c12305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The past decades have witnessed an explosion of de novo protein designs with a remarkable range of scaffolds. It remains challenging, however, to design catalytic functions that are competitive with naturally occurring counterparts as well as biomimetic or nonbiological catalysts. Although directed evolution often offers efficient solutions, the fitness landscape remains opaque. Green fluorescent protein (GFP), which has revolutionized biological imaging and assays, is one of the most redesigned proteins. While not an enzyme in the conventional sense, GFPs feature competing excited-state decay pathways with the same steric and electrostatic origins as conventional ground-state catalysts, and they exert exquisite control over multiple reaction outcomes through the same principles. Thus, GFP is an "excited-state enzyme". Herein we show that rationally designed mutants and hybrids that contain environmental mutations and substituted chromophores provide the basis for a quantitative model and prediction that describes the influence of sterics and electrostatics on excited-state catalysis of GFPs. As both perturbations can selectively bias photoisomerization pathways, GFPs with fluorescence quantum yields (FQYs) and photoswitching characteristics tailored for specific applications could be predicted and then demonstrated. The underlying energetic landscape, readily accessible via spectroscopy for GFPs, offers an important missing link in the design of protein function that is generalizable to catalyst design.
Collapse
Affiliation(s)
- Chi-Yun Lin
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
| | - Matthew G Romei
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
| | - Irimpan I Mathews
- Stanford Synchrotron Radiation Lightsource, 2575 Sand Hill Road, Menlo Park, California 94025, United States
| | - Steven G Boxer
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
| |
Collapse
|
93
|
Tatta ER, Imchen M, Moopantakath J, Kumavath R. Bioprospecting of microbial enzymes: current trends in industry and healthcare. Appl Microbiol Biotechnol 2022; 106:1813-1835. [PMID: 35254498 DOI: 10.1007/s00253-022-11859-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 02/15/2022] [Accepted: 02/26/2022] [Indexed: 12/13/2022]
Abstract
Microbial enzymes have an indispensable role in producing foods, pharmaceuticals, and other commercial goods. Many novel enzymes have been reported from all domains of life, such as plants, microbes, and animals. Nonetheless, industrially desirable enzymes of microbial origin are limited. This review article discusses the classifications, applications, sources, and challenges of most demanded industrial enzymes such as pectinases, cellulase, lipase, and protease. In addition, the production of novel enzymes through protein engineering technologies such as directed evolution, rational, and de novo design, for the improvement of existing industrial enzymes is also explored. We have also explored the role of metagenomics, nanotechnology, OMICs, and machine learning approaches in the bioprospecting of novel enzymes. Overall, this review covers the basics of biocatalysts in industrial and healthcare applications and provides an overview of existing microbial enzyme optimization tools. KEY POINTS: • Microbial bioactive molecules are vital for therapeutic and industrial applications. • High-throughput OMIC is the most proficient approach for novel enzyme discovery. • Comprehensive databases and efficient machine learning models are the need of the hour to fast forward de novo enzyme design and discovery.
Collapse
Affiliation(s)
- Eswar Rao Tatta
- Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (PO.), Kasaragod, Kerala, 671320, India
| | - Madangchanok Imchen
- Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (PO.), Kasaragod, Kerala, 671320, India
| | - Jamseel Moopantakath
- Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (PO.), Kasaragod, Kerala, 671320, India
| | - Ranjith Kumavath
- Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (PO.), Kasaragod, Kerala, 671320, India.
| |
Collapse
|
94
|
Yang P, Ning K. How much metagenome data is needed for protein structure prediction: The advantages of targeted approach from the ecological and evolutionary perspectives. IMETA 2022; 1:e9. [PMID: 38867727 PMCID: PMC10989767 DOI: 10.1002/imt2.9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/23/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2024]
Abstract
It has been proven that three-dimensional protein structures could be modeled by supplementing homologous sequences with metagenome sequences. Even though a large volume of metagenome data is utilized for such purposes, a significant proportion of proteins remain unsolved. In this review, we focus on identifying ecological and evolutionary patterns in metagenome data, decoding the complicated relationships of these patterns with protein structures, and investigating how these patterns can be effectively used to improve protein structure prediction. First, we proposed the metagenome utilization efficiency and marginal effect model to quantify the divergent distribution of homologous sequences for the protein family. Second, we proposed that the targeted approach effectively identifies homologous sequences from specified biomes compared with the untargeted approach's blind search. Finally, we determined the lower bound for metagenome data required for predicting all the protein structures in the Pfam database and showed that the present metagenome data is insufficient for this purpose. In summary, we discovered ecological and evolutionary patterns in the metagenome data that may be used to predict protein structures effectively. The targeted approach is promising in terms of effectively extracting homologous sequences and predicting protein structures using these patterns.
