1
|
Albanese KI, Petrenas R, Pirro F, Naudin EA, Borucu U, Dawson WM, Scott DA, Leggett GJ, Weiner OD, Oliver TAA, Woolfson DN. Rationally seeded computational protein design of ɑ-helical barrels. Nat Chem Biol 2024:10.1038/s41589-024-01642-0. [PMID: 38902458 DOI: 10.1038/s41589-024-01642-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 05/09/2024] [Indexed: 06/22/2024]
Abstract
Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.
Collapse
Affiliation(s)
- Katherine I Albanese
- School of Chemistry, University of Bristol, Bristol, UK
- Max Planck-Bristol Centre for Minimal Biology, University of Bristol, Bristol, UK
| | | | - Fabio Pirro
- School of Chemistry, University of Bristol, Bristol, UK
| | | | - Ufuk Borucu
- School of Biochemistry, University of Bristol, Medical Sciences Building, Bristol, UK
| | | | - D Arne Scott
- Rosa Biotech, Science Creates St Philips, Bristol, UK
| | | | - Orion D Weiner
- Cardiovascular Research Institute, Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
| | | | - Derek N Woolfson
- School of Chemistry, University of Bristol, Bristol, UK.
- Max Planck-Bristol Centre for Minimal Biology, University of Bristol, Bristol, UK.
- School of Biochemistry, University of Bristol, Medical Sciences Building, Bristol, UK.
- Bristol BioDesign Institute, University of Bristol, Bristol, UK.
| |
Collapse
|
2
|
Wallace HM, Yang H, Tan S, Pan HS, Yang R, Xu J, Jo H, Condello C, Polizzi NF, DeGrado WF. De novo design of peptides that bind specific conformers of α-synuclein. Chem Sci 2024; 15:8414-8421. [PMID: 38846390 PMCID: PMC11151861 DOI: 10.1039/d3sc06245g] [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: 11/22/2023] [Accepted: 03/14/2024] [Indexed: 06/09/2024] Open
Abstract
Insoluble amyloids rich in cross-β fibrils are observed in a number of neurodegenerative diseases. Depending on the clinicopathology, the amyloids can adopt distinct supramolecular assemblies, termed conformational strains. However, rapid methods to study amyloids in a conformationally specific manner are lacking. We introduce a novel computational method for de novo design of peptides that tile the surface of α-synuclein fibrils in a conformationally specific manner. Our method begins by identifying surfaces that are unique to the conformational strain of interest, which becomes a "target backbone" for the design of a peptide binder. Next, we interrogate structures in the PDB with high geometric complementarity to the target. Then, we identify secondary structural motifs that interact with this target backbone in a favorable, highly occurring geometry. This method produces monomeric helical motifs with a favorable geometry for interaction with the strands of the underlying amyloid. Each motif is then symmetrically replicated to form a monolayer that tiles the amyloid surface. Finally, amino acid sequences of the peptide binders are computed to provide a sequence with high geometric and physicochemical complementarity to the target amyloid. This method was applied to a conformational strain of α-synuclein fibrils, resulting in a peptide with high specificity for the target relative to other amyloids formed by α-synuclein, tau, or Aβ40. This designed peptide also markedly slowed the formation of α-synuclein amyloids. Overall, this method offers a new tool for examining conformational strains of amyloid proteins.
Collapse
Affiliation(s)
- Hailey M Wallace
- Department of Pharmaceutical Chemistry, The Cardiovascular Research Institution, University of California San Francisco CA 94158 USA
| | - Hyunjun Yang
- Department of Pharmaceutical Chemistry, The Cardiovascular Research Institution, University of California San Francisco CA 94158 USA
- Institute for Neurodegenerative Diseases, University of California San Francisco CA 94143 USA
| | - Sophia Tan
- Department of Pharmaceutical Chemistry, The Cardiovascular Research Institution, University of California San Francisco CA 94158 USA
| | - Henry S Pan
- Department of Pharmaceutical Chemistry, The Cardiovascular Research Institution, University of California San Francisco CA 94158 USA
| | - Rose Yang
- Department of Pharmaceutical Chemistry, The Cardiovascular Research Institution, University of California San Francisco CA 94158 USA
| | - Junyi Xu
- Department of Pharmaceutical Chemistry, The Cardiovascular Research Institution, University of California San Francisco CA 94158 USA
| | - Hyunil Jo
- Department of Pharmaceutical Chemistry, The Cardiovascular Research Institution, University of California San Francisco CA 94158 USA
| | - Carlo Condello
- Institute for Neurodegenerative Diseases, University of California San Francisco CA 94143 USA
- Department of Neurology, University of California San Francisco CA 94143 USA
| | - Nicholas F Polizzi
- Dana Farber Cancer Institute, Harvard Medical School Boston MA 02215 USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School Boston MA 02215 USA
| | - William F DeGrado
- Department of Pharmaceutical Chemistry, The Cardiovascular Research Institution, University of California San Francisco CA 94158 USA
- Institute for Neurodegenerative Diseases, University of California San Francisco CA 94143 USA
| |
Collapse
|
3
|
Lu L, Gou X, Tan SK, Mann SI, Yang H, Zhong X, Gazgalis D, Valdiviezo J, Jo H, Wu Y, Diolaiti ME, Ashworth A, Polizzi NF, DeGrado WF. De novo design of drug-binding proteins with predictable binding energy and specificity. Science 2024; 384:106-112. [PMID: 38574125 DOI: 10.1126/science.adl5364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/28/2024] [Indexed: 04/06/2024]
Abstract
The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule-binding proteins with tuned interaction energies is feasible entirely from computation.
Collapse
Affiliation(s)
- Lei Lu
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Xuxu Gou
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Sophia K Tan
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Samuel I Mann
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
- Department of Chemistry, University of California, Riverside, CA 92521, USA
| | - Hyunjun Yang
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Xiaofang Zhong
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
| | - Dimitrios Gazgalis
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - Jesús Valdiviezo
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - Hyunil Jo
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Yibing Wu
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Morgan E Diolaiti
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Alan Ashworth
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Nicholas F Polizzi
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - William F DeGrado
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| |
Collapse
|
4
|
Guerin N, Childs H, Zhou P, Donald BR. DexDesign: A new OSPREY-based algorithm for designing de novo D-peptide inhibitors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.12.579944. [PMID: 38405797 PMCID: PMC10888900 DOI: 10.1101/2024.02.12.579944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan, which enable exponential reductions in the size of the peptide sequence search space. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a new framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead therapeutic candidates. We provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.
Collapse
|
5
|
Guerin N, Childs H, Zhou P, Donald BR. DexDesign: an OSPREY-based algorithm for designing de novo D-peptide inhibitors. Protein Eng Des Sel 2024; 37:gzae007. [PMID: 38757573 PMCID: PMC11099876 DOI: 10.1093/protein/gzae007] [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: 09/03/2023] [Revised: 04/17/2024] [Indexed: 05/18/2024] Open
Abstract
With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead inhibitors. We also provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.
Collapse
Affiliation(s)
- Nathan Guerin
- Department of Computer Science, Duke University, 308 Research Drive, Durham, NC 27708, United States
| | - Henry Childs
- Department of Chemistry, Duke University, 124 Science Drive, Durham, NC 27708, United States
| | - Pei Zhou
- Department of Biochemistry, Duke University School of Medicine, 307 Research Drive, Durham, NC 22710, United States
| | - Bruce R Donald
- Department of Computer Science, Duke University, 308 Research Drive, Durham, NC 27708, United States
- Department of Chemistry, Duke University, 124 Science Drive, Durham, NC 27708, United States
- Department of Biochemistry, Duke University School of Medicine, 307 Research Drive, Durham, NC 22710, United States
- Department of Mathematics, Duke University, 120 Science Drive, Durham, NC 27708, United States
| |
Collapse
|
6
|
Lu L, Gou X, Tan SK, Mann SI, Yang H, Zhong X, Gazgalis D, Valdiviezo J, Jo H, Wu Y, Diolaiti ME, Ashworth A, Polizzi NF, DeGrado WF. De novo design of drug-binding proteins with predictable binding energy and specificity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.23.573178. [PMID: 38187746 PMCID: PMC10769398 DOI: 10.1101/2023.12.23.573178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The de novo design of small-molecule-binding proteins has seen exciting recent progress; however, the ability to achieve exquisite affinity for binding small molecules while tuning specificity has not yet been demonstrated directly from computation. Here, we develop a computational procedure that results in the highest affinity binders to date with predetermined relative affinities, targeting a series of PARP1 inhibitors. Two of four designed proteins bound with affinities ranging from < 5 nM to low μM, in a predictable manner. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free-energy calculations performed directly on the designed models are in excellent agreement with the experimentally measured affinities, suggesting that the de novo design of small-molecule-binding proteins with tuned interaction energies is now feasible entirely from computation. We expect these methods to open many opportunities in biomedicine, including rapid sensor development, antidote design, and drug delivery vehicles.
Collapse
Affiliation(s)
- Lei Lu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Xuxu Gou
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Sophia K Tan
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Samuel I. Mann
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Hyunjun Yang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | | | - Dimitrios Gazgalis
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Jesús Valdiviezo
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Hyunil Jo
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yibing Wu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Morgan E. Diolaiti
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Alan Ashworth
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | | | - William F. DeGrado
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| |
Collapse
|
7
|
Wallace HM, Yang H, Tan S, Pan HS, Yang R, Xu J, Jo H, Condello C, Polizzi NF, DeGrado WF. De novo Design of Peptides that Bind Specific Conformers of α-Synuclein. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.14.567090. [PMID: 38014268 PMCID: PMC10680688 DOI: 10.1101/2023.11.14.567090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Insoluble amyloids rich in cross-β fibrils are observed in a number of neurodegenerative diseases. Depending on the clinicopathology, the amyloids can adopt distinct supramolecular assemblies, termed conformational strains. However, rapid methods to study amyloid in a conformationally specific manner are lacking. We introduce a novel computational method for de novo design of peptides that tile the surface of α-synuclein fibrils in a conformationally specific manner. Our method begins by identifying surfaces that are unique to the conformational strain of interest, which becomes a "target backbone" for the design of a peptide binder. Next, we interrogate structures in the PDB database with high geometric complementarity to the target. Then, we identify secondary structural motifs that interact with this target backbone in a favorable, highly occurring geometry. This method produces monomeric helical motifs with a favorable geometry for interaction with the strands of the underlying amyloid. Each motif is then symmetrically replicated to form a monolayer that tiles the amyloid surface. Finally, amino acid sequences of the peptide binders are computed to provide a sequence with high geometric and physicochemical complementarity to the target amyloid. This method was applied to a conformational strain of α-synuclein fibrils, resulting in a peptide with high specificity for the target relative to other amyloids formed by α-synuclein, tau, or Aβ40. This designed peptide also markedly slowed the formation of α-synuclein amyloids. Overall, this method offers a new tool for examining conformational strains of amyloid proteins.
