101
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Holt GT, Jou JD, Gill NP, Lowegard AU, Martin JW, Madden DR, Donald BR. Computational Analysis of Energy Landscapes Reveals Dynamic Features That Contribute to Binding of Inhibitors to CFTR-Associated Ligand. J Phys Chem B 2019; 123:10441-10455. [PMID: 31697075 DOI: 10.1021/acs.jpcb.9b07278] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The CFTR-associated ligand PDZ domain (CALP) binds to the cystic fibrosis transmembrane conductance regulator (CFTR) and mediates lysosomal degradation of mature CFTR. Inhibition of this interaction has been explored as a therapeutic avenue for cystic fibrosis. Previously, we reported the ensemble-based computational design of a novel peptide inhibitor of CALP, which resulted in the most binding-efficient inhibitor to date. This inhibitor, kCAL01, was designed using osprey and evinced significant biological activity in in vitro cell-based assays. Here, we report a crystal structure of kCAL01 bound to CALP and compare structural features against iCAL36, a previously developed inhibitor of CALP. We compute side-chain energy landscapes for each structure to not only enable approximation of binding thermodynamics but also reveal ensemble features that contribute to the comparatively efficient binding of kCAL01. Finally, we compare the previously reported design ensemble for kCAL01 vs the new crystal structure and show that, despite small differences between the design model and crystal structure, significant biophysical features that enhance inhibitor binding are captured in the design ensemble. This suggests not only that ensemble-based design captured thermodynamically significant features observed in vitro, but also that a design eschewing ensembles would miss the kCAL01 sequence entirely.
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Affiliation(s)
- Graham T Holt
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States.,Program in Computational Biology and Bioinformatics , Duke University , Durham , North Carolina 27708 , United States
| | - Jonathan D Jou
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States
| | - Nicholas P Gill
- Department of Biochemistry & Cell Biology , Geisel School of Medicine at Dartmouth , Hanover , New Hampshire 03755 , United States
| | - Anna U Lowegard
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States.,Program in Computational Biology and Bioinformatics , Duke University , Durham , North Carolina 27708 , United States
| | - Jeffrey W Martin
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States
| | - Dean R Madden
- Department of Biochemistry & Cell Biology , Geisel School of Medicine at Dartmouth , Hanover , New Hampshire 03755 , United States
| | - Bruce R Donald
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States.,Department of Biochemistry , Duke University , Durham , North Carolina 27710 , United States.,Department of Chemistry , Duke University , Durham , North Carolina 27710 , United States
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102
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Kuhlman B. Designing protein structures and complexes with the molecular modeling program Rosetta. J Biol Chem 2019; 294:19436-19443. [PMID: 31699898 DOI: 10.1074/jbc.aw119.008144] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Proteins perform an amazingly diverse set of functions in all aspects of life. Critical to the function of many proteins are the highly specific three-dimensional structures they adopt. For this reason, there is strong interest in learning how to rationally design proteins that adopt user-defined structures. Over the last 25 years, there has been significant progress in the field of computational protein design as rotamer-based sequence optimization protocols have enabled accurate design of protein tertiary and quaternary structure. In this award article, I will summarize how the molecular modeling program Rosetta is used to design new protein structures and describe how we have taken advantage of this capability to create proteins that have important applications in research and medicine. I will highlight three protein design stories: the use of protein interface design to create therapeutic bispecific antibodies, the engineering of light-inducible proteins that can be used to recruit proteins to specific locations in the cell, and the de novo design of new protein structures from pieces of naturally occurring proteins.
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Affiliation(s)
- Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina 27599-7260 .,Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina 27599
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103
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Kuhlman B, Bradley P. Advances in protein structure prediction and design. Nat Rev Mol Cell Biol 2019; 20:681-697. [PMID: 31417196 PMCID: PMC7032036 DOI: 10.1038/s41580-019-0163-x] [Citation(s) in RCA: 387] [Impact Index Per Article: 77.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2019] [Indexed: 12/18/2022]
Abstract
The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.
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Affiliation(s)
- Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC, USA.
