1
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Listov D, Goverde CA, Correia BE, Fleishman SJ. Opportunities and challenges in design and optimization of protein function. Nat Rev Mol Cell Biol 2024; 25:639-653. [PMID: 38565617 PMCID: PMC7616297 DOI: 10.1038/s41580-024-00718-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.
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Affiliation(s)
- Dina Listov
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Casper A Goverde
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Sarel Jacob Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel.
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2
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Guo AB, Akpinaroglu D, Kelly MJS, Kortemme T. Deep learning guided design of dynamic proteins. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.17.603962. [PMID: 39071443 PMCID: PMC11275770 DOI: 10.1101/2024.07.17.603962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Deep learning has greatly advanced design of highly stable static protein structures, but the controlled conformational dynamics that are hallmarks of natural switch-like signaling proteins have remained inaccessible to de novo design. Here, we describe a general deep-learning-guided approach for de novo design of dynamic changes between intra-domain geometries of proteins, similar to switch mechanisms prevalent in nature, with atom-level precision. We solve 4 structures validating the designed conformations, show microsecond transitions between them, and demonstrate that the conformational landscape can be modulated by orthosteric ligands and allosteric mutations. Physics-based simulations are in remarkable agreement with deep-learning predictions and experimental data, reveal distinct state-dependent residue interaction networks, and predict mutations that tune the designed conformational landscape. Our approach demonstrates that new modes of motion can now be realized through de novo design and provides a framework for constructing biology-inspired, tunable and controllable protein signaling behavior de novo .
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3
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Wang J, Watson JL, Lisanza SL. Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models. Cold Spring Harb Perspect Biol 2024; 16:a041472. [PMID: 38438190 PMCID: PMC11216169 DOI: 10.1101/cshperspect.a041472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design as well. We review recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion. These methods have led to major improvements over previous methods in wet-lab success rates for designing protein binders, metalloproteins, enzymes, and oligomeric assemblies. These results show that structure-prediction models are a powerful foundation for developing protein-design tools and suggest that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design.
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Affiliation(s)
- Jue Wang
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, Washington 98195, USA
- DeepMind, London EC4A 3BF, United Kingdom
| | - Joseph L Watson
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
| | - Sidney L Lisanza
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, Washington 98195, USA
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4
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Ito S, Matsunaga R, Nakakido M, Komura D, Katoh H, Ishikawa S, Tsumoto K. High-throughput system for the thermostability analysis of proteins. Protein Sci 2024; 33:e5029. [PMID: 38801228 PMCID: PMC11129621 DOI: 10.1002/pro.5029] [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: 03/28/2024] [Revised: 04/30/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024]
Abstract
Thermal stability of proteins is a primary metric for evaluating their physical properties. Although researchers attempted to predict it using machine learning frameworks, their performance has been dependent on the quality and quantity of published data. This is due to the technical limitation that thermodynamic characterization of protein denaturation by fluorescence or calorimetry in a high-throughput manner has been challenging. Obtaining a melting curve that derives solely from the target protein requires laborious purification, making it far from practical to prepare a hundred or more samples in a single workflow. Here, we aimed to overcome this throughput limitation by leveraging the high protein secretion efficacy of Brevibacillus and consecutive treatment with plate-scale purification methodologies. By handling the entire process of expression, purification, and analysis on a per-plate basis, we enabled the direct observation of protein denaturation in 384 samples within 4 days. To demonstrate a practical application of the system, we conducted a comprehensive analysis of 186 single mutants of a single-chain variable fragment of nivolumab, harvesting the melting temperature (Tm) ranging from -9.3 up to +10.8°C compared to the wild-type sequence. Our findings will allow for data-driven stabilization in protein design and streamlining the rational approaches.
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Affiliation(s)
- Sae Ito
- Department of Bioengineering, School of EngineeringThe University of TokyoTokyoJapan
| | - Ryo Matsunaga
- Department of Bioengineering, School of EngineeringThe University of TokyoTokyoJapan
- Department of Chemistry and Biotechnology, School of EngineeringThe University of TokyoTokyoJapan
| | - Makoto Nakakido
- Department of Bioengineering, School of EngineeringThe University of TokyoTokyoJapan
- Department of Chemistry and Biotechnology, School of EngineeringThe University of TokyoTokyoJapan
| | - Daisuke Komura
- Department of Preventive Medicine, Graduate School of MedicineThe University of TokyoTokyoJapan
| | - Hiroto Katoh
- Department of Preventive Medicine, Graduate School of MedicineThe University of TokyoTokyoJapan
| | - Shumpei Ishikawa
- Department of Preventive Medicine, Graduate School of MedicineThe University of TokyoTokyoJapan
| | - Kouhei Tsumoto
- Department of Bioengineering, School of EngineeringThe University of TokyoTokyoJapan
- Department of Chemistry and Biotechnology, School of EngineeringThe University of TokyoTokyoJapan
- The Institute of Medical ScienceThe University of TokyoTokyoJapan
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5
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Saikia B, Baruah A. In silico design of misfolding resistant proteins: the role of structural similarity of a competing conformational ensemble in the optimization of frustration. SOFT MATTER 2024; 20:3283-3298. [PMID: 38529658 DOI: 10.1039/d4sm00171k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Most state-of-the-art in silico design methods fail due to misfolding of designed sequences to a conformation other than the target. Thus, a method to design misfolding resistant proteins will provide a better understanding of the misfolding phenomenon and will also increase the success rate of in silico design methods. In this work, we optimize the conformational ensemble to be selected for negative design purposes based on the similarity of the conformational ensemble to the target. Five ensembles with different degrees of similarity to the target are created and destabilized and the target is stabilized while designing sequences using mean field theory and Monte Carlo simulation methods. The results suggest that the degree of similarity of the non-native conformations to the target plays a prominent role in designing misfolding resistant protein sequences. The design procedures that destabilize the conformational ensemble with moderate similarity to the target have proven to be more promising. Incorporation of either highly similar or highly dissimilar conformations to the target conformation into the non-native ensemble to be destabilized may lead to sequences with a higher misfolding propensity. This will significantly reduce the conformational space to be considered in any protein design procedure. Interestingly, the results suggest that a sequence with higher frustration in the target structure does not necessarily lead to a misfold prone sequence. A successful design method may purposefully choose a frustrated sequence in the target conformation if that sequence is even more frustrated in the competing non-native conformations.
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Affiliation(s)
- Bondeepa Saikia
- Department of Chemistry, Dibrugarh University, Dibrugarh 786004, India.
| | - Anupaul Baruah
- Department of Chemistry, Dibrugarh University, Dibrugarh 786004, India.
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6
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Chu AE, Lu T, Huang PS. Sparks of function by de novo protein design. Nat Biotechnol 2024; 42:203-215. [PMID: 38361073 PMCID: PMC11366440 DOI: 10.1038/s41587-024-02133-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/09/2024] [Indexed: 02/17/2024]
Abstract
Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.
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Affiliation(s)
- Alexander E Chu
- Biophysics Program, Stanford University, Palo Alto, CA, USA
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
- Google DeepMind, London, UK
| | - Tianyu Lu
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
| | - Po-Ssu Huang
- Biophysics Program, Stanford University, Palo Alto, CA, USA.
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA.
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7
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Bai G, Sun C, Guo Z, Wang Y, Zeng X, Su Y, Zhao Q, Ma B. Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects. Semin Cancer Biol 2023; 95:13-24. [PMID: 37355214 DOI: 10.1016/j.semcancer.2023.06.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/09/2023] [Accepted: 06/18/2023] [Indexed: 06/26/2023]
Abstract
Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.
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Affiliation(s)
- Ganggang Bai
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chuance Sun
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ziang Guo
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China
| | - Yangjing Wang
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xincheng Zeng
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuhong Su
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China; MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macao Special Administrative Region of China.
| | - Buyong Ma
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Digiwiser BioTechnolgy, Limited, Shanghai 201203, China.
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8
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Maksymenko K, Maurer A, Aghaallaei N, Barry C, Borbarán-Bravo N, Ullrich T, Dijkstra TM, Hernandez Alvarez B, Müller P, Lupas AN, Skokowa J, ElGamacy M. The design of functional proteins using tensorized energy calculations. CELL REPORTS METHODS 2023; 3:100560. [PMID: 37671023 PMCID: PMC10475850 DOI: 10.1016/j.crmeth.2023.100560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 05/25/2023] [Accepted: 07/21/2023] [Indexed: 09/07/2023]
Abstract
In protein design, the energy associated with a huge number of sequence-conformer perturbations has to be routinely estimated. Hence, enhancing the throughput and accuracy of these energy calculations can profoundly improve design success rates and enable tackling more complex design problems. In this work, we explore the possibility of tensorizing the energy calculations and apply them in a protein design framework. We use this framework to design enhanced proteins with anti-cancer and radio-tracing functions. Particularly, we designed multispecific binders against ligands of the epidermal growth factor receptor (EGFR), where the tested design could inhibit EGFR activity in vitro and in vivo. We also used this method to design high-affinity Cu2+ binders that were stable in serum and could be readily loaded with copper-64 radionuclide. The resulting molecules show superior functional properties for their respective applications and demonstrate the generalizable potential of the described protein design approach.