Collapse
|
8
|
Meador K, Castells-Graells R, Aguirre R, Sawaya MR, Arbing MA, Sherman T, Senarathne C, Yeates TO. A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.09.561468. [PMID: 37873110 PMCID: PMC10592684 DOI: 10.1101/2023.10.09.561468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, while methods for their creation remain challenging and unpredictable. In the present study, we apply new computational approaches to design a suite of new tetrahedrally symmetric, self-assembling protein cages. For the generation of docked poses, we emphasize a protein fragment-based approach, while for de novo interface design, a comparison of computational protocols highlights the power and increased experimental success achieved using the machine learning program ProteinMPNN. In relating information from docking and design, we observe that agreement between fragment-based sequence preferences and ProteinMPNN sequence inference correlates with experimental success. Additional insights for designing polar interactions are highlighted by experimentally testing larger and more polar interfaces. In all, using X-ray crystallography and cryo-EM, we report five structures for seven protein cages, with atomic resolution in the best case reaching 2.0 Å. We also report structures of two incompletely assembled protein cages, providing unique insights into one type of assembly failure. The new set of designed cages and their structures add substantially to the body of available protein nanoparticles, and to methodologies for their creation.
Collapse
Affiliation(s)
- Kyle Meador
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA 90095
| | | | - Roman Aguirre
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA 90095
| | - Michael R. Sawaya
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA, USA 90095
| | - Mark A. Arbing
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA, USA 90095
| | - Trent Sherman
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA 90095
| | - Chethaka Senarathne
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA 90095
| | - Todd O. Yeates
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA 90095
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA, USA 90095
| |
Collapse
|
9
|
Gainza P, Wehrle S, Van Hall-Beauvais A, Marchand A, Scheck A, Harteveld Z, Buckley S, Ni D, Tan S, Sverrisson F, Goverde C, Turelli P, Raclot C, Teslenko A, Pacesa M, Rosset S, Georgeon S, Marsden J, Petruzzella A, Liu K, Xu Z, Chai Y, Han P, Gao GF, Oricchio E, Fierz B, Trono D, Stahlberg H, Bronstein M, Correia BE. De novo design of protein interactions with learned surface fingerprints. Nature 2023; 617:176-184. [PMID: 37100904 PMCID: PMC10131520 DOI: 10.1038/s41586-023-05993-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 03/21/2023] [Indexed: 04/28/2023]
Abstract
Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2-9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.
Collapse
Affiliation(s)
- Pablo Gainza
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Monte Rosa Therapeutics, Basel, Switzerland
| | - Sarah Wehrle
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Alexandra Van Hall-Beauvais
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Anthony Marchand
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Andreas Scheck
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Zander Harteveld
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Stephen Buckley
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Dongchun Ni
- Laboratory of Biological Electron Microscopy, Institute of Physics, School of Basic Science, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Shuguang Tan
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Freyr Sverrisson
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Casper Goverde
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Priscilla Turelli
- Laboratory of Virology and Genetics, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Charlène Raclot
- Laboratory of Virology and Genetics, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Alexandra Teslenko
- Laboratory of Biophysical Chemistry of Macromolecules, School of Basic Sciences, Institute of Chemical Sciences and Engineering (ISIC), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Martin Pacesa
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Stéphane Rosset
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Sandrine Georgeon
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Jane Marsden
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Aaron Petruzzella
- Swiss Institute for Experimental Cancer Research, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kefang Liu
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Zepeng Xu
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Yan Chai
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Pu Han
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - George F Gao
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Elisa Oricchio
- Swiss Institute for Experimental Cancer Research, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Beat Fierz
- Laboratory of Biophysical Chemistry of Macromolecules, School of Basic Sciences, Institute of Chemical Sciences and Engineering (ISIC), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Didier Trono
- Laboratory of Virology and Genetics, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Henning Stahlberg
- Laboratory of Biological Electron Microscopy, Institute of Physics, School of Basic Science, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | | | - Bruno E Correia
- Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| |
Collapse
|
10
|
Aguilar F, Yu S, Grant RA, Swanson S, Ghose D, Su BG, Sarosiek KA, Keating AE. Peptides from human BNIP5 and PXT1 and non-native binders of pro-apoptotic BAK can directly activate or inhibit BAK-mediated membrane permeabilization. Structure 2023; 31:265-281.e7. [PMID: 36706751 PMCID: PMC9992319 DOI: 10.1016/j.str.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/24/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023]
Abstract
Apoptosis is important for development and tissue homeostasis, and its dysregulation can lead to diseases, including cancer. As an apoptotic effector, BAK undergoes conformational changes that promote mitochondrial outer membrane disruption, leading to cell death. This is termed "activation" and can be induced by peptides from the human proteins BID, BIM, and PUMA. To identify additional peptides that can regulate BAK, we used computational protein design, yeast surface display screening, and structure-based energy scoring to identify 10 diverse new binders. We discovered peptides from the human proteins BNIP5 and PXT1 and three non-native peptides that activate BAK in liposome assays and induce cytochrome c release from mitochondria. Crystal structures and binding studies reveal a high degree of similarity among peptide activators and inhibitors, ruling out a simple function-determining property. Our results shed light on the vast peptide sequence space that can regulate BAK function and will guide the design of BAK-modulating tools and therapeutics.
Collapse
Affiliation(s)
- Fiona Aguilar
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stacey Yu
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Department of Systems Biology, Harvard Medical School, Boston, MA, USA; Program in Molecular and Integrative Physiological Sciences Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA; John B. Little Center for Radiation Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Robert A Grant
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sebastian Swanson
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dia Ghose
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bonnie G Su
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kristopher A Sarosiek
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Department of Systems Biology, Harvard Medical School, Boston, MA, USA; Program in Molecular and Integrative Physiological Sciences Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA; John B. Little Center for Radiation Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Amy E Keating
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
| |
Collapse
|
11
|
Wang L, Li FL, Ma XY, Cang Y, Bai F. PPI-Miner: A Structure and Sequence Motif Co-Driven Protein-Protein Interaction Mining and Modeling Computational Method. J Chem Inf Model 2022; 62:6160-6171. [PMID: 36448715 DOI: 10.1021/acs.jcim.2c01033] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Protein-protein interactions (PPIs) play important roles in biological processes of life, and predicting PPIs becomes a critical scientific issue of concern. Most PPIs occur through small domains or motifs (fragments), which are challenging and laborious to map by standard biochemical approaches because they generally require the cloning of several truncation mutants. Here, we present a computational method, named as PPI-Miner, to fish potential protein interacting partners utilizing protein motifs as queries. In brief, this work first developed a motif-matching algorithm designed to identify the proteins that contain sequential or structural similar motifs with the given query motif. Being aligned to the query motif, the binding mode of the discovered motif and its receptor protein will be initially determined to be used to build PPI complexes accordingly. Eventually, a PPI complex structure could be built and optimized with a designed automatic protocol. Besides discovering PPIs, PPI-Miner can also be applied to other areas, i.e., the rational design of molecular glues and protein vaccines. In this work, PPI-Miner was employed to mine the potential cereblon (CRBN) substrates from human proteome. As a result, 1,739 candidates were predicted, and 16 of them have been experimentally validated in previous studies. The source code of PPI-Miner can be obtained from the GitHub repository (https://github.com/Wang-Lin-boop/PPI-Miner), the webserver is freely available for users (https://bailab.siais.shanghaitech.edu.cn/services/ppi-miner), and the database of predicted CRBN substrates is accessible at https://bailab.siais.shanghaitech.edu.cn/services/crbn-subslib.
Collapse
Affiliation(s)
| | | | | | | | - Fang Bai
- Shanghai Clinical Research and Trial Center, Shanghai201210, China
| |
Collapse
|
12
|
Aguilar Rangel M, Bedwell A, Costanzi E, Taylor RJ, Russo R, Bernardes GJL, Ricagno S, Frydman J, Vendruscolo M, Sormanni P. Fragment-based computational design of antibodies targeting structured epitopes. SCIENCE ADVANCES 2022; 8:eabp9540. [PMID: 36367941 PMCID: PMC9651861 DOI: 10.1126/sciadv.abp9540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
De novo design methods hold the promise of reducing the time and cost of antibody discovery while enabling the facile and precise targeting of predetermined epitopes. Here, we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterization showed that all designs are stable and bind their intended targets with affinities in the nanomolar range without in vitro affinity maturation. We further discuss how a high-resolution input antigen structure is not required, as similar predictions are obtained when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides a starting point for the rapid generation of lead antibodies binding to preselected epitopes.
Collapse
Affiliation(s)
- Mauricio Aguilar Rangel
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Alice Bedwell
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Elisa Costanzi
- Department of Bioscience, Università degli Studi di Milano, Milano 20133, Italy
| | - Ross J. Taylor
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Rosaria Russo
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milano 20122, Italy
| | - Gonçalo J. L. Bernardes
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Stefano Ricagno
- Department of Bioscience, Università degli Studi di Milano, Milano 20133, Italy
- Institute of Molecular and Translational Cardiology, IRCCS Policlinico San Donato, Milan 20097, Italy
| | - Judith Frydman
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| |
Collapse
|
13
|
Abstract
De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test for our understanding of the underlying physical principles of protein folding and may lead to the development of proteins with unmatched functional characteristics. The first fundamental challenge of de novo design is to devise "designable" structural templates leading to sequences that will adopt the predicted fold. Here, we built on the TopoBuilder (TB) de novo design method, to automatically assemble structural templates with native-like features starting from string descriptors that capture the overall topology of proteins. Our framework eliminates the dependency of hand-crafted and fold-specific rules through an iterative, data-driven approach that extracts geometrical parameters from structural tertiary motifs. We evaluated the TopoBuilder framework by designing sequences for a set of five protein folds and experimental characterization revealed that several sequences were folded and stable in solution. The TopoBuilder de novo design framework will be broadly useful to guide the generation of artificial proteins with customized geometries, enabling the exploration of the protein universe.
Collapse
|
14
|
Koebke KJ, Pinter TBJ, Pitts WC, Pecoraro VL. Catalysis and Electron Transfer in De Novo Designed Metalloproteins. Chem Rev 2022; 122:12046-12109. [PMID: 35763791 PMCID: PMC10735231 DOI: 10.1021/acs.chemrev.1c01025] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
One of the hallmark advances in our understanding of metalloprotein function is showcased in our ability to design new, non-native, catalytically active protein scaffolds. This review highlights progress and milestone achievements in the field of de novo metalloprotein design focused on reports from the past decade with special emphasis on de novo designs couched within common subfields of bioinorganic study: heme binding proteins, monometal- and dimetal-containing catalytic sites, and metal-containing electron transfer sites. Within each subfield, we highlight several of what we have identified as significant and important contributions to either our understanding of that subfield or de novo metalloprotein design as a discipline. These reports are placed in context both historically and scientifically. General suggestions for future directions that we feel will be important to advance our understanding or accelerate discovery are discussed.