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
| | - Philip Bradley
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
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104
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Lin M, Tan J, Xu Z, Huang J, Tian Y, Chen B, Wu Y, Tong Y, Zhu Y. Computational design of enhanced detoxification activity of a zearalenone lactonase from Clonostachys rosea in acidic medium. RSC Adv 2019; 9:31284-31295. [PMID: 35527979 PMCID: PMC9072336 DOI: 10.1039/c9ra04964a] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 09/27/2019] [Indexed: 11/21/2022] Open
Abstract
Computational design of pH-activity profiles for enzymes is of great importance in industrial applications. In this research, a computational strategy was developed to engineer the pH-activity profile of a zearalenone lactonase (ZHD101) from Clonostachys rosea to promote its activity in acidic medium. The active site pK a values of ZHD101 were computationally designed by introducing positively charged lysine mutations on the enzyme surface, and the experimental results showed that two variants, M2(D157K) and M9(E171K), increased the catalytic efficiencies of ZHD101 modestly under acidic conditions. Moreover, two variants, M8(D133K) and M9(E171K), were shown to increase the turnover numbers by 2.73 and 2.06-fold with respect to wild type, respectively, though their apparent Michaelis constants were concomitantly increased. These results imply that the active site pK a value change might affect the pH-activity profile of the enzyme. Our computational strategy for pH-activity profile engineering considers protein stability; therefore, limited experimental validation is needed to discover beneficial mutations under shifted pH conditions.
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Affiliation(s)
- Min Lin
- Department of Chemical Engineering, Tsinghua University Beijing 100084 China
| | - Jian Tan
- Nutrition & Health Research Institute, China National Cereals, Oils and Foodstuffs Corporation (COFCO) Beijing 102209 China
- Beijing Key Lab of Nutrition, Health and Food Safety Beijing 102209 China
- Beijing Livestock Products Quality and Safety Source Control Engineering Technology Research Center Beijing 102209 China
| | - Zhaobin Xu
- Department of Chemical Engineering, Tsinghua University Beijing 100084 China
| | - Jin Huang
- Nutrition & Health Research Institute, China National Cereals, Oils and Foodstuffs Corporation (COFCO) Beijing 102209 China
- Beijing Key Lab of Nutrition, Health and Food Safety Beijing 102209 China
- Beijing Livestock Products Quality and Safety Source Control Engineering Technology Research Center Beijing 102209 China
| | - Ye Tian
- Department of Chemical Engineering, Tsinghua University Beijing 100084 China
| | - Bo Chen
- Nutrition & Health Research Institute, China National Cereals, Oils and Foodstuffs Corporation (COFCO) Beijing 102209 China
- Beijing Key Lab of Nutrition, Health and Food Safety Beijing 102209 China
- Beijing Livestock Products Quality and Safety Source Control Engineering Technology Research Center Beijing 102209 China
| | - Yandong Wu
- National Engineering Research Center of Corn Deep Processing Changchun 130033 Jilin China
| | - Yi Tong
- National Engineering Research Center of Corn Deep Processing Changchun 130033 Jilin China
| | - Yushan Zhu
- Department of Chemical Engineering, Tsinghua University Beijing 100084 China
- MOE Key Lab of Industrial Biocatalysis, Tsinghua University Beijing 100084 China
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105
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Pallarés I, Ventura S. Advances in the Prediction of Protein Aggregation Propensity. Curr Med Chem 2019; 26:3911-3920. [DOI: 10.2174/0929867324666170705121754] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 04/14/2017] [Accepted: 04/20/2017] [Indexed: 12/29/2022]
Abstract
Background:
Protein aggregation into β-sheet-enriched insoluble assemblies is being
found to be associated with an increasing number of debilitating human pathologies, such as Alzheimer’s
disease or type 2 diabetes, but also with premature aging. Furthermore, protein aggregation
represents a major bottleneck in the production and marketing of proteinbased therapeutics.
Thus, the development of methods to accurately forecast the aggregation propensity of a certain
protein is of much value.
Methods/Results:
A myriad of in vitro and in vivo aggregation studies have shown that the aggregation
propensity of a certain polypeptide sequence is highly dependent on its intrinsic properties
and, in most cases, driven by specific short regions of high aggregation propensity. These observations
have fostered the development of a first generation of algorithms aimed to predict protein
aggregation propensities from the protein sequence. A second generation of programs able to map
protein aggregation on protein structures is emerging. Herein, we review the most representative
online accessible predictive tools, emphasizing their main distinctive features and the range of
applications.
Conclusion:
In this review, we describe representative biocomputational approaches to evaluate
the aggregation properties of protein sequences and structures, while illustrating how they can
become very useful tools to target protein aggregation in biomedicine and biotechnology.