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Affiliation(s)
- Kateryna Maksymenko
- Department of Protein Evolution, Max Planck Institute for Biology, 72076 Tübingen, Germany
- Friedrich Miescher Laboratory of the Max Planck Society, 72076 Tübingen, Germany
| | - Andreas Maurer
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) “Image Guided and Functionally Instructed Tumor Therapies,” Eberhard Karls University, 72076 Tübingen, Germany
| | - Narges Aghaallaei
- Division of Translational Oncology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Caroline Barry
- Department of Protein Evolution, Max Planck Institute for Biology, 72076 Tübingen, Germany
- Krieger School of Arts and Sciences, Johns Hopkins University, Washington, DC 20036, USA
| | - Natalia Borbarán-Bravo
- Division of Translational Oncology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Timo Ullrich
- Department of Protein Evolution, Max Planck Institute for Biology, 72076 Tübingen, Germany
- Friedrich Miescher Laboratory of the Max Planck Society, 72076 Tübingen, Germany
| | - Tjeerd M.H. Dijkstra
- Department of Protein Evolution, Max Planck Institute for Biology, 72076 Tübingen, Germany
- Department for Women’s Health, University Hospital Tübingen, 72076 Tübingen, Germany
- Translational Bioinformatics, University Hospital Tübingen, 72072 Tübingen, Germany
| | | | - Patrick Müller
- Friedrich Miescher Laboratory of the Max Planck Society, 72076 Tübingen, Germany
| | - Andrei N. Lupas
- Department of Protein Evolution, Max Planck Institute for Biology, 72076 Tübingen, Germany
| | - Julia Skokowa
- Division of Translational Oncology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Mohammad ElGamacy
- Department of Protein Evolution, Max Planck Institute for Biology, 72076 Tübingen, Germany
- Friedrich Miescher Laboratory of the Max Planck Society, 72076 Tübingen, Germany
- Division of Translational Oncology, University Hospital Tübingen, 72076 Tübingen, Germany
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9
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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: 44] [Impact Index Per Article: 44.0] [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.
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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.
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10
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Bermeo S, Favor A, Chang YT, Norris A, Boyken SE, Hsia Y, Haddox HK, Xu C, Brunette TJ, Wysocki VH, Bhabha G, Ekiert DC, Baker D. De novo design of obligate ABC-type heterotrimeric proteins. Nat Struct Mol Biol 2022; 29:1266-1276. [PMID: 36522429 PMCID: PMC9758053 DOI: 10.1038/s41594-022-00879-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 10/20/2022] [Indexed: 12/23/2022]
Abstract
The de novo design of three protein chains that associate to form a heterotrimer (but not any of the possible two-chain heterodimers) and that can drive the assembly of higher-order branching structures is an important challenge for protein design. We designed helical heterotrimers with specificity conferred by buried hydrogen bond networks and large aromatic residues to enhance shape complementary packing. We obtained ten designs for which all three chains cooperatively assembled into heterotrimers with few or no other species present. Crystal structures of a helical bundle heterotrimer and extended versions, with helical repeat proteins fused to individual subunits, showed all three chains assembling in the designed orientation. We used these heterotrimers as building blocks to construct larger cyclic oligomers, which were structurally validated by electron microscopy. Our three-way junction designs provide new routes to complex protein nanostructures and enable the scaffolding of three distinct ligands for modulation of cell signaling.
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Affiliation(s)
- Sherry Bermeo
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Biological Physics, Structure and Design Graduate Program, University of Washington, Seattle, WA, USA
| | - Andrew Favor
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA, USA
| | - Ya-Ting Chang
- Department of Cell Biology, New York University School of Medicine, New York, NY, USA
| | - Andrew Norris
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH, USA
- Resource for Native Mass Spectrometry Guided Structural Biology, The Ohio State University, Columbus, OH, USA
| | - Scott E Boyken
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Yang Hsia
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Hugh K Haddox
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Chunfu Xu
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - T J Brunette
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Vicki H Wysocki
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH, USA
- Resource for Native Mass Spectrometry Guided Structural Biology, The Ohio State University, Columbus, OH, USA
| | - Gira Bhabha
- Department of Cell Biology, New York University School of Medicine, New York, NY, USA
| | - Damian C Ekiert
- Department of Cell Biology, New York University School of Medicine, New York, NY, USA
- Department of Microbiology, New York University School of Medicine, New York, NY, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
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11
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Elazar A, Chandler NJ, Davey AS, Weinstein JY, Nguyen JV, Trenker R, Cross RS, Jenkins MR, Call MJ, Call ME, Fleishman SJ. De novo-designed transmembrane domains tune engineered receptor functions. eLife 2022; 11:75660. [PMID: 35506657 PMCID: PMC9068223 DOI: 10.7554/elife.75660] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 04/14/2022] [Indexed: 12/20/2022] Open
Abstract
De novo-designed receptor transmembrane domains (TMDs) present opportunities for precise control of cellular receptor functions. We developed a de novo design strategy for generating programmed membrane proteins (proMPs): single-pass α-helical TMDs that self-assemble through computationally defined and crystallographically validated interfaces. We used these proMPs to program specific oligomeric interactions into a chimeric antigen receptor (CAR) that we expressed in mouse primary T cells and found that both in vitro CAR T cell cytokine release and in vivo antitumor activity scaled linearly with the oligomeric state encoded by the receptor TMD, from monomers up to tetramers. All programmed CARs stimulated substantially lower T cell cytokine release relative to the commonly used CD28 TMD, which we show elevated cytokine release through lateral recruitment of the endogenous T cell costimulatory receptor CD28. Precise design using orthogonal and modular TMDs thus provides a new way to program receptor structure and predictably tune activity for basic or applied synthetic biology.
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Affiliation(s)
- Assaf Elazar
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Nicholas J Chandler
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Ashleigh S Davey
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Jonathan Y Weinstein
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Julie V Nguyen
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Raphael Trenker
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Ryan S Cross
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia.,Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Misty R Jenkins
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia.,Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,La Trobe Institute of Molecular Science, La Trobe University, Bundoora, Victoria, Australia
| | - Melissa J Call
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Matthew E Call
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
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12
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Krivacic C, Kundert K, Pan X, Pache RA, Liu L, Conchúir SO, Jeliazkov JR, Gray JJ, Thompson MC, Fraser JS, Kortemme T. Accurate positioning of functional residues with robotics-inspired computational protein design. Proc Natl Acad Sci U S A 2022; 119:e2115480119. [PMID: 35254891 PMCID: PMC8931229 DOI: 10.1073/pnas.2115480119] [Citation(s) in RCA: 6] [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: 10/08/2021] [Accepted: 01/27/2022] [Indexed: 11/18/2022] Open
Abstract
SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein's preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.
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Affiliation(s)
- Cody Krivacic
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Kale Kundert
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Biophysics Graduate Program, University of California, San Francisco, CA 94158
| | - Xingjie Pan
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Roland A. Pache
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Lin Liu
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Shane O Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | | | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Michael C. Thompson
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - James S. Fraser
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Biophysics Graduate Program, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
| | - Tanja Kortemme
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Biophysics Graduate Program, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
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13
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Khersonsky O, Fleishman SJ. What Have We Learned from Design of Function in Large Proteins? BIODESIGN RESEARCH 2022; 2022:9787581. [PMID: 37850148 PMCID: PMC10521758 DOI: 10.34133/2022/9787581] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 02/21/2022] [Indexed: 10/19/2023] Open
Abstract
The overarching goal of computational protein design is to gain complete control over protein structure and function. The majority of sophisticated binders and enzymes, however, are large and exhibit diverse and complex folds that defy atomistic design calculations. Encouragingly, recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design accuracy. In these approaches, evolutionary constraints mitigate the risk from misfolding and aggregation, focusing atomistic design calculations on a small but highly enriched sequence subspace. Such methods have dramatically optimized diverse proteins, including vaccine immunogens, enzymes for sustainable chemistry, and proteins with therapeutic potential. The new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design, in principle, to any natural protein of known sequence. We envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.
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Affiliation(s)
- Olga Khersonsky
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Sarel J. Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
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14
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ElGamacy M. Accelerating therapeutic protein design. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 130:85-118. [PMID: 35534117 DOI: 10.1016/bs.apcsb.2022.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Protein structures provide for defined microenvironments that can support complex pharmacological functions, otherwise unachievable by small molecules. The advent of therapeutic proteins has thus greatly broadened the range of manageable disorders. Leveraging the knowledge and recent advances in de novo protein design methods has the prospect of revolutionizing how protein drugs are discovered and developed. This review lays out the main challenges facing therapeutic proteins discovery and development, and how present and future advancements of protein design can accelerate the protein drug pipelines.
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Affiliation(s)
- Mohammad ElGamacy
- University Hospital Tübingen, Division of Translational Oncology, Tübingen, Germany; Max Planck Institute for Biology, Tübingen, Germany.
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15
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Sahtoe DD, Praetorius F, Courbet A, Hsia Y, Wicky BI, Edman NI, Miller LM, Timmermans BJR, Decarreau J, Morris HM, Kang A, Bera AK, Baker D. Reconfigurable asymmetric protein assemblies through implicit negative design. Science 2022; 375:eabj7662. [PMID: 35050655 PMCID: PMC9881579 DOI: 10.1126/science.abj7662] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Asymmetric multiprotein complexes that undergo subunit exchange play central roles in biology but present a challenge for design because the components must not only contain interfaces that enable reversible association but also be stable and well behaved in isolation. We use implicit negative design to generate β sheet-mediated heterodimers that can be assembled into a wide variety of complexes. The designs are stable, folded, and soluble in isolation and rapidly assemble upon mixing, and crystal structures are close to the computational models. We construct linearly arranged hetero-oligomers with up to six different components, branched hetero-oligomers, closed C4-symmetric two-component rings, and hetero-oligomers assembled on a cyclic homo-oligomeric central hub and demonstrate that such complexes can readily reconfigure through subunit exchange. Our approach provides a general route to designing asymmetric reconfigurable protein systems.