Collapse
Affiliation(s)
- Karl J. Koebke
- Department of Chemistry, University of Michigan Ann Arbor, MI 48109 USA
| | | | - Winston C. Pitts
- Department of Chemistry, University of Michigan Ann Arbor, MI 48109 USA
| | | |
Collapse
|
15
|
Matching protein surface structural patches for high-resolution blind peptide docking. Proc Natl Acad Sci U S A 2022; 119:e2121153119. [PMID: 35482919 PMCID: PMC9170164 DOI: 10.1073/pnas.2121153119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Modeling interactions between short peptides and their receptors is a challenging docking problem due to the peptide flexibility, resulting in a formidable sampling problem of peptide conformation in addition to its orientation. Alternatively, the peptide can be viewed as a piece that complements the receptor monomer structure. Here, we show that the peptide conformation can be determined based on the receptor backbone only and sampled using local structural motifs found in solved protein monomers and interfaces, independent of sequence similarity. This approach outperforms current peptide docking protocols and promotes new directions for peptide interface design. Peptide docking can be perceived as a subproblem of protein–protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, which can then be rigid-body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely, that the protein surface defines the peptide-bound conformation. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein–peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide–protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone RMSD, with corresponding sampling in 81%/100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the bound peptide conformation is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide–protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.
Collapse
|
16
|
Holland J, Grigoryan G. Structure‐conditioned amino‐acid couplings: how contact geometry affects pairwise sequence preferences. Protein Sci 2022; 31:900-917. [PMID: 35060221 PMCID: PMC8927866 DOI: 10.1002/pro.4280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 01/06/2022] [Accepted: 01/12/2022] [Indexed: 11/11/2022]
Abstract
Relating a protein's sequence to its conformation is a central challenge for both structure prediction and sequence design. Statistical contact potentials, as well as their more descriptive versions that account for side‐chain orientation and other geometric descriptors, have served as simplistic but useful means of representing second‐order contributions in sequence–structure relationships. Here we ask what happens when a pairwise potential is conditioned on the fully defined geometry of interacting backbones fragments. We show that the resulting structure‐conditioned coupling energies more accurately reflect pair preferences as a function of structural contexts. These structure‐conditioned energies more reliably encode native sequence information and more highly correlate with experimentally determined coupling energies. Clustering a database of interaction motifs by structure results in ensembles of similar energies and clustering them by energy results in ensembles of similar structures. By comparing many pairs of interaction motifs and showing that structural similarity and energetic similarity go hand‐in‐hand, we provide a tangible link between modular sequence and structure elements. This link is applicable to structural modeling, and we show that scoring CASP models with structured‐conditioned energies results in substantially higher correlation with structural quality than scoring the same models with a contact potential. We conclude that structure‐conditioned coupling energies are a good way to model the impact of interaction geometry on second‐order sequence preferences.
Collapse
Affiliation(s)
- Jack Holland
- Department of Computer Science Dartmouth College Hanover New Hampshire USA
| | - Gevorg Grigoryan
- Department of Computer Science Dartmouth College Hanover New Hampshire USA
| |
Collapse
|
17
|
West J, Satapathy S, Whiten DR, Kelly M, Geraghty NJ, Proctor EJ, Sormanni P, Vendruscolo M, Buxbaum JN, Ranson M, Wilson MR. Neuroserpin and transthyretin are extracellular chaperones that preferentially inhibit amyloid formation. SCIENCE ADVANCES 2021; 7:eabf7606. [PMID: 34890220 PMCID: PMC8664251 DOI: 10.1126/sciadv.abf7606] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 10/21/2021] [Indexed: 06/13/2023]
Abstract
Neuroserpin is a secreted protease inhibitor known to inhibit amyloid formation by the Alzheimer’s beta peptide (Aβ). To test whether this effect was constrained to Aβ, we used a range of in vitro assays to demonstrate that neuroserpin inhibits amyloid formation by several different proteins and protects against the associated cytotoxicity but, unlike other known chaperones, has a poor ability to inhibit amorphous protein aggregation. Collectively, these results suggest that neuroserpin has an unusual chaperone selectivity for intermediates on the amyloid-forming pathway. Bioinformatics analyses identified a highly conserved 14-residue region containing an α helix shared between neuroserpin and the thyroxine-transport protein transthyretin, and we subsequently demonstrated that transthyretin also preferentially inhibits amyloid formation. Last, we used rationally designed neuroserpin mutants to demonstrate a direct involvement of the conserved 14-mer region in its chaperone activity. Identification of this conserved region may prove useful in the future design of anti-amyloid reagents.
Collapse
Affiliation(s)
- Jennifer West
- Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia
- Illawarra Health and Medical Research Institute, Northfields Avenue, Wollongong, NSW 2522, Australia
| | - Sandeep Satapathy
- Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia
- Illawarra Health and Medical Research Institute, Northfields Avenue, Wollongong, NSW 2522, Australia
| | - Daniel R. Whiten
- Kolling Institute of Medical Research, University of Sydney, NSW 2065, Australia
| | - Megan Kelly
- School of Medicine, University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia
| | - Nicholas J. Geraghty
- Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia
- Illawarra Health and Medical Research Institute, Northfields Avenue, Wollongong, NSW 2522, Australia
| | - Emma-Jayne Proctor
- Illawarra Health and Medical Research Institute, Northfields Avenue, Wollongong, NSW 2522, Australia
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Joel N. Buxbaum
- The Scripps Research Institute, La Jolla, CA, USA
- Protego Biopharma, La Jolla, CA, USA
| | - Marie Ranson
- Illawarra Health and Medical Research Institute, Northfields Avenue, Wollongong, NSW 2522, Australia
| | - Mark R. Wilson
- Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia
- Illawarra Health and Medical Research Institute, Northfields Avenue, Wollongong, NSW 2522, Australia
| |
Collapse
|
18
|
|
19
|
Wang F, Gnewou O, Wang S, Osinski T, Zuo X, Egelman EH, Conticello VP. Deterministic chaos in the self-assembly of β sheet nanotubes from an amphipathic oligopeptide. MATTER 2021; 4:3217-3231. [PMID: 34632372 PMCID: PMC8494133 DOI: 10.1016/j.matt.2021.06.037] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
The self-assembly of designed peptides into filaments and other higher-order structures has been the focus of intense interest because of the potential for creating new biomaterials and biomedical devices. These peptide assemblies have also been used as models for understanding biological processes, such as the pathological formation of amyloid. We investigate the assembly of an octapeptide sequence, Ac-FKFEFKFE-NH2, motivated by prior studies that demonstrated that this amphipathic β strand peptide self-assembled into fibrils and biocompatible hydrogels. Using high-resolution cryoelectron microscopy (cryo-EM), we are able to determine the atomic structure for two different coexisting forms of the fibrils, containing four and five β sandwich protofilaments, respectively. Surprisingly, the inner walls in both forms are parallel β sheets, while the outer walls are antiparallel β sheets. Our results demonstrate the chaotic nature of peptide self-assembly and illustrate the importance of cryo-EM structural analysis to understand the complex phase behavior of these materials at near-atomic resolution.
Collapse
Affiliation(s)
- Fengbin Wang
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - Ordy Gnewou
- Department of Chemistry, Emory University, Atlanta, GA 30322, USA
| | - Shengyuan Wang
- Department of Chemistry, Emory University, Atlanta, GA 30322, USA
| | - Tomasz Osinski
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - Xiaobing Zuo
- X-ray Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Edward H. Egelman
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
- Correspondence: (E.H.E.), (V.P.C.)
| | - Vincent P. Conticello
- Department of Chemistry, Emory University, Atlanta, GA 30322, USA
- The Robert P. Apkarian Integrated Electron Microscopy Core (IEMC), Emory University, Atlanta, GA 30322, USA
- Lead contact
- Correspondence: (E.H.E.), (V.P.C.)
| |
Collapse
|
20
|
Kamiński K, Ludwiczak J, Jasiński M, Bukala A, Madaj R, Szczepaniak K, Dunin-Horkawicz S. Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins. Brief Bioinform 2021; 23:6375059. [PMID: 34571541 PMCID: PMC8769691 DOI: 10.1093/bib/bbab371] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/04/2021] [Accepted: 08/22/2021] [Indexed: 11/15/2022] Open
Abstract
The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved βαβ motif shared by all Rossmann fold proteins. While Rossmann methyltransferases recognize only a single cofactor type, the S-adenosylmethionine, the oxidoreductases, depending on the family, bind nicotinamide (nicotinamide adenine dinucleotide, nicotinamide adenine dinucleotide phosphate) or flavin-based (flavin adenine dinucleotide) cofactors. In this study, we showed that despite its short length, the βαβ motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the βαβ motif. A benchmark on two independent test sets, one containing βαβ motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of the cofactor specificity, revealed the nearly perfect performance of the two methods. The Rossmann-toolbox protocols can be accessed via the webserver at https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox.
Collapse
Affiliation(s)
- Kamil Kamiński
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
| | - Jan Ludwiczak
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland.,Laboratory of Bioinformatics, Nencki Institute of Experimental Biology, Pasteura 3, 02-093 Warsaw, Poland
| | - Maciej Jasiński
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
| | - Adriana Bukala
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
| | - Rafal Madaj
- Centre of Molecular and Macromolecular Studies, Polish Academy of Sciences, Sienkiewicza 112, 90-363, Lodz, Poland
| | - Krzysztof Szczepaniak
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
| | - Stanisław Dunin-Horkawicz
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
| |
Collapse
|
21
|
Laniado J, Meador K, Yeates TO. A fragment-based protein interface design algorithm for symmetric assemblies. Protein Eng Des Sel 2021; 34:6269139. [PMID: 33955480 DOI: 10.1093/protein/gzab008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 03/08/2021] [Indexed: 11/13/2022] Open
Abstract
Theoretical and experimental advances in protein engineering have led to the creation of precisely defined, novel protein assemblies of great size and complexity, with diverse applications. One powerful approach involves designing a new attachment or binding interface between two simpler symmetric oligomeric protein components. The required methods of design, which present both similarities and key differences compared to problems in protein docking, remain challenging and are not yet routine. With the aim of more fully enabling this emerging area of protein material engineering, we developed a computer program, nanohedra, to introduce two key advances. First, we encoded in the program the construction rules (i.e. the search space parameters) that underlie all possible symmetric material constructions. Second, we developed algorithms for rapidly identifying favorable docking/interface arrangements based on tabulations of empirical patterns of known protein fragment-pair associations. As a result, the candidate poses that nanohedra generates for subsequent amino acid interface design appear highly native-like (at the protein backbone level), while simultaneously conforming to the exacting requirements for symmetry-based assembly. A retrospective computational analysis of successful vs failed experimental studies supports the expectation that this should improve the success rate for this challenging area of protein engineering.