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Affiliation(s)
- Irantzu Pallarés
- Institut de Biotecnologia i Biomedicina, Universitat Autonoma de Barcelona, 08193-Bellaterra (Barcelona), Spain
| | - Salvador Ventura
- Institut de Biotecnologia i Biomedicina, Universitat Autonoma de Barcelona, 08193-Bellaterra (Barcelona), Spain
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106
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Langan RA, Boyken SE, Ng AH, Samson JA, Dods G, Westbrook AM, Nguyen TH, Lajoie MJ, Chen Z, Berger S, Mulligan VK, Dueber JE, Novak WRP, El-Samad H, Baker D. De novo design of bioactive protein switches. Nature 2019; 572:205-210. [PMID: 31341284 PMCID: PMC6733528 DOI: 10.1038/s41586-019-1432-8] [Citation(s) in RCA: 158] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 06/19/2019] [Indexed: 02/06/2023]
Abstract
Allosteric regulation of protein function is widespread in biology, but is challenging for de novo protein design as it requires the explicit design of multiple states with comparable free energies. Here we explore the possibility of designing switchable protein systems de novo, through the modulation of competing inter- and intramolecular interactions. We design a static, five-helix 'cage' with a single interface that can interact either intramolecularly with a terminal 'latch' helix or intermolecularly with a peptide 'key'. Encoded on the latch are functional motifs for binding, degradation or nuclear export that function only when the key displaces the latch from the cage. We describe orthogonal cage-key systems that function in vitro, in yeast and in mammalian cells with up to 40-fold activation of function by key. The ability to design switchable protein functions that are controlled by induced conformational change is a milestone for de novo protein design, and opens up new avenues for synthetic biology and cell engineering.
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Affiliation(s)
- Robert A Langan
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure, and Design, University of Washington, Seattle, WA, USA
| | - Scott E Boyken
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Andrew H Ng
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
- The UC Berkeley-UCSF Graduate Program in Bioengineering, UCSF, San Francisco, CA, USA
- The UC Berkeley-UCSF Graduate Program in Bioengineering, UC Berkeley, Berkeley, CA, USA
| | - Jennifer A Samson
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
| | - Galen Dods
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Alexandra M Westbrook
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Taylor H Nguyen
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Marc J Lajoie
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Zibo Chen
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure, and Design, University of Washington, Seattle, WA, USA
| | - Stephanie Berger
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Vikram Khipple Mulligan
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - John E Dueber
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
| | - Walter R P Novak
- Department of Chemistry, Wabash College, Crawfordsville, IN, USA
| | - Hana El-Samad
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
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107
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Wang Y, Peng Y, Zhang B, Zhang X, Li H, Wilson AJ, Mineev KS, Wang X. Targeting trimeric transmembrane domain 5 of oncogenic latent membrane protein 1 using a computationally designed peptide. Chem Sci 2019; 10:7584-7590. [PMID: 31588309 PMCID: PMC6761861 DOI: 10.1039/c9sc02474c] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 06/26/2019] [Indexed: 12/20/2022] Open
Abstract
A peptide inhibitor was designed in silico and validated experimentally to disrupt homotrimeric transmembrane helix assembly.
Protein–protein interactions are involved in diverse biological processes. These interactions are therefore vital targets for drug development. However, the design of peptide modulators targeting membrane-based protein–protein interactions is a challenging goal owing to the lack of experimentally-determined structures and efficient protocols to probe their functions. Here we employed rational peptide design and molecular dynamics simulations to design a membrane-insertable peptide that disrupts the strong trimeric self-association of the fifth transmembrane domain (TMD5) of the oncogenic Epstein–Barr virus (EBV) latent membrane protein-1 (LMP-1). The designed anti-TMD5 peptide formed 1 : 2 heterotrimers with TMD5 in micelles and inhibited TMD5 oligomerization in bacterial membranes. Moreover, the designed peptide inhibited LMP-1 homotrimerization based on NF-κB activity in EVB positive lymphoma cells. The results indicated that the designed anti-TMD5 peptide may represent a promising starting point for elaboration of anti-EBV therapeutics via inhibition of LMP-1 oligomerization. To the best of our knowledge, this represents the first example of disrupting homotrimeric transmembrane helices using a designed peptide inhibitor.
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Affiliation(s)
- Yibo Wang
- Laboratory of Chemical Biology , Changchun Institute of Applied Chemistry , Chinese Academy of Sciences , Changchun , Jilin 130022 , China . .,State Key Laboratory of Oncology in South China , Sun Yat-sen University , Guangzhou , Guangdong 510060 , China
| | - Yinghua Peng
- State Key Laboratory for Molecular Biology of Special Wild Economic Animals , Institute of Special Animal and Plant Sciences , Chinese Academy of Agricultural Sciences , Changchun , Jilin 130112 , China
| | - Bo Zhang
- Laboratory of Chemical Biology , Changchun Institute of Applied Chemistry , Chinese Academy of Sciences , Changchun , Jilin 130022 , China .