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Affiliation(s)
- Danny D. Sahtoe
- Department of Biochemistry, University of Washington, Seattle, WA 98195,Institute for Protein Design, University of Washington, Seattle, WA 98195,HHMI, University of Washington, Seattle, WA 98195
| | - Florian Praetorius
- Department of Biochemistry, University of Washington, Seattle, WA 98195,Institute for Protein Design, University of Washington, Seattle, WA 98195
| | - Alexis Courbet
- Department of Biochemistry, University of Washington, Seattle, WA 98195,Institute for Protein Design, University of Washington, Seattle, WA 98195,HHMI, University of Washington, Seattle, WA 98195
| | - Yang Hsia
- Department of Biochemistry, University of Washington, Seattle, WA 98195,Institute for Protein Design, University of Washington, Seattle, WA 98195
| | - Basile I.M. Wicky
- Department of Biochemistry, University of Washington, Seattle, WA 98195,Institute for Protein Design, University of Washington, Seattle, WA 98195
| | - Natasha I. Edman
- Department of Biochemistry, University of Washington, Seattle, WA 98195,Institute for Protein Design, University of Washington, Seattle, WA 98195,Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA.,Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Lauren M. Miller
- Department of Biochemistry, University of Washington, Seattle, WA 98195,Institute for Protein Design, University of Washington, Seattle, WA 98195
| | - Bart J. R. Timmermans
- Department of Biochemistry, University of Washington, Seattle, WA 98195,Institute for Protein Design, University of Washington, Seattle, WA 98195
| | - Justin Decarreau
- Department of Biochemistry, University of Washington, Seattle, WA 98195,Institute for Protein Design, University of Washington, Seattle, WA 98195
| | - Hana M. Morris
- Department of Biochemistry, University of Washington, Seattle, WA 98195,Institute for Protein Design, University of Washington, Seattle, WA 98195
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA 98195,Institute for Protein Design, University of Washington, Seattle, WA 98195
| | - Asim K. Bera
- Department of Biochemistry, University of Washington, Seattle, WA 98195,Institute for Protein Design, University of Washington, Seattle, WA 98195
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195,Institute for Protein Design, University of Washington, Seattle, WA 98195,HHMI, University of Washington, Seattle, WA 98195,Corresponding author.
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16
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Ray S, Tillo D, Assad N, Ufot A, Porollo A, Durell SR, Vinson C. Altering the Double-Stranded DNA Specificity of the bZIP Domain of Zta with Site-Directed Mutagenesis at N182. ACS OMEGA 2022; 7:129-139. [PMID: 35036684 PMCID: PMC8756438 DOI: 10.1021/acsomega.1c04148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 11/23/2021] [Indexed: 06/14/2023]
Abstract
Zta, the Epstein-Barr virus bZIP transcription factor (TF), binds both unmethylated and methylated double-stranded DNA (dsDNA) in a sequence-specific manner. We studied the contribution of a conserved asparagine (N182) to sequence-specific dsDNA binding to four types of dsDNA: (i) dsDNA with cytosine in both strands ((DNA(C|C)), (ii, iii) dsDNA with 5-methylcytosine (5mC, M) or 5-hydroxymethylcytosine (5hmC, H) in one strand and cytosine in the second strand ((DNA(5mC|C) and DNA(5hmC|C)), and (iv) dsDNA with methylated cytosine in both strands in all CG dinucleotides ((DNA(5mCG)). We replaced asparagine with five similarly sized amino acids (glutamine (Q), serine (S), threonine (T), isoleucine (I), or valine (V)) and used protein binding microarrays to evaluate sequence-specific dsDNA binding. Zta preferentially binds the pseudo-palindrome TRE (AP1) motif (T-4G-3A-2G/C 0T2C3A4 ). Zta (N182Q) changes binding to A3 in only one half-site. Zta(N182S) changes binding to G3 in one or both halves of the motif. Zta(N182S) and Zta(N182Q) have 34- and 17-fold weaker median dsDNA binding, respectively. Zta(N182V) and Zta(N182I) have increased binding to dsDNA(5mC|C). Molecular dynamics simulations rationalize some of these results, identifying hydrogen bonds between glutamine and A3 , but do not reveal why serine preferentially binds G3 , suggesting that entropic interactions may mediate this new binding specificity.
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Affiliation(s)
- Sreejana Ray
- Laboratory
of Metabolism, National Cancer Institute,
National Institutes of Health, Room 5000, Building 37, Bethesda, Maryland 20892, United States
| | - Desiree Tillo
- Laboratory
of Metabolism, National Cancer Institute,
National Institutes of Health, Room 5000, Building 37, Bethesda, Maryland 20892, United States
- Cancer
Genetics Branch, National Cancer Institute,
National Institutes of Health, Building 37, Bethesda, Maryland 20892, United States
| | - Nima Assad
- Laboratory
of Metabolism, National Cancer Institute,
National Institutes of Health, Room 5000, Building 37, Bethesda, Maryland 20892, United States
| | - Aniekanabasi Ufot
- Laboratory
of Metabolism, National Cancer Institute,
National Institutes of Health, Room 5000, Building 37, Bethesda, Maryland 20892, United States
| | - Aleksey Porollo
- Center
for Autoimmune Genomics and Etiology, Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229, United States
- Department
of Pediatrics, University of Cincinnati
College of Medicine, Cincinnati, Ohio 45267, United States
| | - Stewart R. Durell
- Laboratory
of Cell Biology, National Cancer Institute,
National Institutes of Health, Building 37, Bethesda, Maryland 20892, United States
| | - Charles Vinson
- Laboratory
of Metabolism, National Cancer Institute,
National Institutes of Health, Room 5000, Building 37, Bethesda, Maryland 20892, United States
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17
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Zhu J, Avakyan N, Kakkis AA, Hoffnagle AM, Han K, Li Y, Zhang Z, Choi TS, Na Y, Yu CJ, Tezcan FA. Protein Assembly by Design. Chem Rev 2021; 121:13701-13796. [PMID: 34405992 PMCID: PMC9148388 DOI: 10.1021/acs.chemrev.1c00308] [Citation(s) in RCA: 107] [Impact Index Per Article: 35.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Proteins are nature's primary building blocks for the construction of sophisticated molecular machines and dynamic materials, ranging from protein complexes such as photosystem II and nitrogenase that drive biogeochemical cycles to cytoskeletal assemblies and muscle fibers for motion. Such natural systems have inspired extensive efforts in the rational design of artificial protein assemblies in the last two decades. As molecular building blocks, proteins are highly complex, in terms of both their three-dimensional structures and chemical compositions. To enable control over the self-assembly of such complex molecules, scientists have devised many creative strategies by combining tools and principles of experimental and computational biophysics, supramolecular chemistry, inorganic chemistry, materials science, and polymer chemistry, among others. Owing to these innovative strategies, what started as a purely structure-building exercise two decades ago has, in short order, led to artificial protein assemblies with unprecedented structures and functions and protein-based materials with unusual properties. Our goal in this review is to give an overview of this exciting and highly interdisciplinary area of research, first outlining the design strategies and tools that have been devised for controlling protein self-assembly, then describing the diverse structures of artificial protein assemblies, and finally highlighting the emergent properties and functions of these assemblies.
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Affiliation(s)
| | | | - Albert A. Kakkis
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0340, United States
| | - Alexander M. Hoffnagle
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0340, United States
| | - Kenneth Han
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0340, United States
| | - Yiying Li
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0340, United States
| | - Zhiyin Zhang
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0340, United States
| | - Tae Su Choi
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0340, United States
| | - Youjeong Na
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0340, United States
| | - Chung-Jui Yu
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0340, United States
| | - F. Akif Tezcan
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0340, United States
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18
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Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1. Proc Natl Acad Sci U S A 2021; 118:2012800118. [PMID: 33723038 PMCID: PMC8000195 DOI: 10.1073/pnas.2012800118] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-β-lactamase 1 (NDM-1), a bacterial enzyme that degrades β-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures of l- and d-amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC50 values across seven designed NDM-1- inhibiting peptides. We were able to determine X-ray crystal structures of three of the designed inhibitors in complex with NDM-1, and in all three the conformation of the peptide is very close to the computationally designed model. In two of the three structures, the binding mode with NDM-1 is also very similar to the design model, while in the third, we observed an alternative binding mode likely arising from internal symmetry in the shape of the design combined with flexibility of the target. Although challenges remain in robustly predicting target backbone changes, binding mode, and the effects of mutations on binding affinity, our methods for designing ordered, binding-competent macrocycles should have broad applicability to a wide range of therapeutic targets.
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19
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Fleishman SJ, Horovitz A. Extending the New Generation of Structure Predictors to Account for Dynamics and Allostery. J Mol Biol 2021; 433:167007. [PMID: 33901536 DOI: 10.1016/j.jmb.2021.167007] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 04/18/2021] [Accepted: 04/19/2021] [Indexed: 10/21/2022]
Abstract
Recent progress in structure-prediction methods that rely on deep learning suggests that the atomic structure of almost any protein may soon be predictable directly from its amino acid sequence. This much-awaited revolution was driven by substantial improvements in the reliability of methods for inferring the spatial distances between amino acid pairs from an analysis of homologous sequences. Improved reliability has been accompanied, however, by a reduced ability to detect amino acid relationships that are not due to direct spatial contacts, such as those that arise from protein dynamics or allostery. Given the central importance of dynamics and allostery to protein activity, we argue that an important future advance would extend modeling beyond predicting a single static structure. Here, we briefly review some of the developments that have led to the remarkable recent achievement in structure prediction and speculate what methods and sources of information may be leveraged in the future to develop a modeling framework that addresses protein dynamics and allostery.
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Affiliation(s)
- Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7600001, Israel.
| | - Amnon Horovitz
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot 7600001, Israel.