Collapse
Affiliation(s)
- Joshua Laniado
- UCLA Molecular Biology Institute, Los Angeles, CA 90095, USA
| | - Kyle Meador
- UCLA Department of Chemistry and Biochemistry, Los Angeles, CA 90095, USA
| | - Todd O Yeates
- UCLA Molecular Biology Institute, Los Angeles, CA 90095, USA.,UCLA Department of Chemistry and Biochemistry, Los Angeles, CA 90095, USA.,UCLA DOE Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA
| |
Collapse
|
22
|
Schoeder CT, Schmitz S, Adolf-Bryfogle J, Sevy AM, Finn JA, Sauer MF, Bozhanova NG, Mueller BK, Sangha AK, Bonet J, Sheehan JH, Kuenze G, Marlow B, Smith ST, Woods H, Bender BJ, Martina CE, Del Alamo D, Kodali P, Gulsevin A, Schief WR, Correia BE, Crowe JE, Meiler J, Moretti R. Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design. Biochemistry 2021; 60:825-846. [PMID: 33705117 PMCID: PMC7992133 DOI: 10.1021/acs.biochem.0c00912] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
![]()
Structure-based antibody
and antigen design has advanced greatly
in recent years, due not only to the increasing availability of experimentally
determined structures but also to improved computational methods for
both prediction and design. Constant improvements in performance within
the Rosetta software suite for biomolecular modeling have given rise
to a greater breadth of structure prediction, including docking and
design application cases for antibody and antigen modeling. Here,
we present an overview of current protocols for antibody and antigen
modeling using Rosetta and exemplify those by detailed tutorials originally
developed for a Rosetta workshop at Vanderbilt University. These tutorials
cover antibody structure prediction, docking, and design and antigen
design strategies, including the addition of glycans in Rosetta. We
expect that these materials will allow novice users to apply Rosetta
in their own projects for modeling antibodies and antigens.
Collapse
Affiliation(s)
- Clara T Schoeder
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Samuel Schmitz
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California 92037, United States.,IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Alexander M Sevy
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee 37232-0301, United States.,Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee 37232-0417, United States
| | - Jessica A Finn
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee 37232-0417, United States.,Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Marion F Sauer
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee 37232-0301, United States.,Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee 37232-0417, United States
| | - Nina G Bozhanova
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Benjamin K Mueller
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Amandeep K Sangha
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Jonathan H Sheehan
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Institute for Drug Discovery, University Leipzig Medical School, 04103 Leipzig, Germany
| | - Brennica Marlow
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee 37232-0301, United States
| | - Shannon T Smith
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee 37232-0301, United States
| | - Hope Woods
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee 37232-0301, United States
| | - Brian J Bender
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Cristina E Martina
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Diego Del Alamo
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee 37232-0301, United States
| | - Pranav Kodali
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Alican Gulsevin
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - William R Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California 92037, United States.,IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - James E Crowe
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee 37232-0417, United States.,Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States.,Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States.,Institute for Drug Discovery, University Leipzig Medical School, 04103 Leipzig, Germany
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| |
Collapse
|
23
|
Wang F, Gnewou O, Modlin C, Beltran LC, Xu C, Su Z, Juneja P, Grigoryan G, Egelman EH, Conticello VP. Structural analysis of cross α-helical nanotubes provides insight into the designability of filamentous peptide nanomaterials. Nat Commun 2021; 12:407. [PMID: 33462223 PMCID: PMC7814010 DOI: 10.1038/s41467-020-20689-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 12/02/2020] [Indexed: 12/12/2022] Open
Abstract
The exquisite structure-function correlations observed in filamentous protein assemblies provide a paradigm for the design of synthetic peptide-based nanomaterials. However, the plasticity of quaternary structure in sequence-space and the lability of helical symmetry present significant challenges to the de novo design and structural analysis of such filaments. Here, we describe a rational approach to design self-assembling peptide nanotubes based on controlling lateral interactions between protofilaments having an unusual cross-α supramolecular architecture. Near-atomic resolution cryo-EM structural analysis of seven designed nanotubes provides insight into the designability of interfaces within these synthetic peptide assemblies and identifies a non-native structural interaction based on a pair of arginine residues. This arginine clasp motif can robustly mediate cohesive interactions between protofilaments within the cross-α nanotubes. The structure of the resultant assemblies can be controlled through the sequence and length of the peptide subunits, which generates synthetic peptide filaments of similar dimensions to flagella and pili.
Collapse
Affiliation(s)
- Fengbin Wang
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Ordy Gnewou
- Department of Chemistry, Emory University, Atlanta, GA, 30322, USA
| | - Charles Modlin
- Department of Chemistry, Emory University, Atlanta, GA, 30322, USA
| | - Leticia C Beltran
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Chunfu Xu
- Department of Chemistry, Emory University, Atlanta, GA, 30322, USA
| | - Zhangli Su
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Puneet Juneja
- The Robert P. Apkarian Integrated Electron Microscopy Core (IEMC), Emory University, Atlanta, GA, 30322, USA
| | - Gevorg Grigoryan
- Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA.,Department of Biological Sciences, Dartmouth College, Hanover, NH, 03755, USA
| | - Edward H Egelman
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Vincent P Conticello
- Department of Chemistry, Emory University, Atlanta, GA, 30322, USA. .,The Robert P. Apkarian Integrated Electron Microscopy Core (IEMC), Emory University, Atlanta, GA, 30322, USA.
| |
Collapse
|
24
|
Mann SI, Nayak A, Gassner GT, Therien MJ, DeGrado WF. De Novo Design, Solution Characterization, and Crystallographic Structure of an Abiological Mn-Porphyrin-Binding Protein Capable of Stabilizing a Mn(V) Species. J Am Chem Soc 2021; 143:252-259. [PMID: 33373215 DOI: 10.1021/jacs.0c10136] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
De novo protein design offers the opportunity to test our understanding of how metalloproteins perform difficult transformations. Attaining high-resolution structural information is critical to understanding how such designs function. There have been many successes in the design of porphyrin-binding proteins; however, crystallographic characterization has been elusive, limiting what can be learned from such studies as well as the extension to new functions. Moreover, formation of highly oxidizing high-valent intermediates poses design challenges that have not been previously implemented: (1) purposeful design of substrate/oxidant access to the binding site and (2) limiting deleterious oxidation of the protein scaffold. Here we report the first crystallographically characterized porphyrin-binding protein that was programmed to not only bind a synthetic Mn-porphyrin but also maintain binding site access to form high-valent oxidation states. We explicitly designed a binding site with accessibility to dioxygen units in the open coordination site of the Mn center. In solution, the protein is capable of accessing a high-valent Mn(V)-oxo species which can transfer an O atom to a thioether substrate. The crystallographic structure is within 0.6 Å of the design and indeed contained an aquo ligand with a second water molecule stabilized by hydrogen bonding to a Gln side chain in the active site, offering a structural explanation for the observed reactivity.
Collapse
Affiliation(s)
- Samuel I Mann
- Department of Pharmaceutical Chemistry and the Cardiovascular Research Institute, University of California at San Francisco, San Francisco, California 94158-9001, United States
| | - Animesh Nayak
- Department of Chemistry, Duke University, Durham, North Carolina 27708, United States
| | - George T Gassner
- Department of Chemistry and Biochemistry, San Francisco State University, San Francisco, California 94132, United States
| | - Michael J Therien
- Department of Chemistry, Duke University, Durham, North Carolina 27708, United States
| | - William F DeGrado
- Department of Pharmaceutical Chemistry and the Cardiovascular Research Institute, University of California at San Francisco, San Francisco, California 94158-9001, United States
| |
Collapse
|
25
|
Abstract
We describe the de novo design of an allosterically regulated protein, which comprises two tightly coupled domains. One domain is based on the DF (Due Ferri in Italian or two-iron in English) family of de novo proteins, which have a diiron cofactor that catalyzes a phenol oxidase reaction, while the second domain is based on PS1 (Porphyrin-binding Sequence), which binds a synthetic Zn-porphyrin (ZnP). The binding of ZnP to the original PS1 protein induces changes in structure and dynamics, which we expected to influence the catalytic rate of a fused DF domain when appropriately coupled. Both DF and PS1 are four-helix bundles, but they have distinct bundle architectures. To achieve tight coupling between the domains, they were connected by four helical linkers using a computational method to discover the most designable connections capable of spanning the two architectures. The resulting protein, DFP1 (Due Ferri Porphyrin), bound the two cofactors in the expected manner. The crystal structure of fully reconstituted DFP1 was also in excellent agreement with the design, and it showed the ZnP cofactor bound over 12 Å from the dimetal center. Next, a substrate-binding cleft leading to the diiron center was introduced into DFP1. The resulting protein acts as an allosterically modulated phenol oxidase. Its Michaelis-Menten parameters were strongly affected by the binding of ZnP, resulting in a fourfold tighter K m and a 7-fold decrease in k cat These studies establish the feasibility of designing allosterically regulated catalytic proteins, entirely from scratch.
Collapse
|
26
|
Pan X, Thompson MC, Zhang Y, Liu L, Fraser JS, Kelly MJS, Kortemme T. Expanding the space of protein geometries by computational design of de novo fold families. Science 2020; 369:1132-1136. [PMID: 32855341 DOI: 10.1126/science.abc0881] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 07/14/2020] [Indexed: 01/03/2023]
Abstract
Naturally occurring proteins vary the precise geometries of structural elements to create distinct shapes optimal for function. We present a computational design method, loop-helix-loop unit combinatorial sampling (LUCS), that mimics nature's ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space. Biophysical characterization showed that 17 (38%) of 45 tested LUCS designs encompassing two different structural topologies were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely matched the designs. LUCS greatly expands the designable structure space and offers a new paradigm for designing proteins with tunable geometries that may be customizable for novel functions.
Collapse
Affiliation(s)
- Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA. .,UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA, USA
| | - Michael C Thompson
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Yang Zhang
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Lin Liu
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA
| | - Mark J S Kelly
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA. .,UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
| |
Collapse
|
27
|
Polizzi NF, DeGrado WF. A defined structural unit enables de novo design of small-molecule-binding proteins. Science 2020; 369:1227-1233. [PMID: 32883865 PMCID: PMC7526616 DOI: 10.1126/science.abb8330] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Accepted: 06/29/2020] [Indexed: 12/21/2022]
Abstract
The de novo design of proteins that bind highly functionalized small molecules represents a great challenge. To enable computational design of binders, we developed a unit of protein structure-a van der Mer (vdM)-that maps the backbone of each amino acid to statistically preferred positions of interacting chemical groups. Using vdMs, we designed six de novo proteins to bind the drug apixaban; two bound with low and submicromolar affinity. X-ray crystallography and mutagenesis confirmed a structure with a precisely designed cavity that forms favorable interactions in the drug-protein complex. vdMs may enable design of functional proteins for applications in sensing, medicine, and catalysis.