| | - Xiaozheng Zhang
- Laboratory of Chemical Biology , Changchun Institute of Applied Chemistry , Chinese Academy of Sciences , Changchun , Jilin 130022 , China .
| | - Hongyuan Li
- Laboratory of Chemical Biology , Changchun Institute of Applied Chemistry , Chinese Academy of Sciences , Changchun , Jilin 130022 , China .
| | - Andrew J Wilson
- School of Chemistry , University of Leeds , Woodhouse Lane , Leeds , LS2 9JT , UK.,Astbury Centre for Structural Molecular Biology , University of Leeds , Woodhouse Lane , Leeds , LS2 9JT , UK
| | - Konstantin S Mineev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry , Russian Academy of Sciences , Moscow , 117997 , Russian
| | - Xiaohui Wang
- Laboratory of Chemical Biology , Changchun Institute of Applied Chemistry , Chinese Academy of Sciences , Changchun , Jilin 130022 , China . .,Department of Applied Chemistry and Engineering , University of Science and Technology of China , Hefei , Anhui 230026 , China
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108
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Tabasinezhad M, Talebkhan Y, Wenzel W, Rahimi H, Omidinia E, Mahboudi F. Trends in therapeutic antibody affinity maturation: From in-vitro towards next-generation sequencing approaches. Immunol Lett 2019; 212:106-113. [PMID: 31247224 DOI: 10.1016/j.imlet.2019.06.009] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 06/08/2019] [Accepted: 06/24/2019] [Indexed: 12/12/2022]
Abstract
Current advances in antibody engineering driving the strongest growth area in biotherapeutic agents development. Affinity improvement that is mainly important for biological activity and clinical efficacy of therapeutic antibodies, has still remained a challenging task. In the human body, during a course of immune response affinity maturation increase antibody activity by several rounds of somatic hypermutation and clonal selection in the germinal center. The final outputs are antibodies representing higher affinity and specificity against a particular antigen. In the realm of biotechnology, exploring of mutations which improve antibody affinity while preserving its specificity and stability is an extremely time-consuming and laborious process. Recent advances in computational algorithms and DNA sequencing technologies help researchers to redesign antibody structure to achieve desired properties such as improved binding affinity. In this review, we briefly described the principle of affinity maturation and different corresponding in vitro techniques. Also, we recapitulated the most recent advancements in the field of antibody affinity maturation including computational approaches and next-generation sequencing (NGS).
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Affiliation(s)
- Maryam Tabasinezhad
- Biotechnology Research Centre, Pasteur Institute of Iran, Tehran, Iran; Institute of Nanotechnology, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Yeganeh Talebkhan
- Biotechnology Research Centre, Pasteur Institute of Iran, Tehran, Iran
| | - Wolfgang Wenzel
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Hamzeh Rahimi
- Molecular Medicine Department, Pasteur Institute of Iran, Tehran, Iran
| | - Eskandar Omidinia
- Genetics & Metabolism Research Centre, Pasteur Institute of Iran, Tehran, Iran.
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109
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Bonet J, Harteveld Z, Sesterhenn F, Scheck A, Correia BE. rstoolbox - a Python library for large-scale analysis of computational protein design data and structural bioinformatics. BMC Bioinformatics 2019; 20:240. [PMID: 31092198 PMCID: PMC6521408 DOI: 10.1186/s12859-019-2796-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 04/08/2019] [Indexed: 01/13/2023] Open
Abstract
Background Large-scale datasets of protein structures and sequences are becoming ubiquitous in many domains of biological research. Experimental approaches and computational modelling methods are generating biological data at an unprecedented rate. The detailed analysis of structure-sequence relationships is critical to unveil governing principles of protein folding, stability and function. Computational protein design (CPD) has emerged as an important structure-based approach to engineer proteins for novel functions. Generally, CPD workflows rely on the generation of large numbers of structural models to search for the optimal structure-sequence configurations. As such, an important step of the CPD process is the selection of a small subset of sequences to be experimentally characterized. Given the limitations of current CPD scoring functions, multi-step design protocols and elaborated analysis of the decoy populations have become essential for the selection of sequences for experimental characterization and the success of CPD strategies. Results Here, we present the rstoolbox, a Python library for the analysis of large-scale structural data tailored for CPD applications. rstoolbox is oriented towards both CPD software users and developers, being easily integrated in analysis workflows. For users, it offers the ability to profile and select decoy sets, which may guide multi-step design protocols or for follow-up experimental characterization. rstoolbox provides intuitive solutions for the visualization of large sequence/structure datasets (e.g. logo plots and heatmaps) and facilitates the analysis of experimental data obtained through traditional biochemical techniques (e.g. circular dichroism and surface plasmon resonance) and high-throughput sequencing. For CPD software developers, it provides a framework to easily benchmark and compare different CPD approaches. Here, we showcase the rstoolbox in both types of applications. Conclusions rstoolbox is a library for the evaluation of protein structures datasets tailored for CPD data. It provides interactive access through seamless integration with IPython, while still being suitable for high-performance computing. In addition to its functionalities for data analysis and graphical representation, the inclusion of rstoolbox in protein design pipelines will allow to easily standardize the selection of design candidates, as well as, to improve the overall reproducibility and robustness of CPD selection processes.