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20
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Peleg Y, Vincentelli R, Collins BM, Chen KE, Livingstone EK, Weeratunga S, Leneva N, Guo Q, Remans K, Perez K, Bjerga GEK, Larsen Ø, Vaněk O, Skořepa O, Jacquemin S, Poterszman A, Kjær S, Christodoulou E, Albeck S, Dym O, Ainbinder E, Unger T, Schuetz A, Matthes S, Bader M, de Marco A, Storici P, Semrau MS, Stolt-Bergner P, Aigner C, Suppmann S, Goldenzweig A, Fleishman SJ. Community-Wide Experimental Evaluation of the PROSS Stability-Design Method. J Mol Biol 2021; 433:166964. [PMID: 33781758 DOI: 10.1016/j.jmb.2021.166964] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 03/08/2021] [Accepted: 03/22/2021] [Indexed: 10/21/2022]
Abstract
Recent years have seen a dramatic improvement in protein-design methodology. Nevertheless, most methods demand expert intervention, limiting their widespread adoption. By contrast, the PROSS algorithm for improving protein stability and heterologous expression levels has been successfully applied to a range of challenging enzymes and binding proteins. Here, we benchmark the application of PROSS as a stand-alone tool for protein scientists with no or limited experience in modeling. Twelve laboratories from the Protein Production and Purification Partnership in Europe (P4EU) challenged the PROSS algorithm with 14 unrelated protein targets without support from the PROSS developers. For each target, up to six designs were evaluated for expression levels and in some cases, for thermal stability and activity. In nine targets, designs exhibited increased heterologous expression levels either in prokaryotic and/or eukaryotic expression systems under experimental conditions that were tailored for each target protein. Furthermore, we observed increased thermal stability in nine of ten tested targets. In two prime examples, the human Stem Cell Factor (hSCF) and human Cadherin-Like Domain (CLD12) from the RET receptor, the wild type proteins were not expressible as soluble proteins in E. coli, yet the PROSS designs exhibited high expression levels in E. coli and HEK293 cells, respectively, and improved thermal stability. We conclude that PROSS may improve stability and expressibility in diverse cases, and that improvement typically requires target-specific expression conditions. This study demonstrates the strengths of community-wide efforts to probe the generality of new methods and recommends areas for future research to advance practically useful algorithms for protein science.
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Affiliation(s)
- Yoav Peleg
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel.
| | - Renaud Vincentelli
- Unité Mixte de Recherche (UMR) 7257, Centre National de la Recherche Scientifique (CNRS) Aix-Marseille Université, Architecture et Fonction des Macromolécules Biologiques (AFMB), Marseille, France
| | - Brett M Collins
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Kai-En Chen
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Emma K Livingstone
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Saroja Weeratunga
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Natalya Leneva
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Qian Guo
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Kim Remans
- European Molecular Biology Laboratory (EMBL), Protein Expression and Purification Core Facility, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Kathryn Perez
- European Molecular Biology Laboratory (EMBL), Protein Expression and Purification Core Facility, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Gro E K Bjerga
- NORCE Norwegian Research Centre, Postboks 22 Nygårdstangen, 5038 Bergen, Norway
| | - Øivind Larsen
- NORCE Norwegian Research Centre, Postboks 22 Nygårdstangen, 5038 Bergen, Norway
| | - Ondřej Vaněk
- Department of Biochemistry, Faculty of Science, Charles University, Hlavova 2030/8, 12840 Prague, Czech Republic
| | - Ondřej Skořepa
- Department of Biochemistry, Faculty of Science, Charles University, Hlavova 2030/8, 12840 Prague, Czech Republic
| | - Sophie Jacquemin
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Centre National de la Recherche Scientifique (CNRS), UMR 7104, Institut National de la Santé et de la Recherche Médicale (INSERM), U1258, Université de Strasbourg, France
| | - Arnaud Poterszman
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Centre National de la Recherche Scientifique (CNRS), UMR 7104, Institut National de la Santé et de la Recherche Médicale (INSERM), U1258, Université de Strasbourg, France
| | - Svend Kjær
- Structural Biology Science Technology Platform, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Evangelos Christodoulou
- Structural Biology Science Technology Platform, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Shira Albeck
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Orly Dym
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Elena Ainbinder
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Tamar Unger
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Anja Schuetz
- Max-Delbrück Center for Molecular Medicine (MDC), Robert-Rössle-Straße 10, 13125 Berlin-Buch, Germany
| | - Susann Matthes
- Max-Delbrück Center for Molecular Medicine (MDC), Robert-Rössle-Straße 10, 13125 Berlin-Buch, Germany
| | - Michael Bader
- Max-Delbrück Center for Molecular Medicine (MDC), Robert-Rössle-Straße 10, 13125 Berlin-Buch, Germany; University of Lübeck, Institute for Biology, Ratzeburger Allee 160, 23562 Lübeck, Germany; Charité University Medicine, Charitéplatz 1, 10117 Berlin, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
| | - Ario de Marco
- Laboratory for Environmental and Life Sciences, University of Nova Gorica, Slovenia
| | - Paola Storici
- Elettra Sincrotrone Trieste - SS 14 - km 163, 5 in Area Science Park, 34149 Basovizza, Trieste, Italy
| | - Marta S Semrau
- Elettra Sincrotrone Trieste - SS 14 - km 163, 5 in Area Science Park, 34149 Basovizza, Trieste, Italy
| | - Peggy Stolt-Bergner
- Vienna Biocenter Core Facilities GmbH, Dr. Bohr-gasse 3, 1030 Vienna, Austria
| | - Christian Aigner
- Vienna Biocenter Core Facilities GmbH, Dr. Bohr-gasse 3, 1030 Vienna, Austria
| | - Sabine Suppmann
- Max-Planck Institute of Biochemistry, Biochemistry Core Facility, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Adi Goldenzweig
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.
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21
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Zhang GJ, Xie TY, Zhou XG, Wang LJ, Hu J. Protein Structure Prediction Using Population-Based Algorithm Guided by Information Entropy. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:697-707. [PMID: 31180869 DOI: 10.1109/tcbb.2019.2921958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Ab initio protein structure prediction is one of the most challenging problems in computational biology. Multistage algorithms are widely used in ab initio protein structure prediction. The different computational costs of a multistage algorithm for different proteins are important to be considered. In this study, a population-based algorithm guided by information entropy (PAIE), which includes exploration and exploitation stages, is proposed for protein structure prediction. In PAIE, an entropy-based stage switch strategy is designed to switch from the exploration stage to the exploitation stage. Torsion angle statistical information is also deduced from the first stage and employed to enhance the exploitation in the second stage. Results indicate that an improvement in the performance of protein structure prediction in a benchmark of 30 proteins and 17 other free modeling targets in CASP.
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22
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Vorobieva AA, White P, Liang B, Horne JE, Bera AK, Chow CM, Gerben S, Marx S, Kang A, Stiving AQ, Harvey SR, Marx DC, Khan GN, Fleming KG, Wysocki VH, Brockwell DJ, Tamm LK, Radford SE, Baker D. De novo design of transmembrane β barrels. Science 2021; 371:eabc8182. [PMID: 33602829 PMCID: PMC8064278 DOI: 10.1126/science.abc8182] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 12/07/2020] [Indexed: 12/12/2022]
Abstract
Transmembrane β-barrel proteins (TMBs) are of great interest for single-molecule analytical technologies because they can spontaneously fold and insert into membranes and form stable pores, but the range of pore properties that can be achieved by repurposing natural TMBs is limited. We leverage the power of de novo computational design coupled with a "hypothesis, design, and test" approach to determine TMB design principles, notably, the importance of negative design to slow β-sheet assembly. We design new eight-stranded TMBs, with no homology to known TMBs, that insert and fold reversibly into synthetic lipid membranes and have nuclear magnetic resonance and x-ray crystal structures very similar to the computational models. These advances should enable the custom design of pores for a wide range of applications.
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Affiliation(s)
- Anastassia A Vorobieva
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - Paul White
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, USA
| | - Binyong Liang
- Department of Molecular Physiology and Biological Physics and Center for Membrane and Cell Physiology, University of Virginia, Charlottesville, VA 22903, USA
| | - Jim E Horne
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, USA
| | - Asim K Bera
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Cameron M Chow
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Stacey Gerben
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Sinduja Marx
- Department of Molecular Engineering and Sciences, University of Washington, Seattle, WA 98195, USA
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Alyssa Q Stiving
- Department of Chemistry and Biochemistry, Resource for Native Mass Spectrometry Guided Structural Biology, The Ohio State University, Columbus, OH 43210, USA
| | - Sophie R Harvey
- Department of Chemistry and Biochemistry, Resource for Native Mass Spectrometry Guided Structural Biology, The Ohio State University, Columbus, OH 43210, USA
| | - Dagan C Marx
- TC Jenkins Department of Biophysics Johns Hopkins University, Baltimore, MD 21218, USA
| | - G Nasir Khan
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, USA
| | - Karen G Fleming
- TC Jenkins Department of Biophysics Johns Hopkins University, Baltimore, MD 21218, USA
| | - Vicki H Wysocki
- Department of Chemistry and Biochemistry, Resource for Native Mass Spectrometry Guided Structural Biology, The Ohio State University, Columbus, OH 43210, USA
| | - David J Brockwell
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, USA
| | - Lukas K Tamm
- Department of Molecular Physiology and Biological Physics and Center for Membrane and Cell Physiology, University of Virginia, Charlottesville, VA 22903, USA
| | - Sheena E Radford
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
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23
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Jernigan RL, Sankar K, Jia K, Faraggi E, Kloczkowski A. Computational Ways to Enhance Protein Inhibitor Design. Front Mol Biosci 2021; 7:607323. [PMID: 33614705 PMCID: PMC7886686 DOI: 10.3389/fmolb.2020.607323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 12/08/2020] [Indexed: 11/22/2022] Open
Abstract
Two new computational approaches are described to aid in the design of new peptide-based drugs by evaluating ensembles of protein structures from their dynamics and through the assessing of structures using empirical contact potential. These approaches build on the concept that conformational variability can aid in the binding process and, for disordered proteins, can even facilitate the binding of more diverse ligands. This latter consideration indicates that such a design process should be less restrictive so that multiple inhibitors might be effective. The example chosen here focuses on proteins/peptides that bind to hemagglutinin (HA) to block the large-scale conformational change for activation. Variability in the conformations is considered from sets of experimental structures, or as an alternative, from their simple computed dynamics; the set of designe peptides/small proteins from the David Baker lab designed to bind to hemagglutinin, is the large set considered and is assessed with the new empirical contact potentials.