Collapse
Affiliation(s)
- Nicholas F Polizzi
- Department of Pharmaceutical Chemistry, Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - William F DeGrado
- Department of Pharmaceutical Chemistry, Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
| |
Collapse
|
28
|
Leone L, Chino M, Nastri F, Maglio O, Pavone V, Lombardi A. Mimochrome, a metalloporphyrin‐based catalytic Swiss knife†. Biotechnol Appl Biochem 2020; 67:495-515. [DOI: 10.1002/bab.1985] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 07/09/2020] [Indexed: 12/20/2022]
Affiliation(s)
- Linda Leone
- Department of Chemical Sciences University of Napoli “Federico II” Napoli Italy
| | - Marco Chino
- Department of Chemical Sciences University of Napoli “Federico II” Napoli Italy
| | - Flavia Nastri
- Department of Chemical Sciences University of Napoli “Federico II” Napoli Italy
| | - Ornella Maglio
- Department of Chemical Sciences University of Napoli “Federico II” Napoli Italy
- IBB ‐ National Research Council Napoli Italy
| | - Vincenzo Pavone
- Department of Chemical Sciences University of Napoli “Federico II” Napoli Italy
| | - Angela Lombardi
- Department of Chemical Sciences University of Napoli “Federico II” Napoli Italy
| |
Collapse
|
29
|
Restriction of S-adenosylmethionine conformational freedom by knotted protein binding sites. PLoS Comput Biol 2020; 16:e1007904. [PMID: 32453784 PMCID: PMC7319350 DOI: 10.1371/journal.pcbi.1007904] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 06/26/2020] [Accepted: 04/23/2020] [Indexed: 02/07/2023] Open
Abstract
S-adenosylmethionine (SAM) is one of the most important enzyme substrates. It is vital for the function of various proteins, including large group of methyltransferases (MTs). Intriguingly, some bacterial and eukaryotic MTs, while catalysing the same reaction, possess significantly different topologies, with the former being a knotted one. Here, we conducted a comprehensive analysis of SAM conformational space and factors that affect its vastness. We investigated SAM in two forms: free in water (via NMR studies and explicit solvent simulations) and bound to proteins (based on all data available in the PDB and on all-atom molecular dynamics simulations in water). We identified structural descriptors—angles which show the major differences in SAM conformation between unknotted and knotted methyltransferases. Moreover, we report that this is caused mainly by a characteristic for knotted MTs compact binding site formed by the knot and the presence of adenine-binding loop. Additionally, we elucidate conformational restrictions imposed on SAM molecules by other protein groups in comparison to conformational space in water. The topology of a folded polypeptide chain has great impact on the resulting protein function and its interaction with ligands. Interestingly, topological constraints appear to affect binding of one of the most ubiquitous substrates in the cell, S-adenosylmethionine (SAM), to its target proteins. Here, we demonstrate how binding sites of specific proteins restrict SAM conformational freedom in comparison to its unbound state, with a special interest in proteins with non-trivial topology, including an exciting group of knotted methyltransferases. Using a vast array of computational methods combined with NMR experiments, we identify key structural features of knotted methyltransferases that impose unorthodox SAM conformations. We compare them with the characteristics of standard, unknotted SAM binding proteins. These results are significant for understanding differences between analogous, yet topologically different enzymes, as well as for future rational drug design.
Collapse
|
30
|
Sesterhenn F, Yang C, Bonet J, Cramer JT, Wen X, Wang Y, Chiang CI, Abriata LA, Kucharska I, Castoro G, Vollers SS, Galloux M, Dheilly E, Rosset S, Corthésy P, Georgeon S, Villard M, Richard CA, Descamps D, Delgado T, Oricchio E, Rameix-Welti MA, Más V, Ervin S, Eléouët JF, Riffault S, Bates JT, Julien JP, Li Y, Jardetzky T, Krey T, Correia BE. De novo protein design enables the precise induction of RSV-neutralizing antibodies. Science 2020; 368:eaay5051. [PMID: 32409444 PMCID: PMC7391827 DOI: 10.1126/science.aay5051] [Citation(s) in RCA: 115] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 01/30/2020] [Accepted: 04/08/2020] [Indexed: 12/27/2022]
Abstract
De novo protein design has been successful in expanding the natural protein repertoire. However, most de novo proteins lack biological function, presenting a major methodological challenge. In vaccinology, the induction of precise antibody responses remains a cornerstone for next-generation vaccines. Here, we present a protein design algorithm called TopoBuilder, with which we engineered epitope-focused immunogens displaying complex structural motifs. In both mice and nonhuman primates, cocktails of three de novo-designed immunogens induced robust neutralizing responses against the respiratory syncytial virus. Furthermore, the immunogens refocused preexisting antibody responses toward defined neutralization epitopes. Overall, our design approach opens the possibility of targeting specific epitopes for the development of vaccines and therapeutic antibodies and, more generally, will be applicable to the design of de novo proteins displaying complex functional motifs.
Collapse
Affiliation(s)
- Fabian Sesterhenn
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
| | - Che Yang
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
| | - Johannes T Cramer
- Institute of Virology, Hannover Medical School, Hannover 30625, Germany
| | - Xiaolin Wen
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yimeng Wang
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD 20850, USA
| | - Chi-I Chiang
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD 20850, USA
| | - Luciano A Abriata
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
| | - Iga Kucharska
- Program in Molecular Medicine, Hospital for Sick Children Research Institute, Toronto, Ontario M5G 0A4, Canada
- Departments of Biochemistry and Immunology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Giacomo Castoro
- Institute of Virology, Hannover Medical School, Hannover 30625, Germany
| | - Sabrina S Vollers
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
| | - Marie Galloux
- Université Paris-Saclay, INRAE, UVSQ, VIM, 78350 Jouy-en-Josas, France
| | - Elie Dheilly
- Swiss Institute for Experimental Cancer Research, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Stéphane Rosset
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
| | - Patricia Corthésy
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
| | - Sandrine Georgeon
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
| | - Mélanie Villard
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
| | | | - Delphyne Descamps
- Université Paris-Saclay, INRAE, UVSQ, VIM, 78350 Jouy-en-Josas, France
| | - Teresa Delgado
- Centro Nacional de Microbiología, Instituto de Salud Carlos III, 28220 Madrid, Spain
| | - Elisa Oricchio
- Swiss Institute for Experimental Cancer Research, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | | | - Vicente Más
- Centro Nacional de Microbiología, Instituto de Salud Carlos III, 28220 Madrid, Spain
| | - Sean Ervin
- Wake Forest Baptist Medical Center, Winston Salem, NC 27157, USA
| | | | - Sabine Riffault
- Université Paris-Saclay, INRAE, UVSQ, VIM, 78350 Jouy-en-Josas, France
| | - John T Bates
- University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Jean-Philippe Julien
- Program in Molecular Medicine, Hospital for Sick Children Research Institute, Toronto, Ontario M5G 0A4, Canada
- Departments of Biochemistry and Immunology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Yuxing Li
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD 20850, USA
- Department of Microbiology and Immunology & Center of Biomolecular Therapeutics, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Theodore Jardetzky
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Thomas Krey
- Institute of Virology, Hannover Medical School, Hannover 30625, Germany
- German Center for Infection Research (DZIF), 38124 Braunschweig, Germany
- Institute of Biochemistry, Center of Structural and Cell Biology in Medicine, University of Luebeck, D-23538 Luebeck, Germany
- Excellence Cluster 2155 RESIST, Hannover Medical School, 30625 Hannover, Germany
- Centre for Structural Systems Biology (CSSB), 22607 Hamburg, Germany
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
| |
Collapse
|
31
|
Medina A, Triviño J, Borges RJ, Millán C, Usón I, Sammito MD. ALEPH: a network-oriented approach for the generation of fragment-based libraries and for structure interpretation. Acta Crystallogr D Struct Biol 2020; 76:193-208. [PMID: 32133985 PMCID: PMC7057218 DOI: 10.1107/s2059798320001679] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 02/05/2020] [Indexed: 11/17/2022] Open
Abstract
The analysis of large structural databases reveals general features and relationships among proteins, providing useful insight. A different approach is required to characterize ubiquitous secondary-structure elements, where flexibility is essential in order to capture small local differences. The ALEPH software is optimized for the analysis and the extraction of small protein folds by relying on their geometry rather than on their sequence. The annotation of the structural variability of a given fold provides valuable information for fragment-based molecular-replacement methods, in which testing alternative model hypotheses can succeed in solving difficult structures when no homology models are available or are successful. ARCIMBOLDO_BORGES combines the use of composite secondary-structure elements as a search model with density modification and tracing to reveal the rest of the structure when both steps are successful. This phasing method relies on general fold libraries describing variations around a given pattern of β-sheets and helices extracted using ALEPH. The program introduces characteristic vectors defined from the main-chain atoms as a way to describe the geometrical properties of the structure. ALEPH encodes structural properties in a graph network, the exploration of which allows secondary-structure annotation, decomposition of a structure into small compact folds, generation of libraries of models representing a variation of a given fold and finally superposition of these folds onto a target structure. These functions are available through a graphical interface designed to interactively show the results of structure manipulation, annotation, fold decomposition, clustering and library generation. ALEPH can produce pictures of the graphs, structures and folds for publication purposes.
Collapse
Grants
- 790122 H2020 Marie Skłodowska-Curie Actions
- BES-2017-080368 Ministerio de Economía, Industria y Competitividad, Gobierno de España
- BES-2015-071397 Ministerio de Economía, Industria y Competitividad, Gobierno de España
- BIO2015-64216-P Ministerio de Economía, Industria y Competitividad, Gobierno de España
- BIO2013-49604-EXP Ministerio de Economía, Industria y Competitividad, Gobierno de España
- MDM2014-0435-01 Ministerio de Economía, Industria y Competitividad, Gobierno de España
- 16/24191-8 Fundação de Amparo à Pesquisa do Estado de São Paulo
- 17/13485-3 Fundação de Amparo à Pesquisa do Estado de São Paulo
Collapse
Affiliation(s)
- Ana Medina
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain
| | - Josep Triviño
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain
| | - Rafael J. Borges
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain
- Departamento de Física e Biofísica, Instituto de Biociências, Universidade Estadual Paulista (UNESP), Botucatu-SP 18618-689, Brazil
| | - Claudia Millán
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain
| | - Isabel Usón
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain
- ICREA, Institució Catalana de Recerca i Estudis Avançats, Passeig Lluís Companys 23, 08003 Barcelona, Spain
| | - Massimo D. Sammito
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, England
| |
Collapse
|
32
|
Zhou J, Panaitiu AE, Grigoryan G. A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures. Proc Natl Acad Sci U S A 2020; 117:1059-1068. [PMID: 31892539 PMCID: PMC6969538 DOI: 10.1073/pnas.1908723117] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Current state-of-the-art approaches to computational protein design (CPD) aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a reliable general solution to CPD has yet to be found. Here, we propose a design framework-one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of interatomic interactions. We carry out extensive computational analyses and an experimental validation for our method. Our results strongly argue that the Protein Data Bank is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. Because our method is likely to have orthogonal strengths relative to existing techniques, it could represent an important step toward removing remaining barriers to robust CPD.