Collapse
Affiliation(s)
- Pengshuo Yang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular‐Imaging, Department of Bioinformatics and Systems BiologyCenter of AI Biology, College of Life Science and Technology, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular‐Imaging, Department of Bioinformatics and Systems BiologyCenter of AI Biology, College of Life Science and Technology, Huazhong University of Science and TechnologyWuhanHubeiChina
| |
Collapse
|
95
|
Kim TY, Yoon TS, Kang S, Afzal M. Juggling with fluorescent proteins: Spectrum and structural changes of the mCardinal2 variants. Biochem Biophys Res Commun 2022; 593:79-83. [PMID: 35063773 DOI: 10.1016/j.bbrc.2022.01.044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 01/06/2022] [Accepted: 01/12/2022] [Indexed: 11/16/2022]
Abstract
mCardinal2 is a red fluorescent protein developed through the designs of mKate, mNeptune and mCardinal. Fluorescence spectrums of mCardinal2 and its five mutants (T143C, T143G, C158A, C158D and M160E) were measured with their quantum yields. C158A and C158D increased brightness with slight changes in fluorescence spectrums while T143C, T143G and M160E decreased brightness with blue shift in fluorescence spectrums, which resulted in green, cyan and green fluorescent proteins respectively. Crystal structures of all six variants were analyzed and compared together with those of mKate, LSS-mKate1, LSS-mKate2 and mCardinal. Around the Cα-Cβ bond of Tyr64 in the MYG chromophores, only C158A and C158D were in the trans conformation while all others were mostly in the cis conformation. Blue-shift brightness-decreased variants (T143C, T143G and M160E) showed the diminished hydrogen bonds while large-Stoke-shift brightness-increased variant C158D showed the enhanced hydrogen bonds around the chromophore.
Collapse
Affiliation(s)
- Tae-Yeon Kim
- Disease Target Structure Research Center, Korea Research Institute Bioscience & Biotechnology, 125 Gwahak-Ro, Yuseong-Gu, Daejeon, 34141, South Korea; Proteomic Structural Biology, KRIBB School, University Science & Technology, 125 Gwahak-Ro, Yuseong-Gu, Daejeon, 34141, South Korea
| | - Tae-Sung Yoon
- Disease Target Structure Research Center, Korea Research Institute Bioscience & Biotechnology, 125 Gwahak-Ro, Yuseong-Gu, Daejeon, 34141, South Korea; Proteomic Structural Biology, KRIBB School, University Science & Technology, 125 Gwahak-Ro, Yuseong-Gu, Daejeon, 34141, South Korea.
| | - Sunghyun Kang
- Disease Target Structure Research Center, Korea Research Institute Bioscience & Biotechnology, 125 Gwahak-Ro, Yuseong-Gu, Daejeon, 34141, South Korea.
| | - Muhammad Afzal
- Department of Mechanical Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea.
| |
Collapse
|
96
|
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: 42] [Impact Index Per Article: 21.0] [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.
Collapse
|
97
|
Anand N, Eguchi R, Mathews II, Perez CP, Derry A, Altman RB, Huang PS. Protein sequence design with a learned potential. Nat Commun 2022; 13:746. [PMID: 35136054 PMCID: PMC8826426 DOI: 10.1038/s41467-022-28313-9] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 01/08/2022] [Indexed: 11/08/2022] Open
Abstract
The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.