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Affiliation(s)
- Robert L. Jernigan
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA, United States
| | - Kannan Sankar
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA, United States
| | - Kejue Jia
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA, United States
| | - Eshel Faraggi
- Research and Information Systems, LLC, Indianapolis, IN, United States
- Department of Physics, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States
| | - Andrzej Kloczkowski
- Battelle Center for Mathematical Medicine, Nationwide Children's Hospital, Columbus, OH, United States
- Department of Pediatrics, The Ohio State University, Columbus, OH, United States
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24
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Prediction of Protein-Protein Binding Interactions in Dimeric Coiled Coils by Information Contained in Folding Energy Landscapes. Int J Mol Sci 2021; 22:ijms22031368. [PMID: 33573048 PMCID: PMC7866404 DOI: 10.3390/ijms22031368] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/22/2021] [Accepted: 01/25/2021] [Indexed: 11/16/2022] Open
Abstract
Coiled coils represent the simplest form of a complex formed between two interacting protein partners. Their extensive study has led to the development of various methods aimed towards the investigation and design of complex forming interactions. Despite the progress that has been made to predict the binding affinities for protein complexes, and specifically those tailored towards coiled coils, many challenges still remain. In this work, we explore whether the information contained in dimeric coiled coil folding energy landscapes can be used to predict binding interactions. Using the published SYNZIP dataset, we start from the amino acid sequence, to simultaneously fold and dock approximately 1000 coiled coil dimers. Assessment of the folding energy landscapes showed that a model based on the calculated number of clusters for the lowest energy structures displayed a signal that correlates with the experimentally determined protein interactions. Although the revealed correlation is weak, we show that such correlation exists; however, more work remains to establish whether further improvements can be made to the presented model.
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25
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Lipsh-Sokolik R, Listov D, Fleishman SJ. The AbDesign computational pipeline for modular backbone assembly and design of binders and enzymes. Protein Sci 2020; 30:151-159. [PMID: 33040418 PMCID: PMC7737780 DOI: 10.1002/pro.3970] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/07/2020] [Accepted: 10/09/2020] [Indexed: 12/12/2022]
Abstract
The functional sites of many protein families are dominated by diverse backbone regions that lack secondary structure (loops) but fold stably into their functionally competent state. Nevertheless, the design of structured loop regions from scratch, especially in functional sites, has met with great difficulty. We therefore developed an approach, called AbDesign, to exploit the natural modularity of many protein families and computationally assemble a large number of new backbones by combining naturally occurring modular fragments. This strategy yielded large, atomically accurate, and highly efficient proteins, including antibodies and enzymes exhibiting dozens of mutations from any natural protein. The combinatorial backbone‐conformation space that can be accessed by AbDesign even for a modestly sized family of homologs may exceed the diversity in the entire PDB, providing the sub‐Ångstrom level of control over the positioning of active‐site groups that is necessary for obtaining highly active proteins. This manuscript describes how to implement the pipeline using code that is freely available at https://github.com/Fleishman‐Lab/AbDesign_for_enzymes.
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Affiliation(s)
| | - Dina Listov
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
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26
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Weinstein J, Khersonsky O, Fleishman SJ. Practically useful protein-design methods combining phylogenetic and atomistic calculations. Curr Opin Struct Biol 2020; 63:58-64. [PMID: 32505941 DOI: 10.1016/j.sbi.2020.04.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 04/06/2020] [Indexed: 12/11/2022]
Abstract
Our ability to design new or improved biomolecular activities depends on understanding the sequence-function relationships in proteins. The large size and fold complexity of most proteins, however, obscure these relationships, and protein-optimization methods continue to rely on laborious experimental iterations. Recently, a deeper understanding of the roles of stability-threshold effects and biomolecular epistasis in proteins has led to the development of hybrid methods that combine phylogenetic analysis with atomistic design calculations. These methods enable reliable and even single-step optimization of protein stability, expressibility, and activity in proteins that were considered outside the scope of computational design. Furthermore, ancestral-sequence reconstruction produces insights on missing links in the evolution of enzymes and binders that may be used in protein design. Through the combination of phylogenetic and atomistic calculations, the long-standing goal of general computational methods that can be universally applied to study and optimize proteins finally seems within reach.
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Affiliation(s)
- Jonathan Weinstein
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Olga Khersonsky
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.
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27
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Tweedy SE, Rodríguez Benítez A, Narayan ARH, Zimmerman PM, Brooks CL, Wymore T. Hydroxyl Radical-Coupled Electron-Transfer Mechanism of Flavin-Dependent Hydroxylases. J Phys Chem B 2019; 123:8065-8073. [PMID: 31532200 DOI: 10.1021/acs.jpcb.9b08178] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Class A flavin-dependent hydroxylases (FdHs) catalyze the hydroxylation of organic compounds in a site- and stereoselective manner. In stark contrast, conventional synthetic routes require environmentally hazardous reagents and give modest yields. Thus, understanding the detailed mechanism of this class of enzymes is essential to their rational manipulation for applications in green chemistry and pharmaceutical production. Both electrophilic substitution and radical intermediate mechanisms have been proposed as interpretations of FdH hydroxylation rates and optical spectra. While radical mechanistic steps are often difficult to examine directly, modern quantum chemistry calculations combined with statistical mechanical approaches can yield detailed mechanistic models providing insights that can be used to differentiate reaction pathways. In the current work, we report quantum mechanical/molecular mechanical (QM/MM) calculations on the fungal TropB enzyme that shows an alternative reaction pathway in which hydroxylation through a hydroxyl radical-coupled electron-transfer mechanism is significantly favored over electrophilic substitution. Furthermore, QM/MM calculations on several modified flavins provide a more consistent interpretation of the experimental trends in the reaction rates seen experimentally for a related enzyme, para-hydroxybenzoate hydroxylase. These calculations should guide future enzyme and substrate design strategies and broaden the scope of biological spin chemistry.
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28
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Chen J, Schafer NP, Wolynes PG, Clementi C. Localizing Frustration in Proteins Using All-Atom Energy Functions. J Phys Chem B 2019; 123:4497-4504. [PMID: 31063375 DOI: 10.1021/acs.jpcb.9b01545] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The problems of protein folding and protein design are two sides of the same coin. Protein folding involves exploring a protein's configuration space given a fixed sequence, whereas protein design involves searching in sequence space given a particular target structure. For a protein to fold quickly and reliably, its energy landscape must be biased toward the folded ensemble throughout its configuration space and must lack deep kinetic traps that would otherwise frustrate folding. Evolution has "designed" the sequences of many naturally occurring proteins, through an eons-long process of random mutation and selection, to yield landscapes with a minimal degree of frustration. The task facing humans hoping to design protein sequences that fold into particular structures is to use the available approximate energy functions to sculpt funneled landscapes that work in the laboratory. In this work, we demonstrate how to calculate several localized frustration measures using an all-atom energy function. Specifically, we employ the Rosetta energy function, which has been used successfully to design proteins and which has a natural pairwise decomposition that is suitably solvent-averaged. We calculate these newly developed frustration measures for both a mutated WW domain, FiP35, and a three-helix bundle that was designed completely by humans, Alpha3D. The structure of FiP35 exhibits less localized frustration than that of Alpha3D. A mutation toward the consensus sequence for WW domains in FiP35, which has been shown unexpectedly in experiment to disrupt folding, induces localized frustration by disrupting the hydrophobic core. By performing a limited redesign on the sequence of Alpha3D, we show that some, but not all, mutations that lower the energy also result in decreased frustration. The results suggest that, in addition to being useful for detecting residual frustration in protein structures, optimizing the localized frustration measures presented here may be a useful and automatic means of balancing positive and negative design in protein design tasks.
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29
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Kundert K, Kortemme T. Computational design of structured loops for new protein functions. Biol Chem 2019; 400:275-288. [PMID: 30676995 PMCID: PMC6530579 DOI: 10.1515/hsz-2018-0348] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 12/18/2018] [Indexed: 12/20/2022]
Abstract
The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.
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Affiliation(s)
- Kale Kundert
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Tanja Kortemme
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158, USA
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30
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Sormanni P, Aprile FA, Vendruscolo M. Third generation antibody discovery methods: in silico rational design. Chem Soc Rev 2018; 47:9137-9157. [PMID: 30298157 DOI: 10.1039/c8cs00523k] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.
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Affiliation(s)
- Pietro Sormanni
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK.
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31
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Netzer R, Listov D, Lipsh R, Dym O, Albeck S, Knop O, Kleanthous C, Fleishman SJ. Ultrahigh specificity in a network of computationally designed protein-interaction pairs. Nat Commun 2018; 9:5286. [PMID: 30538236 PMCID: PMC6290019 DOI: 10.1038/s41467-018-07722-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 11/21/2018] [Indexed: 01/21/2023] Open
Abstract
Protein networks in all organisms comprise homologous interacting pairs. In these networks, some proteins are specific, interacting with one or a few binding partners, whereas others are multispecific and bind a range of targets. We describe an algorithm that starts from an interacting pair and designs dozens of new pairs with diverse backbone conformations at the binding site as well as new binding orientations and sequences. Applied to a high-affinity bacterial pair, the algorithm results in 18 new ones, with cognate affinities from pico- to micromolar. Three pairs exhibit 3-5 orders of magnitude switch in specificity relative to the wild type, whereas others are multispecific, collectively forming a protein-interaction network. Crystallographic analysis confirms design accuracy, including in new backbones and polar interactions. Preorganized polar interaction networks are responsible for high specificity, thus defining design principles that can be applied to program synthetic cellular interaction networks of desired affinity and specificity. The molecular basis of ultrahigh specificity in protein-protein interactions remains obscure. The authors present a computational method to design atomically accurate new pairs exhibiting >100,000-fold specificity switches, generating a large and complex interaction network.