Collapse
Affiliation(s)
- Jianfu Zhou
- Department of Computer Science, Dartmouth College, Hanover, NH 03755
| | | | - Gevorg Grigoryan
- Department of Computer Science, Dartmouth College, Hanover, NH 03755;
- Department of Biological Sciences, Dartmouth College, Hanover, NH 03755
| |
Collapse
|
33
|
Hwang JY, Holland JE, Valenteros KB, Sun Y, Usherwood YK, Verissimo AF, McLellan JS, Grigoryan G, Usherwood EJ. Dissociating STAT4 and STAT5 Signaling Inhibitory Functions of SOCS3: Effects on CD8 T Cell Responses. Immunohorizons 2019; 3:547-558. [PMID: 31748225 PMCID: PMC7178138 DOI: 10.4049/immunohorizons.1800075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 10/31/2019] [Indexed: 12/27/2022] Open
Abstract
Cytokines are critical for guiding the differentiation of T lymphocytes to perform specialized tasks in the immune response. Developing strategies to manipulate cytokine-signaling pathways holds promise to program T cell differentiation toward the most therapeutically useful direction. Suppressor of cytokine signaling (SOCS) proteins are attractive targets, as they effectively inhibit undesirable cytokine signaling. However, these proteins target multiple signaling pathways, some of which we may need to remain uninhibited. SOCS3 inhibits IL-12 signaling but also inhibits the IL-2–signaling pathway. In this study, we use computational protein design based on SOCS3 and JAK crystal structures to engineer a mutant SOCS3 with altered specificity. We generated a mutant SOCS3 designed to ablate interactions with JAK1 but maintain interactions with JAK2. We show that this mutant does indeed ablate JAK1 inhibition, although, unexpectedly, it still coimmunoprecipitates with JAK1 and does so to a greater extent than with JAK2. When expressed in CD8 T cells, mutant SOCS3 preserved inhibition of JAK2-dependent STAT4 phosphorylation following IL-12 treatment. However, inhibition of STAT phosphorylation was ablated following stimulation with JAK1-dependent cytokines IL-2, IFN-α, and IL-21. Wild-type SOCS3 inhibited CD8 T cell expansion in vivo and induced a memory precursor phenotype. In vivo T cell expansion was restored by expression of the mutant SOCS3, and this also reverted the phenotype toward effector T cell differentiation. These data show that SOCS proteins can be engineered to fine-tune their specificity, and this can exert important changes to T cell biology.
Collapse
Affiliation(s)
- Ji Young Hwang
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth College, Lebanon, NH 03755
| | - John E Holland
- Department of Computer Science, Dartmouth College, Hanover, NH 03755
| | - Kristine B Valenteros
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth College, Lebanon, NH 03755
| | - Yanbo Sun
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth College, Lebanon, NH 03755
| | - Young-Kwang Usherwood
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth College, Lebanon, NH 03755
| | - Andreia F Verissimo
- Institute for Molecular Targeting, Geisel School of Medicine at Dartmouth College, Hanover, NH 03755; and
| | - Jason S McLellan
- Department of Biochemistry and Cell Biology, Geisel School of Medicine at Dartmouth College, Hanover, NH 03755
| | - Gevorg Grigoryan
- Department of Computer Science, Dartmouth College, Hanover, NH 03755
| | - Edward J Usherwood
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth College, Lebanon, NH 03755;
| |
Collapse
|
34
|
HALLEN MARKA, DONALD BRUCER. Protein Design by Provable Algorithms. COMMUNICATIONS OF THE ACM 2019; 62:76-84. [PMID: 31607753 PMCID: PMC6788629 DOI: 10.1145/3338124] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Protein design algorithms can leverage provable guarantees of accuracy to provide new insights and unique optimized molecules.
Collapse
Affiliation(s)
- MARK A. HALLEN
- Research assistant professor at the Toyota Technological Institute at Chicago, IL, USA
| | - BRUCE R. DONALD
- James B. Duke Professor of Computer Science at Duke University, as well as a
professor of chemistry and biochemistry in the Duke University Medical
Center, Durham, NC, USA
| |
Collapse
|
35
|
Mravic M, Thomaston JL, Tucker M, Solomon PE, Liu L, DeGrado WF. Packing of apolar side chains enables accurate design of highly stable membrane proteins. Science 2019; 363:1418-1423. [PMID: 30923216 DOI: 10.1126/science.aav7541] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 02/28/2019] [Indexed: 12/21/2022]
Abstract
The features that stabilize the structures of membrane proteins remain poorly understood. Polar interactions contribute modestly, and the hydrophobic effect contributes little to the energetics of apolar side-chain packing in membranes. Disruption of steric packing can destabilize the native folds of membrane proteins, but is packing alone sufficient to drive folding in lipids? If so, then membrane proteins stabilized by this feature should be readily designed and structurally characterized-yet this has not been achieved. Through simulation of the natural protein phospholamban and redesign of variants, we define a steric packing code underlying its assembly. Synthetic membrane proteins designed using this code and stabilized entirely by apolar side chains conform to the intended fold. Although highly stable, the steric complementarity required for their folding is surprisingly stringent. Structural informatics shows that the designed packing motif recurs across the proteome, emphasizing a prominent role for precise apolar packing in membrane protein folding, stabilization, and evolution.
Collapse
Affiliation(s)
- Marco Mravic
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jessica L Thomaston
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Maxwell Tucker
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Paige E Solomon
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Lijun Liu
- State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China. .,DLX Scientific, Lawrence, KS 66049, USA
| | - William F DeGrado
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
| |
Collapse
|
36
|
Thomaston JL, Wu Y, Polizzi N, Liu L, Wang J, DeGrado WF. X-ray Crystal Structure of the Influenza A M2 Proton Channel S31N Mutant in Two Conformational States: An Open and Shut Case. J Am Chem Soc 2019; 141:11481-11488. [PMID: 31184871 DOI: 10.1021/jacs.9b02196] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The amantadine-resistant S31N mutant of the influenza A M2 proton channel has become prevalent in currently circulating viruses. Here, we have solved an X-ray crystal structure of M2(22-46) S31N that contains two distinct conformational states within its asymmetric unit. This structure reveals the mechanism of adamantane resistance in both conformational states of the M2 channel. In the Inwardopen conformation, the mutant Asn31 side chain faces the channel pore and sterically blocks the adamantane binding site. In the Inwardclosed conformation, Asn31 forms hydrogen bonds with carbonyls at the monomer-monomer interface, which twists the monomer helices and constricts the channel pore at the drug binding site. We also examine M2(19-49) WT and S31N using solution NMR spectroscopy and show that distribution of the two conformational states is dependent on both detergent choice and experimental pH.
Collapse
Affiliation(s)
- Jessica L Thomaston
- Department of Pharmaceutical Chemistry , University of California , San Francisco , California 94158 , United States
| | - Yibing Wu
- Department of Pharmaceutical Chemistry , University of California , San Francisco , California 94158 , United States
| | - Nicholas Polizzi
- Department of Pharmaceutical Chemistry , University of California , San Francisco , California 94158 , United States
| | - Lijun Liu
- State Key Laboratory of Chemical Oncogenomics , Peking University Shenzhen Graduate School , Shenzhen 518055 , China.,DLX Scientific , Lawrence , Kansas 66049 , United States
| | - Jun Wang
- Department of Pharmacology and Toxicology, College of Pharmacy , University of Arizona , Tucson , Arizona 85721 , United States
| | - William F DeGrado
- Department of Pharmaceutical Chemistry , University of California , San Francisco , California 94158 , United States
| |
Collapse
|
37
|
Rosetta FunFolDes - A general framework for the computational design of functional proteins. PLoS Comput Biol 2018; 14:e1006623. [PMID: 30452434 PMCID: PMC6277116 DOI: 10.1371/journal.pcbi.1006623] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 12/03/2018] [Accepted: 11/06/2018] [Indexed: 01/11/2023] Open
Abstract
The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability. The low success rates of computational design protocols and the extensive in vitro optimization often required, highlight the challenge of designing proteins that perform essential biochemical functions, such as binding or catalysis. One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins. The structural complexity of the functional motif largely determines how readily one can find host protein structures that are "designable", meaning that are likely to present the functional motif in the desired conformation. One promising route to enhance the "designability" of protein structures is to allow backbone flexibility. Here, we present a computational approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins-Rosetta Functional Folding and Design (FunFolDes). We performed extensive computational benchmarks, where we observed that the enforcement of functional requirements resulted in designs distant from the global energetic minimum of the protein. An observation consistent with several experimental studies that have revealed function-stability tradeoffs. To test the design capabilities of FunFolDes we transplanted two viral epitopes into distant structural templates including one de novo "functionless" fold, which represent two typical challenges where the designability problem arises. The designed proteins were experimentally characterized showing high binding affinities to monoclonal antibodies, making them valuable candidates for vaccine design endeavors. Overall, we present an accessible strategy to repurpose old protein folds for new functions. This may lead to important improvements on the computational design of proteins, with structurally complex functional sites, that can perform elaborate biochemical functions related to binding and catalysis.