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Affiliation(s)
- Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), CH-1015, Lausanne, Switzerland
| | - Zander Harteveld
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), CH-1015, Lausanne, Switzerland
| | - Fabian Sesterhenn
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), CH-1015, Lausanne, Switzerland
| | - Andreas Scheck
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), CH-1015, Lausanne, Switzerland
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland. .,Swiss Institute of Bioinformatics (SIB), CH-1015, Lausanne, Switzerland.
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110
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Methods for the Refinement of Protein Structure 3D Models. Int J Mol Sci 2019; 20:ijms20092301. [PMID: 31075942 PMCID: PMC6539982 DOI: 10.3390/ijms20092301] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 04/24/2019] [Accepted: 05/07/2019] [Indexed: 12/25/2022] Open
Abstract
The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.
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111
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Lapin J, Nevzorov AA. Validation of protein backbone structures calculated from NMR angular restraints using Rosetta. JOURNAL OF BIOMOLECULAR NMR 2019; 73:229-244. [PMID: 31076969 DOI: 10.1007/s10858-019-00251-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Accepted: 05/02/2019] [Indexed: 06/09/2023]
Abstract
Multidimensional solid-state NMR spectra of oriented membrane proteins can be used to infer the backbone torsion angles and hence the overall protein fold by measuring dipolar couplings and chemical shift anisotropies, which depend on the orientation of each peptide plane with respect to the external magnetic field. However, multiple peptide plane orientations can be consistent with a given set of angular restraints. This ambiguity is further exacerbated by experimental uncertainty in obtaining and interpreting such restraints. The previously developed algorithms for structure calculations using angular restraints typically involve a sequential walkthrough along the backbone to find the torsion angles between the consecutive peptide plane orientations that are consistent with the experimental data. This method is sensitive to experimental uncertainty in interpreting the peak positions of as low as ± 10 Hz, often yielding high structural RMSDs for the calculated structures. Here we present a significantly improved version of the algorithm which includes the fitting of several peptide planes at once in order to prevent propagation of error along the backbone. In addition, a protocol has been devised for filtering the structural solutions using Rosetta scoring functions in order to find the structures that both fit the spectrum and satisfy bioinformatics restraints. The robustness of the new algorithm has been tested using synthetic angular restraints generated from the known structures for two proteins: a soluble protein 2gb1 (56 residues), chosen for its diverse secondary structure elements, i.e. an alpha-helix and two beta-sheets, and a membrane protein 4a2n, from which the first two transmembrane helices (having a total of 64 residues) have been used. Extensive simulations have been performed by varying the number of fitted planes, experimental error, and the number of NMR dimensions. It has been found that simultaneously fitting two peptide planes always shifted the distribution of the calculated structures toward lower structural RMSD values as compared to fitting a single torsion-angle pair. For each protein, irrespective of the simulation parameters, Rosetta was able to distinguish the most plausible structures, often having structural RMSDs lower than 2 Å with respect to the original structure. This study establishes a framework for de-novo protein structure prediction using a combination of solid-state NMR angular restraints and bioinformatics.
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Affiliation(s)
- Joel Lapin
- Department of Chemistry, North Carolina State University, 2620 Yarbrough Drive, Raleigh, NC, 27695-8204, USA
| | - Alexander A Nevzorov
- Department of Chemistry, North Carolina State University, 2620 Yarbrough Drive, Raleigh, NC, 27695-8204, USA.