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Affiliation(s)
- Ravit Netzer
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Dina Listov
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Rosalie Lipsh
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Orly Dym
- Structural Proteomics Unit, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Shira Albeck
- Structural Proteomics Unit, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Orli Knop
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Colin Kleanthous
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel.
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32
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Goldenzweig A, Fleishman SJ. Principles of Protein Stability and Their Application in Computational Design. Annu Rev Biochem 2018; 87:105-129. [PMID: 29401000 DOI: 10.1146/annurev-biochem-062917-012102] [Citation(s) in RCA: 162] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Proteins are increasingly used in basic and applied biomedical research. Many proteins, however, are only marginally stable and can be expressed in limited amounts, thus hampering research and applications. Research has revealed the thermodynamic, cellular, and evolutionary principles and mechanisms that underlie marginal stability. With this growing understanding, computational stability design methods have advanced over the past two decades starting from methods that selectively addressed only some aspects of marginal stability. Current methods are more general and, by combining phylogenetic analysis with atomistic design, have shown drastic improvements in solubility, thermal stability, and aggregation resistance while maintaining the protein's primary molecular activity. Stability design is opening the way to rational engineering of improved enzymes, therapeutics, and vaccines and to the application of protein design methodology to large proteins and molecular activities that have proven challenging in the past.
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Affiliation(s)
- Adi Goldenzweig
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel;
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel;
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33
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Abstract
Natural proteins must both fold into a stable conformation and exert their molecular function. To date, computational design has successfully produced stable and atomically accurate proteins by using so-called "ideal" folds rich in regular secondary structures and almost devoid of loops and destabilizing elements, such as cavities. Molecular function, such as binding and catalysis, however, often demands nonideal features, including large and irregular loops and buried polar interaction networks, which have remained challenging for fold design. Through five design/experiment cycles, we learned principles for designing stable and functional antibody variable fragments (Fvs). Specifically, we (i) used sequence-design constraints derived from antibody multiple-sequence alignments, and (ii) during backbone design, maintained stabilizing interactions observed in natural antibodies between the framework and loops of complementarity-determining regions (CDRs) 1 and 2. Designed Fvs bound their ligands with midnanomolar affinities and were as stable as natural antibodies, despite having >30 mutations from mammalian antibody germlines. Furthermore, crystallographic analysis demonstrated atomic accuracy throughout the framework and in four of six CDRs in one design and atomic accuracy in the entire Fv in another. The principles we learned are general, and can be implemented to design other nonideal folds, generating stable, specific, and precise antibodies and enzymes.
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34
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Negahdaripour M, Golkar N, Hajighahramani N, Kianpour S, Nezafat N, Ghasemi Y. Harnessing self-assembled peptide nanoparticles in epitope vaccine design. Biotechnol Adv 2017; 35:575-596. [PMID: 28522213 PMCID: PMC7127164 DOI: 10.1016/j.biotechadv.2017.05.002] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 04/23/2017] [Accepted: 05/11/2017] [Indexed: 12/11/2022]
Abstract
Vaccination has been one of the most successful breakthroughs in medical history. In recent years, epitope-based subunit vaccines have been introduced as a safer alternative to traditional vaccines. However, they suffer from limited immunogenicity. Nanotechnology has shown value in solving this issue. Different kinds of nanovaccines have been employed, among which virus-like nanoparticles (VLPs) and self-assembled peptide nanoparticles (SAPNs) seem very promising. Recently, SAPNs have attracted special interest due to their unique properties, including molecular specificity, biodegradability, and biocompatibility. They also resemble pathogens in terms of their size. Their multivalency allows an orderly repetitive display of antigens on their surface, which induces a stronger immune response than single immunogens. In vaccine design, SAPN self-adjuvanticity is regarded an outstanding advantage, since the use of toxic adjuvants is no longer required. SAPNs are usually composed of helical or β-sheet secondary structures and are tailored from natural peptides or de novo structures. Flexibility in subunit selection opens the door to a wide variety of molecules with different characteristics. SAPN engineering is an emerging area, and more novel structures are expected to be generated in the future, particularly with the rapid progress in related computational tools. The aim of this review is to provide a state-of-the-art overview of self-assembled peptide nanoparticles and their use in vaccine design in recent studies. Additionally, principles for their design and the application of computational approaches to vaccine design are summarized.
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Affiliation(s)
- Manica Negahdaripour
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran; Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Nasim Golkar
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Pharmaceutics Department, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Nasim Hajighahramani
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran; Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Sedigheh Kianpour
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran; Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Navid Nezafat
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Younes Ghasemi
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran; Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Department of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran; Biotechnology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
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35
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Nanda V, Belure SV, Shir OM. Searching for the Pareto frontier in multi-objective protein design. Biophys Rev 2017; 9:339-344. [PMID: 28799089 DOI: 10.1007/s12551-017-0288-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Accepted: 07/25/2017] [Indexed: 12/26/2022] Open
Abstract
The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence-structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set-designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multi-objective protein design, the development of Pareto optimization methods, and present a specific case study using multi-objective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides.
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Affiliation(s)
- Vikas Nanda
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, NJ, USA.
- Department of Biochemistry and Molecular Biophysics, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA.
| | - Sandeep V Belure
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, NJ, USA
- Department of Biochemistry and Molecular Biophysics, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
| | - Ofer M Shir
- Department of Computer Science, Tel-Hai College, Kiryat Shmona, Upper Galilee, Israel
- The Galilee Research Institute-Migal, Kiryat Shmona, Upper Galilee, Israel
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36
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Hansen WA, Khare SD. Benchmarking a computational design method for the incorporation of metal ion-binding sites at symmetric protein interfaces. Protein Sci 2017; 26:1584-1594. [PMID: 28513090 PMCID: PMC5521545 DOI: 10.1002/pro.3194] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2017] [Revised: 04/07/2017] [Accepted: 05/06/2017] [Indexed: 11/12/2022]
Abstract
The design of novel metal-ion binding sites along symmetric axes in protein oligomers could provide new avenues for metalloenzyme design, construction of protein-based nanomaterials and novel ion transport systems. Here, we describe a computational design method, symmetric protein recursive ion-cofactor sampling (SyPRIS), for locating constellations of backbone positions within oligomeric protein structures that are capable of supporting desired symmetrically coordinated metal ion(s) chelated by sidechains (chelant model). Using SyPRIS on a curated benchmark set of protein structures with symmetric metal binding sites, we found high recovery of native metal coordinating rotamers: in 65 of the 67 (97.0%) cases, native rotamers featured in the best scoring model while in the remaining cases native rotamers were found within the top three scoring models. In a second test, chelant models were crossmatched against protein structures with identical cyclic symmetry. In addition to recovering all native placements, 10.4% (8939/86013) of the non-native placements, had acceptable geometric compatibility scores. Discrimination between native and non-native metal site placements was further enhanced upon constrained energy minimization using the Rosetta energy function. Upon sequence design of the surrounding first-shell residues, we found further stabilization of native placements and a small but significant (1.7%) number of non-native placement-based sites with favorable Rosetta energies, indicating their designability in existing protein interfaces. The generality of the SyPRIS approach allows design of novel symmetric metal sites including with non-natural amino acid sidechains, and should enable the predictive incorporation of a variety of metal-containing cofactors at symmetric protein interfaces.
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Affiliation(s)
- William A. Hansen
- Institute for Quantitative Biomedicine at Rutgers610 Taylor RoadPiscatawayNew Jersey08854
- Center for integrative Proteomics Research610 Taylor RoadPiscatawayNew Jersey08854
| | - Sagar D. Khare
- Institute for Quantitative Biomedicine at Rutgers610 Taylor RoadPiscatawayNew Jersey08854
- Center for integrative Proteomics Research610 Taylor RoadPiscatawayNew Jersey08854
- Chemistry and Chemical Biology at Rutgers610 Taylor RoadPiscatawayNew Jersey08854
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37
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Jain S, Jou JD, Georgiev IS, Donald BR. A critical analysis of computational protein design with sparse residue interaction graphs. PLoS Comput Biol 2017; 13:e1005346. [PMID: 28358804 PMCID: PMC5391103 DOI: 10.1371/journal.pcbi.1005346] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 04/13/2017] [Accepted: 01/03/2017] [Indexed: 11/19/2022] Open
Abstract
Protein design algorithms enumerate a combinatorial number of candidate structures to compute the Global Minimum Energy Conformation (GMEC). To efficiently find the GMEC, protein design algorithms must methodically reduce the conformational search space. By applying distance and energy cutoffs, the protein system to be designed can thus be represented using a sparse residue interaction graph, where the number of interacting residue pairs is less than all pairs of mutable residues, and the corresponding GMEC is called the sparse GMEC. However, ignoring some pairwise residue interactions can lead to a change in the energy, conformation, or sequence of the sparse GMEC vs. the original or the full GMEC. Despite the widespread use of sparse residue interaction graphs in protein design, the above mentioned effects of their use have not been previously analyzed. To analyze the costs and benefits of designing with sparse residue interaction graphs, we computed the GMECs for 136 different protein design problems both with and without distance and energy cutoffs, and compared their energies, conformations, and sequences. Our analysis shows that the differences between the GMECs depend critically on whether or not the design includes core, boundary, or surface residues. Moreover, neglecting long-range interactions can alter local interactions and introduce large sequence differences, both of which can result in significant structural and functional changes. Designs on proteins with experimentally measured thermostability show it is beneficial to compute both the full and the sparse GMEC accurately and efficiently. To this end, we show that a provable, ensemble-based algorithm can efficiently compute both GMECs by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine sparse residue interaction graphs with provable, ensemble-based algorithms to reap the benefits of sparse residue interaction graphs while avoiding their potential inaccuracies. Computational structure-based protein design algorithms have successfully redesigned proteins to fold and bind target substrates in vitro, and even in vivo. Because the complexity of a computational design increases dramatically with the number of mutable residues, many design algorithms employ cutoffs (distance or energy) to neglect some pairwise residue interactions, thereby reducing the effective search space and computational cost. However, the energies neglected by such cutoffs can add up, which may have nontrivial effects on the designed sequence and its function. To study the effects of using cutoffs on protein design, we computed the optimal sequence both with and without cutoffs, and showed that neglecting long-range interactions can significantly change the computed conformation and sequence. Designs on proteins with experimentally measured thermostability showed the benefits of computing the optimal sequences (and their conformations), both with and without cutoffs, efficiently and accurately. Therefore, we also showed that a provable, ensemble-based algorithm can efficiently compute the optimal conformation and sequence, both with and without applying cutoffs, by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine cutoffs with provable, ensemble-based algorithms to reap the computational efficiency of cutoffs while avoiding their potential inaccuracies.