Collapse
|
38
|
Hallen MA, Donald BR. CATS (Coordinates of Atoms by Taylor Series): protein design with backbone flexibility in all locally feasible directions. Bioinformatics 2018; 33:i5-i12. [PMID: 28882005 PMCID: PMC5870559 DOI: 10.1093/bioinformatics/btx277] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Motivation When proteins mutate or bind to ligands, their backbones often move significantly, especially in loop regions. Computational protein design algorithms must model these motions in order to accurately optimize protein stability and binding affinity. However, methods for backbone conformational search in design have been much more limited than for sidechain conformational search. This is especially true for combinatorial protein design algorithms, which aim to search a large sequence space efficiently and thus cannot rely on temporal simulation of each candidate sequence. Results We alleviate this difficulty with a new parameterization of backbone conformational space, which represents all degrees of freedom of a specified segment of protein chain that maintain valid bonding geometry (by maintaining the original bond lengths and angles and ω dihedrals). In order to search this space, we present an efficient algorithm, CATS, for computing atomic coordinates as a function of our new continuous backbone internal coordinates. CATS generalizes the iMinDEE and EPIC protein design algorithms, which model continuous flexibility in sidechain dihedrals, to model continuous, appropriately localized flexibility in the backbone dihedrals ϕ and ψ as well. We show using 81 test cases based on 29 different protein structures that CATS finds sequences and conformations that are significantly lower in energy than methods with less or no backbone flexibility do. In particular, we show that CATS can model the viability of an antibody mutation known experimentally to increase affinity, but that appears sterically infeasible when modeled with less or no backbone flexibility. Availability and implementation Our code is available as free software at https://github.com/donaldlab/OSPREY_refactor. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Mark A Hallen
- Department of Computer Science, Duke University, Durham, NC, USA.,Toyota Technological Institute at Chicago, Chicago, IL, USA
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC, USA.,Department of Chemistry, Duke University, Durham, NC, USA.,Department of Biochemistry, Duke University Medical Center, Durham, NC, USA
| |
Collapse
|
39
|
Lai JI, Verma D, Bailey-Kellogg C, Ackerman ME. Towards conformational fidelity of a quaternary HIV-1 epitope: computational design and directed evolution of a minimal V1V2 antigen. Protein Eng Des Sel 2018; 31:121-133. [PMID: 29897567 PMCID: PMC6030936 DOI: 10.1093/protein/gzy010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 04/16/2018] [Accepted: 04/24/2018] [Indexed: 12/11/2022] Open
Abstract
Structure-based approaches to antigen design utilize insights from antibody (Ab):antigen interactions and a refined understanding of protective Ab responses to engineer novel antigens presenting epitopes with conformations relevant to eliciting or discovering protective humoral responses. For human immunodeficiency virus-1 (HIV-1), one model of protection is provided by broadly neutralizing Abs (bnAbs) against epitopes present in the closed prefusion trimeric conformation of HIV-1 envelope glycoprotein, such as the variable loops 1-2 (V1V2) apex. Here, computational design and directed evolution yielded a novel V1V2 sequence variant with potential utility for inclusion in an immunogen for eliciting bnAbs, or as an epitope probe for their detection. The computational design goal was to engineer a minimal single-chain antigen with three copies of the V1V2 loops to support maintenance of closed prefusion V1V2 trimeric conformation and presentation of bnAb epitopes. Via directed evolution of this computationally designed single-chain antigen, we isolated a V1V2 sequence variant that in monomeric form exhibited preferential recognition by quaternary-preferring and conformation-dependent mAbs. Structural context and transferability of this phenotype to V1V2 sequences from all strains of HIV-1 tested suggest a conformation-stabilizing effect. This example demonstrates the potential utility of computational design and directed evolution-based protein engineering strategies to develop minimal, conformation-stabilized epitope-specific antigens.
Collapse
Affiliation(s)
- Jennifer I Lai
- Thayer School of Engineering, Dartmouth College, 14 Engineering Dr, Hanover NH, USA
| | - Deeptak Verma
- Department of Computer Science, Dartmouth College, 9 Maynard St, Hanover NH, USA
| | - Chris Bailey-Kellogg
- Department of Computer Science, Dartmouth College, 9 Maynard St, Hanover NH, USA
| | - Margaret E Ackerman
- Thayer School of Engineering, Dartmouth College, 14 Engineering Dr, Hanover NH, USA
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, 1 Medical Center Dr, Lebanon NH, USA
| |
Collapse
|
40
|
Joh NH, Grigoryan G, Wu Y, DeGrado WF. Design of self-assembling transmembrane helical bundles to elucidate principles required for membrane protein folding and ion transport. Philos Trans R Soc Lond B Biol Sci 2018. [PMID: 28630154 DOI: 10.1098/rstb.2016.0214] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Ion transporters and channels are able to identify and act on specific substrates among myriads of ions and molecules critical to cellular processes, such as homeostasis, cell signalling, nutrient influx and drug efflux. Recently, we designed Rocker, a minimalist model for Zn2+/H+ co-transport. The success of this effort suggests that de novo membrane protein design has now come of age so as to serve a key approach towards probing the determinants of membrane protein folding, assembly and function. Here, we review general principles that can be used to design membrane proteins, with particular reference to helical assemblies with transport function. We also provide new functional and NMR data that probe the dynamic mechanism of conduction through Rocker.This article is part of the themed issue 'Membrane pores: from structure and assembly, to medicine and technology'.
Collapse
Affiliation(s)
- Nathan H Joh
- Department of Pharmaceutical Chemistry, Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Gevorg Grigoryan
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.,Department of Biological Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - Yibing Wu
- Department of Pharmaceutical Chemistry, Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - William F DeGrado
- Department of Pharmaceutical Chemistry, Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| |
Collapse
|
41
|
Ojewole AA, Jou JD, Fowler VG, Donald BR. BBK* (Branch and Bound Over K*): A Provable and Efficient Ensemble-Based Protein Design Algorithm to Optimize Stability and Binding Affinity Over Large Sequence Spaces. J Comput Biol 2018; 25:726-739. [PMID: 29641249 DOI: 10.1089/cmb.2017.0267] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Computational protein design (CPD) algorithms that compute binding affinity, Ka, search for sequences with an energetically favorable free energy of binding. Recent work shows that three principles improve the biological accuracy of CPD: ensemble-based design, continuous flexibility of backbone and side-chain conformations, and provable guarantees of accuracy with respect to the input. However, previous methods that use all three design principles are single-sequence (SS) algorithms, which are very costly: linear in the number of sequences and thus exponential in the number of simultaneously mutable residues. To address this computational challenge, we introduce BBK*, a new CPD algorithm whose key innovation is the multisequence (MS) bound: BBK* efficiently computes a single provable upper bound to approximate Ka for a combinatorial number of sequences, and avoids SS computation for all provably suboptimal sequences. Thus, to our knowledge, BBK* is the first provable, ensemble-based CPD algorithm to run in time sublinear in the number of sequences. Computational experiments on 204 protein design problems show that BBK* finds the tightest binding sequences while approximating Ka for up to 105-fold fewer sequences than the previous state-of-the-art algorithms, which require exhaustive enumeration of sequences. Furthermore, for 51 protein-ligand design problems, BBK* provably approximates Ka up to 1982-fold faster than the previous state-of-the-art iMinDEE/[Formula: see text]/[Formula: see text] algorithm. Therefore, BBK* not only accelerates protein designs that are possible with previous provable algorithms, but also efficiently performs designs that are too large for previous methods.
Collapse
Affiliation(s)
- Adegoke A Ojewole
- 1 Department of Computer Science, Duke University , Durham, North Carolina.,2 Computational Biology and Bioinformatics Program, Duke University , Durham, North Carolina
| | - Jonathan D Jou
- 1 Department of Computer Science, Duke University , Durham, North Carolina
| | - Vance G Fowler
- 3 Division of Infectious Diseases, Duke University Medical Center , Durham, North Carolina
| | - Bruce R Donald
- 1 Department of Computer Science, Duke University , Durham, North Carolina.,4 Department of Biochemistry, Duke University Medical Center , Durham North Carolina
| |
Collapse
|
42
|
Joshi RR. Diversity and motif conservation in protein 3D structural landscape: exploration by a new multivariate simulation method. J Mol Model 2018; 24:76. [PMID: 29500695 DOI: 10.1007/s00894-018-3614-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 01/31/2018] [Indexed: 11/29/2022]
Abstract
In this paper, diversity and conservation in the 'landscape' of random variation of protein tertiary structures are explored for quantitative feature-vector models of major types of functionally important 3D structural motifs. For this, I have deployed a recently developed nonparametric regression (NPR)-based multidimensional copula method of simulation. Apart from improved accuracy of multidimensional random sample generation, the simulation provides additional insight into diversity in the protein structural landscape in terms of random variation in the feature-vector. It shows the relative importance of several features, with biological implications, in conservation of motifs. Mapping of this landscape in distance-preserving 2D eigenspace also shows consistency in demarcation of different motif classes and preservation of their characteristic patterns in this 2D space.
Collapse
Affiliation(s)
- Rajani R Joshi
- Department of Mathematics, Indian Institute of Technology Bombay, Mumbai, India.
| |
Collapse
|
43
|
Zhang SQ, Chino M, Liu L, Tang Y, Hu X, DeGrado WF, Lombardi A. De Novo Design of Tetranuclear Transition Metal Clusters Stabilized by Hydrogen-Bonded Networks in Helical Bundles. J Am Chem Soc 2018; 140:1294-1304. [PMID: 29249157 PMCID: PMC5860638 DOI: 10.1021/jacs.7b08261] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
De novo design provides an attractive approach to test the mechanism by which metalloproteins define the geometry and reactivity of their metal ion cofactors. While there has been considerable progress in designing proteins that bind transition metal ions including iron-sulfur clusters, the design of tetranuclear clusters with oxygen-rich environments has not been accomplished. Here, we describe the design of tetranuclear clusters, consisting of four Zn2+ and four carboxylate oxygens situated at the vertices of a distorted cube-like structure. The tetra-Zn2+ clusters are bound at a buried site within a four-helix bundle, with each helix donating a single carboxylate (Glu or Asp) and imidazole (His) ligand, as well as second- and third-shell ligands. Overall, the designed site consists of four Zn2+ and 16 polar side chains in a fully connected hydrogen-bonded network. The designed proteins have apolar cores at the top and bottom of the bundle, which drive the assembly of the liganding residues near the center of the bundle. The steric bulk of the apolar residues surrounding the binding site was varied to determine how subtle changes in helix-helix packing affect the binding site. The crystal structures of two of four proteins synthesized were in good agreement with the overall design; both formed a distorted cuboidal site stabilized by flanking second- and third-shell interactions that stabilize the primary ligands. A third structure bound a single Zn2+ in an unanticipated geometry, and the fourth bound multiple Zn2+ at multiple sites at partial occupancy. The metal-binding and conformational properties of the helical bundles in solution, probed by circular dichroism spectroscopy, analytical ultracentrifugation, and NMR, were consistent with the crystal structures.