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112
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Networks of electrostatic and hydrophobic interactions modulate the complex folding free energy surface of a designed βα protein. Proc Natl Acad Sci U S A 2019; 116:6806-6811. [PMID: 30877249 DOI: 10.1073/pnas.1818744116] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The successful de novo design of proteins can provide insights into the physical chemical basis of stability, the role of evolution in constraining amino acid sequences, and the production of customizable platforms for engineering applications. Previous guanidine hydrochloride (GdnHCl; an ionic denaturant) experiments of a designed, naturally occurring βα fold, Di-III_14, revealed a cooperative, two-state unfolding transition and a modest stability. Continuous-flow mixing experiments in our laboratory revealed a simple two-state reaction in the microsecond to millisecond time range and consistent with the thermodynamic results. In striking contrast, the protein remains folded up to 9.25 M in urea, a neutral denaturant, and hydrogen exchange (HDX) NMR analysis in water revealed the presence of numerous high-energy states that interconvert on a time scale greater than seconds. The complex protection pattern for HDX corresponds closely with a pair of electrostatic networks on the surface and an extensive network of hydrophobic side chains in the interior of the protein. Mutational analysis showed that electrostatic and hydrophobic networks contribute to the resistance to urea denaturation for the WT protein; remarkably, single charge reversals on the protein surface restore the expected urea sensitivity. The roughness of the energy surface reflects the densely packed hydrophobic core; the removal of only two methyl groups eliminates the high-energy states and creates a smooth surface. The design of a very stable βα fold containing electrostatic and hydrophobic networks has created a complex energy surface rarely observed in natural proteins.
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113
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The CDR1 and Other Regions of Immunoglobulin Light Chains are Hot Spots for Amyloid Aggregation. Sci Rep 2019; 9:3123. [PMID: 30816248 PMCID: PMC6395779 DOI: 10.1038/s41598-019-39781-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 01/17/2019] [Indexed: 12/14/2022] Open
Abstract
Immunoglobulin light chain-derived (AL) amyloidosis is a debilitating disease without known cure. Almost nothing is known about the structural factors driving the amyloidogenesis of the light chains. This study aimed to identify the fibrillogenic hotspots of the model protein 6aJL2 and in pursuing this goal, two complementary approaches were applied. One of them was based on several web-based computational tools optimized to predict fibrillogenic/aggregation-prone sequences based on different structural and biophysical properties of the polypeptide chain. Then, the predictions were confirmed with an ad-hoc synthetic peptide library. In the second approach, 6aJL2 protein was proteolyzed with trypsin, and the products incubated in aggregation-promoting conditions. Then, the aggregation-prone fragments were identified by combining standard proteomic methods, and the results validated with a set of synthetic peptides with the sequence of the tryptic fragments. Both strategies coincided to identify a fibrillogenic hotspot located at the CDR1 and β-strand C of the protein, which was confirmed by scanning proline mutagenesis analysis. However, only the proteolysis-based strategy revealed additional fibrillogenic hotspots in two other regions of the protein. It was shown that a fibrillogenic hotspot associated to the CDR1 is also encoded by several κ and λ germline variable domain gene segments. Some parts of this study have been included in the chapter “The Structural Determinants of the Immunoglobulin Light Chain Amyloid Aggregation”, published in Physical Biology of Proteins and Peptides, Springer 2015 (ISBN 978-3-319-21687-4).
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Sesterhenn F, Galloux M, Vollers SS, Csepregi L, Yang C, Descamps D, Bonet J, Friedensohn S, Gainza P, Corthésy P, Chen M, Rosset S, Rameix-Welti MA, Éléouët JF, Reddy ST, Graham BS, Riffault S, Correia BE. Boosting subdominant neutralizing antibody responses with a computationally designed epitope-focused immunogen. PLoS Biol 2019; 17:e3000164. [PMID: 30789898 PMCID: PMC6400402 DOI: 10.1371/journal.pbio.3000164] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 03/05/2019] [Accepted: 02/08/2019] [Indexed: 12/20/2022] Open
Abstract
Throughout the last several decades, vaccination has been key to prevent and eradicate infectious diseases. However, many pathogens (e.g., respiratory syncytial virus [RSV], influenza, dengue, and others) have resisted vaccine development efforts, largely because of the failure to induce potent antibody responses targeting conserved epitopes. Deep profiling of human B cells often reveals potent neutralizing antibodies that emerge from natural infection, but these specificities are generally subdominant (i.e., are present in low titers). A major challenge for next-generation vaccines is to overcome established immunodominance hierarchies and focus antibody responses on crucial neutralization epitopes. Here, we show that a computationally designed epitope-focused immunogen presenting a single RSV neutralization epitope elicits superior epitope-specific responses compared to the viral fusion protein. In addition, the epitope-focused immunogen efficiently boosts antibodies targeting the palivizumab epitope, resulting in enhanced neutralization. Overall, we show that epitope-focused immunogens can boost subdominant neutralizing antibody responses in vivo and reshape established antibody hierarchies. A computationally designed epitope-focused immunogen presenting a single neutralization epitope from Respiratory Syncytial Virus elicits superior epitope-specific responses compared to the viral fusion protein. Furthermore, epitope-focused immunogens can reshape established antibody hierarchies. Vaccines are one of the most valuable instruments to prevent and control infectious diseases. Their primary correlate of protection is the level of induction of neutralizing antibodies that target critical antigenic sites and thereby block infection. Natural infections with pathogens such as the respiratory syncytial virus (RSV) or influenza induce a broad repertoire of antibodies that target multiple epitopes. Among those, functional antibodies with key specificities are often subdominant (present in low titers). Thus, a central goal for vaccine development is to focus antibody responses on such neutralization epitopes. Here, we show that a computationally designed, epitope-focused immunogen mimicking an important RSV neutralization epitope (site II) can focus antibodies onto this well-defined epitope. In a scenario of preexisting immunity, in which site II–specific antibodies were subdominant, the epitope-focused immunogen selectively boosted site II–specific antibodies, resulting in an increased viral neutralization through this epitope. We propose that rationally designed immunogens spotlighting defined epitopes have a unique potential to focus antibody responses on functionally conserved sites in cases of preexisting immunity. Our results have broad implications for vaccine design as a strategy to steer preexisting antibody responses away from immunodominant, variable epitopes and toward subdominant epitopes that confer broad and potent neutralization.