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Affiliation(s)
- Swati Jain
- Computational Biology and Bioinformatics Program, Duke University, Durham, North Carolina, United States of America
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Jonathan D. Jou
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Ivelin S. Georgiev
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Bruce R. Donald
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, United States of America
- Department of Chemistry, Duke University, Durham, North Carolina, United States of America
- * E-mail:
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38
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Khersonsky O, Fleishman SJ. Incorporating an allosteric regulatory site in an antibody through backbone design. Protein Sci 2017; 26:807-813. [PMID: 28142198 DOI: 10.1002/pro.3126] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2016] [Revised: 01/20/2017] [Accepted: 01/24/2017] [Indexed: 12/17/2022]
Abstract
Allosteric regulation underlies living cells' ability to sense changes in nutrient and signaling-molecule concentrations, but the ability to computationally design allosteric regulation into non-allosteric proteins has been elusive. Allosteric-site design is complicated by the requirement to encode the relative stabilities of active and inactive conformations of the same protein in the presence and absence of both ligand and effector. To address this challenge, we used Rosetta to design the backbone of the flexible heavy-chain complementarity-determining region 3 (HCDR3), and used geometric matching and sequence optimization to place a Zn2+ -coordination site in a fluorescein-binding antibody. We predicted that due to HCDR3's flexibility, the fluorescein-binding pocket would configure properly only upon Zn2+ application. We found that regulation by Zn2+ was reversible and sensitive to the divalent ion's identity, and came at the cost of reduced antibody stability and fluorescein-binding affinity. Fluorescein bound at an order of magnitude higher affinity in the presence of Zn2+ than in its absence, and the increase in fluorescein affinity was due almost entirely to faster fluorescein on-rate, suggesting that Zn2+ preorganized the antibody for fluorescein binding. Mutation analysis demonstrated the extreme sensitivity of Zn2+ regulation on the atomic details in and around the metal-coordination site. The designed antibody could serve to study how allosteric regulation evolved from non-allosteric binding proteins, and suggests a way to designing molecular sensors for environmental and biomedical targets.
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Affiliation(s)
- Olga Khersonsky
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
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39
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Liu X, Taylor RD, Griffin L, Coker SF, Adams R, Ceska T, Shi J, Lawson ADG, Baker T. Computational design of an epitope-specific Keap1 binding antibody using hotspot residues grafting and CDR loop swapping. Sci Rep 2017; 7:41306. [PMID: 28128368 PMCID: PMC5269676 DOI: 10.1038/srep41306] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 12/19/2016] [Indexed: 12/20/2022] Open
Abstract
Therapeutic and diagnostic applications of monoclonal antibodies often require careful selection of binders that recognize specific epitopes on the target molecule to exert a desired modulation of biological function. Here we present a proof-of-concept application for the rational design of an epitope-specific antibody binding with the target protein Keap1, by grafting pre-defined structural interaction patterns from the native binding partner protein, Nrf2, onto geometrically matched positions of a set of antibody scaffolds. The designed antibodies bind to Keap1 and block the Keap1-Nrf2 interaction in an epitope-specific way. One resulting antibody is further optimised to achieve low-nanomolar binding affinity by in silico redesign of the CDRH3 sequences. An X-ray co-crystal structure of one resulting design reveals that the actual binding orientation and interface with Keap1 is very close to the design model, despite an unexpected CDRH3 tilt and VH/VL interface deviation, which indicates that the modelling precision may be improved by taking into account simultaneous CDR loops conformation and VH/VL orientation optimisation upon antibody sequence change. Our study confirms that, given a pre-existing crystal structure of the target protein-protein interaction, hotspots grafting with CDR loop swapping is an attractive route to the rational design of an antibody targeting a pre-selected epitope.
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Affiliation(s)
- Xiaofeng Liu
- UCB Celltech, 216 Bath Road, Slough, United Kingdom
| | | | | | | | - Ralph Adams
- UCB Celltech, 216 Bath Road, Slough, United Kingdom
| | - Tom Ceska
- UCB Celltech, 216 Bath Road, Slough, United Kingdom
| | - Jiye Shi
- UCB Pharma, Chemin du Foriest 1, B-1420 Braine-l'Alleud, Belgium
| | | | - Terry Baker
- UCB Celltech, 216 Bath Road, Slough, United Kingdom
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40
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Jeon J, Shell MS. Peptide binding landscapes: Specificity and homophilicity across sequence space in a lattice model. Phys Rev E 2016; 94:042405. [PMID: 27841641 DOI: 10.1103/physreve.94.042405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Indexed: 11/07/2022]
Abstract
Peptide aggregation frequently involves sequences with strong homophilic binding character, i.e., sequences that self-assemble with like species in a crowded cellular environment, in the face of a multitude of other peptides or proteins as potential heterophilic binding partners. What kinds of sequences display a strong tendency towards homophilic binding and self-assembly, and what are the origins of this behavior? Here, we consider how sequence specificity in oligomerization processes plays out in a simple two-dimensional (2D) lattice statistical-thermodynamic peptide model that permits exhaustive examination of the entire sequence and configurational landscapes. We find that sequences with strong self-specificities have either alternating hydrophobic and hydrophilic residues or short patches of hydrophobic residues, both which minimize intramolecular hydrophobic interactions in part due to the constraints of the 2D lattice. We also find that these specificities are highly sensitive to entropic and free energetic features of the unbound conformational state, such that direct binding interaction energies alone do not capture the complete behavior. These results suggest that the ability of particular peptide sequences to self-assemble and aggregate in a many-protein environment reflects a precise balance of direct binding interactions and behavior in the unbound (monomeric) state.
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Affiliation(s)
- Joohyun Jeon
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106-5080, USA
| | - M Scott Shell
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106-5080, USA
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41
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Norn CH, André I. Computational design of protein self-assembly. Curr Opin Struct Biol 2016; 39:39-45. [DOI: 10.1016/j.sbi.2016.04.002] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Revised: 04/07/2016] [Accepted: 04/07/2016] [Indexed: 01/29/2023]
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42
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Venev SV, Zeldovich KB. Massively parallel sampling of lattice proteins reveals foundations of thermal adaptation. J Chem Phys 2016; 143:055101. [PMID: 26254668 DOI: 10.1063/1.4927565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Evolution of proteins in bacteria and archaea living in different conditions leads to significant correlations between amino acid usage and environmental temperature. The origins of these correlations are poorly understood, and an important question of protein theory, physics-based prediction of types of amino acids overrepresented in highly thermostable proteins, remains largely unsolved. Here, we extend the random energy model of protein folding by weighting the interaction energies of amino acids by their frequencies in protein sequences and predict the energy gap of proteins designed to fold well at elevated temperatures. To test the model, we present a novel scalable algorithm for simultaneous energy calculation for many sequences in many structures, targeting massively parallel computing architectures such as graphics processing unit. The energy calculation is performed by multiplying two matrices, one representing the complete set of sequences, and the other describing the contact maps of all structural templates. An implementation of the algorithm for the CUDA platform is available at http://www.github.com/kzeldovich/galeprot and calculates protein folding energies over 250 times faster than a single central processing unit. Analysis of amino acid usage in 64-mer cubic lattice proteins designed to fold well at different temperatures demonstrates an excellent agreement between theoretical and simulated values of energy gap. The theoretical predictions of temperature trends of amino acid frequencies are significantly correlated with bioinformatics data on 191 bacteria and archaea, and highlight protein folding constraints as a fundamental selection pressure during thermal adaptation in biological evolution.
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Affiliation(s)
- Sergey V Venev
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 368 Plantation St, Worcester, Massachusetts 01605, USA
| | - Konstantin B Zeldovich
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 368 Plantation St, Worcester, Massachusetts 01605, USA
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43
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Maximova T, Moffatt R, Ma B, Nussinov R, Shehu A. Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics. PLoS Comput Biol 2016; 12:e1004619. [PMID: 27124275 PMCID: PMC4849799 DOI: 10.1371/journal.pcbi.1004619] [Citation(s) in RCA: 132] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.