Collapse
Affiliation(s)
- Namrata Anand
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Raphael Eguchi
- Department of Biochemistry, Stanford University, Stanford, CA, USA
| | - Irimpan I Mathews
- Stanford Synchrotron Radiation Lightsource, Menlo Park, CA, 94025, USA
| | - Carla P Perez
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Alexander Derry
- Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Departments of Genetics and Medicine, Stanford University, Stanford, CA, USA
| | - Po-Ssu Huang
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
| |
Collapse
|
98
|
Zhang M, Chen C, Zhang Y, Geng J. Biological nanopores for sensing applications. Proteins 2022; 90:1786-1799. [PMID: 35092317 DOI: 10.1002/prot.26308] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/27/2021] [Accepted: 01/27/2022] [Indexed: 02/05/2023]
Abstract
Biological nanopores are proteins with transmembrane pore that can be embedded in lipid bilayer. With the development of single-channel current measurement technologies, biological nanopores have been reconstituted into planar lipid bilayer and used for single-molecule sensing of various analytes and events such as single-molecule DNA sensing and sequencing. To improve the sensitivity for specific analytes, various engineered nanopore proteins and strategies are deployed. Here, we introduce the origin and principle of nanopore sensing technology as well as the structure and associated properties of frequently used protein nanopores. Furthermore, sensing strategies for different applications are reviewed, with focus on the alteration of buffer condition, protein engineering, and deployment of accessory proteins and adapter-assisted sensing. Finally, outlooks for de novo design of nanopore and nanopore beyond sensing are discussed.
Collapse
Affiliation(s)
- Ming Zhang
- Department of Laboratory Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, China
| | - Chen Chen
- Department of Laboratory Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, China
| | - Yanjing Zhang
- Department of Laboratory Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, China
| | - Jia Geng
- Department of Laboratory Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
99
|
Röder K, Wales DJ. The Energy Landscape Perspective: Encoding Structure and Function for Biomolecules. Front Mol Biosci 2022; 9:820792. [PMID: 35155579 PMCID: PMC8829389 DOI: 10.3389/fmolb.2022.820792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 01/07/2022] [Indexed: 12/02/2022] Open
Abstract
The energy landscape perspective is outlined with particular reference to biomolecules that perform multiple functions. We associate these multifunctional molecules with multifunnel energy landscapes, illustrated by some selected examples, where understanding the organisation of the landscape has provided new insight into function. Conformational selection and induced fit may provide alternative routes to realisation of multifunctionality, exploiting the possibility of environmental control and distinct binding modes.
Collapse
Affiliation(s)
| | - David J. Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| |
Collapse
|
100
|
Wei J, Xu L, Wu WH, Sun F, Zhang WB. Genetically engineered materials: Proteins and beyond. Sci China Chem 2022; 65:486-496. [PMID: 35154293 PMCID: PMC8815391 DOI: 10.1007/s11426-021-1183-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 12/21/2021] [Indexed: 02/01/2023]
Abstract
Information-rich molecules provide opportunities for evolution. Genetically engineered materials are superior in that their properties are coded within genetic sequences and could be fine-tuned. In this review, we elaborate the concept of genetically engineered materials (GEMs) using examples ranging from engineered protein materials to engineered living materials. Protein-based materials are the materials of choice by nature. Recent progress in protein engineering has led to opportunities to tune their sequences for optimal material performance. Proteins also play a central role in living materials where they act in concert with other biological components as well as nonbiological cofactors, giving rise to living features. While the existing GEMs are often limited to those constructed by building blocks of biological origin, being genetically engineerable does not preclude nonbiologic or synthetic materials, the latter of which have yet to be fully explored.
![]()
Collapse
Affiliation(s)
- Jingjing Wei
- College of Chemical and Environmental Engineering, Anyang Institute of Technology, Anyang, 455000 China
| | - Lianjie Xu
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Polymer Chemistry & Physics of Ministry of Education, Center for Soft Matter Science and Engineering, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871 China
| | - Wen-Hao Wu
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Polymer Chemistry & Physics of Ministry of Education, Center for Soft Matter Science and Engineering, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871 China
| | - Fei Sun
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
| | - Wen-Bin Zhang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Polymer Chemistry & Physics of Ministry of Education, Center for Soft Matter Science and Engineering, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871 China
| |
Collapse
|