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MESH Headings
- Animals
- Antibodies, Monoclonal, Humanized/chemistry
- Antibodies, Monoclonal, Humanized/immunology
- Antibodies, Neutralizing/biosynthesis
- Antibodies, Neutralizing/genetics
- Antibodies, Viral/biosynthesis
- Antibodies, Viral/genetics
- Cloning, Molecular
- Computer-Aided Design
- Epitopes/chemistry
- Epitopes/immunology
- Escherichia coli/genetics
- Escherichia coli/metabolism
- Female
- Gene Expression
- Genetic Vectors/chemistry
- Genetic Vectors/metabolism
- Immunization/methods
- Immunogenicity, Vaccine
- Mice
- Mice, Inbred BALB C
- Nanoparticles/administration & dosage
- Nanoparticles/chemistry
- Palivizumab/chemistry
- Palivizumab/immunology
- Receptors, Antigen, B-Cell/chemistry
- Receptors, Antigen, B-Cell/genetics
- Receptors, Antigen, B-Cell/immunology
- Recombinant Fusion Proteins/administration & dosage
- Recombinant Fusion Proteins/chemistry
- Recombinant Fusion Proteins/genetics
- Recombinant Fusion Proteins/immunology
- Respiratory Syncytial Virus Vaccines/administration & dosage
- Respiratory Syncytial Virus Vaccines/biosynthesis
- Respiratory Syncytial Virus Vaccines/genetics
- Respiratory Syncytial Viruses/immunology
- Structural Homology, Protein
- Viral Fusion Proteins/administration & dosage
- Viral Fusion Proteins/chemistry
- Viral Fusion Proteins/genetics
- Viral Fusion Proteins/immunology
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Affiliation(s)
- Fabian Sesterhenn
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Marie Galloux
- Unité de Virologie et Immunologie Moléculaires (UR892), INRA, Université Paris-Saclay, Jouy-en-Josas, France
| | - Sabrina S. Vollers
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Lucia Csepregi
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Che Yang
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Delphyne Descamps
- Unité de Virologie et Immunologie Moléculaires (UR892), INRA, Université Paris-Saclay, Jouy-en-Josas, France
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Simon Friedensohn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Pablo Gainza
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Patricia Corthésy
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Man Chen
- Viral Pathogenesis Laboratory, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Stéphane Rosset
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Marie-Anne Rameix-Welti
- UMR1173, INSERM, Université de Versailles St. Quentin, Montigny le Bretonneux, France
- AP-HP, Laboratoire de Microbiologie, Hôpital Ambroise Paré, Boulogne-Billancourt, France
| | - Jean-François Éléouët
- Unité de Virologie et Immunologie Moléculaires (UR892), INRA, Université Paris-Saclay, Jouy-en-Josas, France
| | - Sai T. Reddy
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Barney S. Graham
- Viral Pathogenesis Laboratory, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Sabine Riffault
- Unité de Virologie et Immunologie Moléculaires (UR892), INRA, Université Paris-Saclay, Jouy-en-Josas, France
| | - Bruno E. Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- * E-mail:
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115
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Jäger VD, Kloss R, Grünberger A, Seide S, Hahn D, Karmainski T, Piqueray M, Embruch J, Longerich S, Mackfeld U, Jaeger KE, Wiechert W, Pohl M, Krauss U. Tailoring the properties of (catalytically)-active inclusion bodies. Microb Cell Fact 2019; 18:33. [PMID: 30732596 PMCID: PMC6367779 DOI: 10.1186/s12934-019-1081-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 01/30/2019] [Indexed: 01/02/2023] Open
Abstract
Background Immobilization is an appropriate tool to ease the handling and recycling of enzymes in biocatalytic processes and to increase their stability. Most of the established immobilization methods require case-to-case optimization, which is laborious and time-consuming. Often, (chromatographic) enzyme purification is required and stable immobilization usually includes additional cross-linking or adsorption steps. We have previously shown in a few case studies that the molecular biological fusion of an aggregation-inducing tag to a target protein induces the intracellular formation of protein aggregates, so called inclusion bodies (IBs), which to a certain degree retain their (catalytic) function. This enables the combination of protein production and immobilization in one step. Hence, those biologically-produced immobilizates were named catalytically-active inclusion bodies (CatIBs) or, in case of proteins without catalytic activity, functional IBs (FIBs). While this strategy has been proven successful, the efficiency, the potential for