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Affiliation(s)
- Tatiana Maximova
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
| | - Ryan Moffatt
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
| | - Buyong Ma
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland, United States of America
| | - Ruth Nussinov
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland, United States of America
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
- Department of Biongineering, George Mason University, Fairfax, Virginia, United States of America
- School of Systems Biology, George Mason University, Manassas, Virginia, United States of America
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44
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Khersonsky O, Fleishman SJ. Why reinvent the wheel? Building new proteins based on ready-made parts. Protein Sci 2016; 25:1179-87. [PMID: 26821641 DOI: 10.1002/pro.2892] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Revised: 01/20/2016] [Accepted: 01/27/2016] [Indexed: 12/12/2022]
Abstract
We protein engineers are ambivalent about evolution: on the one hand, evolution inspires us with myriad examples of biomolecular binders, sensors, and catalysts; on the other hand, these examples are seldom well-adapted to the engineering tasks we have in mind. Protein engineers have therefore modified natural proteins by point substitutions and fragment exchanges in an effort to generate new functions. A counterpoint to such design efforts, which is being pursued now with greater success, is to completely eschew the starting materials provided by nature and to design new protein functions from scratch by using de novo molecular modeling and design. While important progress has been made in both directions, some areas of protein design are still beyond reach. To this end, we advocate a synthesis of these two strategies: by using design calculations to both recombine and optimize fragments from natural proteins, we can build stable and as of yet un-sampled structures, thereby granting access to an expanded repertoire of conformations and desired functions. We propose that future methods that combine phylogenetic analysis, structure and sequence bioinformatics, and atomistic modeling may well succeed where any one of these approaches has failed on its own.
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Affiliation(s)
- Olga Khersonsky
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
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45
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Elazar A, Weinstein J, Biran I, Fridman Y, Bibi E, Fleishman SJ. Mutational scanning reveals the determinants of protein insertion and association energetics in the plasma membrane. eLife 2016; 5:e12125. [PMID: 26824389 PMCID: PMC4786438 DOI: 10.7554/elife.12125] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Accepted: 01/28/2016] [Indexed: 11/13/2022] Open
Abstract
Insertion of helix-forming segments into the membrane and their association determines the structure, function, and expression levels of all plasma membrane proteins. However, systematic and reliable quantification of membrane-protein energetics has been challenging. We developed a deep mutational scanning method to monitor the effects of hundreds of point mutations on helix insertion and self-association within the bacterial inner membrane. The assay quantifies insertion energetics for all natural amino acids at 27 positions across the membrane, revealing that the hydrophobicity of biological membranes is significantly higher than appreciated. We further quantitate the contributions to membrane-protein insertion from positively charged residues at the cytoplasm-membrane interface and reveal large and unanticipated differences among these residues. Finally, we derive comprehensive mutational landscapes in the membrane domains of Glycophorin A and the ErbB2 oncogene, and find that insertion and self-association are strongly coupled in receptor homodimers.
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Affiliation(s)
- Assaf Elazar
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Jonathan Weinstein
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Ido Biran
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Yearit Fridman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Eitan Bibi
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
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46
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Tinberg CE, Khare SD. Improving Binding Affinity and Selectivity of Computationally Designed Ligand-Binding Proteins Using Experiments. Methods Mol Biol 2016; 1414:155-171. [PMID: 27094290 DOI: 10.1007/978-1-4939-3569-7_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The ability to de novo design proteins that can bind small molecules has wide implications for synthetic biology and medicine. Combining computational protein design with the high-throughput screening of mutagenic libraries of computationally designed proteins is emerging as a general approach for creating binding proteins with programmable binding modes, affinities, and selectivities. The computational step enables the creation of a binding site in a protein that otherwise does not (measurably) bind the intended ligand, and targeted mutagenic screening allows for validation and refinement of the computational model as well as provides orders-of-magnitude increases in the binding affinity. Deep sequencing of mutagenic libraries can provide insights into the mutagenic binding landscape and enable further affinity improvements. Moreover, in such a combined computational-experimental approach where the binding mode is preprogrammed and iteratively refined, selectivity can be achieved (and modulated) by the placement of specified amino acid side chain groups around the ligand in defined orientations. Here, we describe the experimental aspects of a combined computational-experimental approach for designing-using the software suite Rosetta-proteins that bind a small molecule of choice and engineering, using fluorescence-activated cell sorting and high-throughput yeast surface display, high affinity and ligand selectivity. We illustrated the utility of this approach by performing the design of a selective digoxigenin (DIG)-binding protein that, after affinity maturation, binds DIG with picomolar affinity and high selectivity over structurally related steroids.
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Affiliation(s)
- Christine E Tinberg
- Department of Biochemistry, University of Washington, Seattle, WA, 98109, USA.
- Amgen, South San Francisco, CA, 94080, USA.
| | - Sagar D Khare
- Department of Chemistry and Chemical Biology, Rutgers State University of New Jersey, Piscataway, NJ, 08854, USA
- Center for Integrative Proteomics Research, Rutgers State University of New Jersey, Piscataway, NJ, 08854, USA
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47
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Pseudocontact Shift-Driven Iterative Resampling for 3D Structure Determinations of Large Proteins. J Mol Biol 2016; 428:522-32. [DOI: 10.1016/j.jmb.2016.01.007] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Revised: 01/08/2016] [Accepted: 01/08/2016] [Indexed: 01/23/2023]
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48
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Hauser K, Essuman B, He Y, Coutsias E, Garcia-Diaz M, Simmerling C. A human transcription factor in search mode. Nucleic Acids Res 2015; 44:63-74. [PMID: 26673724 PMCID: PMC4705650 DOI: 10.1093/nar/gkv1091] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Accepted: 10/07/2015] [Indexed: 12/14/2022] Open
Abstract
Transcription factors (TF) can change shape to bind and recognize DNA, shifting the energy landscape from a weak binding, rapid search mode to a higher affinity recognition mode. However, the mechanism(s) driving this conformational change remains unresolved and in most cases high-resolution structures of the non-specific complexes are unavailable. Here, we investigate the conformational switch of the human mitochondrial transcription termination factor MTERF1, which has a modular, superhelical topology complementary to DNA. Our goal was to characterize the details of the non-specific search mode to complement the crystal structure of the specific binding complex, providing a basis for understanding the recognition mechanism. In the specific complex, MTERF1 binds a significantly distorted and unwound DNA structure, exhibiting a protein conformation incompatible with binding to B-form DNA. In contrast, our simulations of apo MTERF1 revealed significant flexibility, sampling structures with superhelical pitch and radius complementary to the major groove of B-DNA. Docking these structures to B-DNA followed by unrestrained MD simulations led to a stable complex in which MTERF1 was observed to undergo spontaneous diffusion on the DNA. Overall, the data support an MTERF1-DNA binding and recognition mechanism driven by intrinsic dynamics of the MTERF1 superhelical topology.
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Affiliation(s)
- Kevin Hauser
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
| | | | - Yiqing He
- Great Neck South High School, Great Neck, NY 11023, USA
| | - Evangelos Coutsias
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Miguel Garcia-Diaz
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY 11794, USA
| | - Carlos Simmerling
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
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49
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Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening. Structure 2015; 23:2377-2386. [PMID: 26526849 DOI: 10.1016/j.str.2015.09.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Revised: 09/14/2015] [Accepted: 09/15/2015] [Indexed: 12/14/2022]
Abstract
Recent benchmark studies have demonstrated the difficulties in obtaining accurate predictions of ligand binding conformations to comparative models of G-protein-coupled receptors. We have developed a data-driven optimization protocol, which integrates mutational data and structural information from multiple X-ray receptor structures in combination with a fully flexible ligand docking protocol to determine the binding conformation of AR231453, a small-molecule agonist, in the GPR119 receptor. Resulting models converge to one conformation that explains the majority of data from mutation studies and is consistent with the structure-activity relationship for a large number of AR231453 analogs. Another key property of the refined models is their success in separating active ligands from decoys in a large-scale virtual screening. These results demonstrate that mutation-guided receptor modeling can provide predictions of practical value for describing receptor-ligand interactions and drug discovery.
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Parmar AS, Xu F, Pike DH, Belure SV, Hasan NF, Drzewiecki KE, Shreiber DI, Nanda V. Metal Stabilization of Collagen and de Novo Designed Mimetic Peptides. Biochemistry 2015; 54:4987-97. [PMID: 26225466 PMCID: PMC5335877 DOI: 10.1021/acs.biochem.5b00502] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We explore the design of metal binding sites to modulate triple-helix stability of collagen and collagen-mimetic peptides. Globular proteins commonly utilize metals to connect tertiary structural elements that are well separated in sequence, constraining structure and enhancing stability. It is more challenging to engineer structural metals into fibrous protein scaffolds, which lack the extensive tertiary contacts seen in globular proteins. In the collagen triple helix, the structural adjacency of the carboxy-termini of the three chains makes this region an attractive target for introducing metal binding sites. We engineered His3 sites based on structural modeling constraints into a series of designed homotrimeric and heterotrimeric peptides, assessing the capacity of metal binding to improve stability and in the case of heterotrimers, affect specificity of assembly. Notable enhancements in stability for both homo- and heteromeric systems were observed upon addition of zinc(II) and several other metal ions only when all three histidine ligands were present. Metal binding affinities were consistent with the expected Irving-Williams series for imidazole. Unlike other metals tested, copper(II) also bound to peptides lacking histidine ligands. Acetylation of the peptide N-termini prevented copper binding, indicating proline backbone amide metal-coordination at this site. Copper similarly stabilized animal extracted Type I collagen in a metal-specific fashion, highlighting the potential importance of metal homeostasis within the extracellular matrix.
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Affiliation(s)
- Avanish S. Parmar
- Department of Biotechnology & Bioinformatics, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad-500046, Telangana, INDIA
- Center for Advanced Biotechnology and Medicine, Department of Biochemistry and Molecular Biology Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Fei Xu
- Center for Advanced Biotechnology and Medicine, Department of Biochemistry and Molecular Biology Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Douglas H. Pike
- Center for Advanced Biotechnology and Medicine, Department of Biochemistry and Molecular Biology Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Sandeep V. Belure
- Center for Advanced Biotechnology and Medicine, Department of Biochemistry and Molecular Biology Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Nida F. Hasan
- Center for Advanced Biotechnology and Medicine, Department of Biochemistry and Molecular Biology Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Kathryn E. Drzewiecki
- Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey 08854, USA
| | - David I. Shreiber
- Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Vikas Nanda
- Center for Advanced Biotechnology and Medicine, Department of Biochemistry and Molecular Biology Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey 08854, USA
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