Collapse
Affiliation(s)
- Shao-Qing Zhang
- Department of Chemistry, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA 19104-6396, United States
- Department of Pharmaceutical Chemistry and the Cardiovascular Research Institute, University of California at San Francisco, San Francisco, CA 94158-9001, United States
| | - Marco Chino
- Department of Chemical Sciences, University of Napoli “Federico II”, Via Cintia, 46, I-80126 Napoli, Italy
| | - Lijun Liu
- Department of Pharmaceutical Chemistry and the Cardiovascular Research Institute, University of California at San Francisco, San Francisco, CA 94158-9001, United States
- DLX Scientific, Lawrence, KS 66049, United States
| | - Youzhi Tang
- Department of Pharmaceutical Chemistry and the Cardiovascular Research Institute, University of California at San Francisco, San Francisco, CA 94158-9001, United States
- College of Veterinary Medicine, South China Agricultural University, Guangdong 510642, China
| | - Xiaozhen Hu
- Department of Pharmaceutical Chemistry and the Cardiovascular Research Institute, University of California at San Francisco, San Francisco, CA 94158-9001, United States
| | - William F. DeGrado
- Department of Pharmaceutical Chemistry and the Cardiovascular Research Institute, University of California at San Francisco, San Francisco, CA 94158-9001, United States
| | - Angela Lombardi
- Department of Chemical Sciences, University of Napoli “Federico II”, Via Cintia, 46, I-80126 Napoli, Italy
| |
Collapse
|
44
|
Mackenzie CO, Grigoryan G. Protein structural motifs in prediction and design. Curr Opin Struct Biol 2017; 44:161-167. [PMID: 28460216 PMCID: PMC5513761 DOI: 10.1016/j.sbi.2017.03.012] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 03/18/2017] [Accepted: 03/28/2017] [Indexed: 01/11/2023]
Abstract
The Protein Data Bank (PDB) has been an integral resource for shaping our fundamental understanding of protein structure and for the advancement of such applications as protein design and structure prediction. Over the years, information from the PDB has been used to generate models ranging from specific structural mechanisms to general statistical potentials. With accumulating structural data, it has become possible to mine for more complete and complex structural observations, deducing more accurate generalizations. Motif libraries, which capture recurring structural features along with their sequence preferences, have exposed modularity in the structural universe and found successful application in various problems of structural biology. Here we summarize recent achievements in this arena, focusing on subdomain level structural patterns and their applications to protein design and structure prediction, and suggest promising future directions as the structural database continues to grow.
Collapse
Affiliation(s)
- Craig O Mackenzie
- Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755, United States
| | - Gevorg Grigoryan
- Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755, United States; Department of Computer Science, Dartmouth College, Hanover, NH 03755, United States.
| |
Collapse
|
45
|
Sequence statistics of tertiary structural motifs reflect protein stability. PLoS One 2017; 12:e0178272. [PMID: 28552940 PMCID: PMC5446159 DOI: 10.1371/journal.pone.0178272] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Accepted: 05/10/2017] [Indexed: 11/19/2022] Open
Abstract
The Protein Data Bank (PDB) has been a key resource for learning general rules of sequence-structure relationships in proteins. Quantitative insights have been gained by defining geometric descriptors of structure (e.g., distances, dihedral angles, solvent exposure, etc.) and observing their distributions and sequence preferences. Here we argue that as the PDB continues to grow, it may become unnecessary to reduce structure into a set of elementary descriptors. Instead, it could be possible to deduce quantitative sequence-structure relationships in the context of precisely-defined complex structural motifs by mining the PDB for closely matching backbone geometries. To validate this idea, we turned to the the task of predicting changes in protein stability upon amino-acid substitution—a difficult problem of broad significance. We defined non-contiguous tertiary motifs (TERMs) around a protein site of interest and extracted sequence preferences from ensembles of closely-matching substructures in the PDB to predict mutational stability changes at the site, ΔΔGm. We demonstrate that these ensemble statistics predict ΔΔGm on par with state-of-the-art statistical and machine-learning methods on large thermodynamic datasets, and outperform these, along with a leading structure-based modeling approach, when tested in the context of unbiased diverse mutations. Further, we show that the performance of the TERM-based method is directly related to the amount of available relevant structural data, automatically improving with the growing PDB. This enables a means of estimating prediction accuracy. Our results clearly demonstrate that: 1) statistics of non-contiguous structural motifs in the PDB encode fundamental sequence-structure relationships related to protein thermodynamic stability, and 2) the PDB is now large enough that such statistics are already useful in practice, with their accuracy expected to continue increasing as the database grows. These observations suggest new ways of using structural data towards addressing problems of computational structural biology.
Collapse
|
46
|
Abstract
Here, we systematically decompose the known protein structural universe into its basic elements, which we dub tertiary structural motifs (TERMs). A TERM is a compact backbone fragment that captures the secondary, tertiary, and quaternary environments around a given residue, comprising one or more disjoint segments (three on average). We seek the set of universal TERMs that capture all structure in the Protein Data Bank (PDB), finding remarkable degeneracy. Only ∼600 TERMs are sufficient to describe 50% of the PDB at sub-Angstrom resolution. However, more rare geometries also exist, and the overall structural coverage grows logarithmically with the number of TERMs. We go on to show that universal TERMs provide an effective mapping between sequence and structure. We demonstrate that TERM-based statistics alone are sufficient to recapitulate close-to-native sequences given either NMR or X-ray backbones. Furthermore, sequence variability predicted from TERM data agrees closely with evolutionary variation. Finally, locations of TERMs in protein chains can be predicted from sequence alone based on sequence signatures emergent from TERM instances in the PDB. For multisegment motifs, this method identifies spatially adjacent fragments that are not contiguous in sequence-a major bottleneck in structure prediction. Although all TERMs recur in diverse proteins, some appear specialized for certain functions, such as interface formation, metal coordination, or even water binding. Structural biology has benefited greatly from previously observed degeneracies in structure. The decomposition of the known structural universe into a finite set of compact TERMs offers exciting opportunities toward better understanding, design, and prediction of protein structure.
Collapse
|
47
|
Nastri F, Chino M, Maglio O, Bhagi-Damodaran A, Lu Y, Lombardi A. Design and engineering of artificial oxygen-activating metalloenzymes. Chem Soc Rev 2016; 45:5020-54. [PMID: 27341693 PMCID: PMC5021598 DOI: 10.1039/c5cs00923e] [Citation(s) in RCA: 133] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Many efforts are being made in the design and engineering of metalloenzymes with catalytic properties fulfilling the needs of practical applications. Progress in this field has recently been accelerated by advances in computational, molecular and structural biology. This review article focuses on the recent examples of oxygen-activating metalloenzymes, developed through the strategies of de novo design, miniaturization processes and protein redesign. Considerable progress in these diverse design approaches has produced many metal-containing biocatalysts able to adopt the functions of native enzymes or even novel functions beyond those found in Nature.
Collapse
Affiliation(s)
- Flavia Nastri
- Department of Chemical Sciences, University of Naples “Federico II”, Via Cintia, 80126 Naples, Italy
| | - Marco Chino
- Department of Chemical Sciences, University of Naples “Federico II”, Via Cintia, 80126 Naples, Italy
| | - Ornella Maglio
- Department of Chemical Sciences, University of Naples “Federico II”, Via Cintia, 80126 Naples, Italy
- IBB, CNR, Via Mezzocannone 16, 80134 Naples, Italy
| | - Ambika Bhagi-Damodaran
- Department of Chemistry, University of Illinois at Urbana-Champaign, A322 CLSL, 600 South Mathews Avenue, Urbana, IL 61801
| | - Yi Lu
- Department of Chemistry, University of Illinois at Urbana-Champaign, A322 CLSL, 600 South Mathews Avenue, Urbana, IL 61801
| | - Angela Lombardi
- Department of Chemical Sciences, University of Naples “Federico II”, Via Cintia, 80126 Naples, Italy
| |
Collapse
|
48
|
Gainza P, Nisonoff HM, Donald BR. Algorithms for protein design. Curr Opin Struct Biol 2016; 39:16-26. [PMID: 27086078 PMCID: PMC5065368 DOI: 10.1016/j.sbi.2016.03.006] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 03/15/2016] [Accepted: 03/22/2016] [Indexed: 02/05/2023]
Abstract
Computational structure-based protein design programs are becoming an increasingly important tool in molecular biology. These programs compute protein sequences that are predicted to fold to a target structure and perform a desired function. The success of a program's predictions largely relies on two components: first, the input biophysical model, and second, the algorithm that computes the best sequence(s) and structure(s) according to the biophysical model. Improving both the model and the algorithm in tandem is essential to improving the success rate of current programs, and here we review recent developments in algorithms for protein design, emphasizing how novel algorithms enable the use of more accurate biophysical models. We conclude with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins and protein assemblies.
Collapse
Affiliation(s)
- Pablo Gainza
- Department of Computer Science, Duke University, Durham, NC, United States
| | - Hunter M Nisonoff
- Department of Computer Science, Duke University, Durham, NC, United States
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC, United States; Department of Biochemistry, Duke University Medical Center, Durham, NC, United States; Department of Chemistry, Duke University, Durham, NC, United States.
| |
Collapse
|
49
|
Protein-directed self-assembly of a fullerene crystal. Nat Commun 2016; 7:11429. [PMID: 27113637 PMCID: PMC4853425 DOI: 10.1038/ncomms11429] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 03/23/2016] [Indexed: 01/03/2023] Open
Abstract
Learning to engineer self-assembly would enable the precise organization of molecules by design to create matter with tailored properties. Here we demonstrate that proteins can direct the self-assembly of buckminsterfullerene (C60) into ordered superstructures. A previously engineered tetrameric helical bundle binds C60 in solution, rendering it water soluble. Two tetramers associate with one C60, promoting further organization revealed in a 1.67-Å crystal structure. Fullerene groups occupy periodic lattice sites, sandwiched between two Tyr residues from adjacent tetramers. Strikingly, the assembly exhibits high charge conductance, whereas both the protein-alone crystal and amorphous C60 are electrically insulating. The affinity of C60 for its crystal-binding site is estimated to be in the nanomolar range, with lattices of known protein crystals geometrically compatible with incorporating the motif. Taken together, these findings suggest a new means of organizing fullerene molecules into a rich variety of lattices to generate new properties by design. Self-assembly enables complex structures to be fabricated from a few relatively simple components, but requires a detailed understanding of how the constituents may interact. Here, the authors report the rational assembly and crystallographic characterization of a fullerene-protein superstructure.
Collapse
|
50
|
Abstract
Selective targeting of protein-protein interactions in the cell is of great interest in biological research. Computational structure-based design of peptides to bind protein interaction interfaces could provide a potential means of generating such reagents. However, to avoid perturbing off-target interactions, methods that explicitly account for interaction specificity are needed. Further, as peptides often retain considerable flexibility upon association, their binding reaction is computationally demanding to model-a stark limitation for structure-based design. Here we present a protocol for designing peptides that selectively target a given peptide-binding domain, relative to a pre-specified set of possibly related domains. We recently used the method to design peptides that discriminate with high selectivity between two closely related PDZ domains. The framework accounts for the flexibility of the peptide in the binding site, but is efficient enough to quickly analyze trade-offs between affinity and selectivity, enabling the identification of optimal peptides.
Collapse
Affiliation(s)
- Fan Zheng
- Department of Biological Sciences, Dartmouth College, Hanover, NH, 03755, USA
| | - Gevorg Grigoryan
- Department of Biological Sciences, Dartmouth College, Hanover, NH, 03755, USA.
- Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA.
| |
Collapse
|