optimization and important CatIB/FIB properties like yield, activity and morphology have not been investigated systematically. Results We here evaluated a CatIB/FIB toolbox of different enzymes and proteins. Different optimization strategies, like linker deletion, C- versus N-terminal fusion and the fusion of alternative aggregation-inducing tags were evaluated. The obtained CatIBs/FIBs varied with respect to formation efficiency, yield, composition and residual activity, which could be correlated to differences in their morphology; as revealed by (electron) microscopy. Last but not least, we demonstrate that the CatIB/FIB formation efficiency appears to be correlated to the solvent-accessible hydrophobic surface area of the target protein, providing a structure-based rationale for our strategy and opening up the possibility to predict its efficiency for any given target protein. Conclusion We here provide evidence for the general applicability, predictability and flexibility of the CatIB/FIB immobilization strategy, highlighting the application potential of CatIB-based enzyme immobilizates for synthetic chemistry, biocatalysis and industry. Electronic supplementary material The online version of this article (10.1186/s12934-019-1081-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- V D Jäger
- Institut für Molekulare Enzymtechnologie, Heinrich-Heine-Universität Düsseldorf, Forschungszentrum Jülich, 52425, Jülich, Germany.,Bioeconomy Science Center (BioSC), c/o, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - R Kloss
- IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.,Bioeconomy Science Center (BioSC), c/o, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - A Grünberger
- IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.,Multiscale Bioengineering, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
| | - S Seide
- IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - D Hahn
- IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - T Karmainski
- IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - M Piqueray
- Institut für Molekulare Enzymtechnologie, Heinrich-Heine-Universität Düsseldorf, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - J Embruch
- IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - S Longerich
- Institut für Molekulare Enzymtechnologie, Heinrich-Heine-Universität Düsseldorf, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - U Mackfeld
- IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - K-E Jaeger
- Institut für Molekulare Enzymtechnologie, Heinrich-Heine-Universität Düsseldorf, Forschungszentrum Jülich, 52425, Jülich, Germany.,IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.,Bioeconomy Science Center (BioSC), c/o, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - W Wiechert
- IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.,Bioeconomy Science Center (BioSC), c/o, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - M Pohl
- IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.,Bioeconomy Science Center (BioSC), c/o, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - U Krauss
- Institut für Molekulare Enzymtechnologie, Heinrich-Heine-Universität Düsseldorf, Forschungszentrum Jülich, 52425, Jülich, Germany. .,Bioeconomy Science Center (BioSC), c/o, Forschungszentrum Jülich, 52425, Jülich, Germany.
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116
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Chan HCS, Pan L, Li Y, Yuan S. Rationalization of stereoselectivity in enzyme reactions. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2018. [DOI: 10.1002/wcms.1403] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- H. C. Stephen Chan
- Faculty of Chemistry, Biological and Chemical Research Centre University of Warsaw Warszawa Poland
- Faculty of Life Sciences University of Bradford Bradford UK
| | - Lu Pan
- Key Laboratory of Synthetic Chemistry of Natural Substances, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences Shanghai China
| | - Yi Li
- Department of Neurology University of Southern California Los Angeles California
| | - Shuguang Yuan
- Faculty of Chemistry, Biological and Chemical Research Centre University of Warsaw Warszawa Poland
- Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne Lausanne Switzerland
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117
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Computational affinity maturation of camelid single-domain intrabodies against the nonamyloid component of alpha-synuclein. Sci Rep 2018; 8:17611. [PMID: 30514850 PMCID: PMC6279780 DOI: 10.1038/s41598-018-35464-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 10/26/2018] [Indexed: 12/21/2022] Open
Abstract
Improving the affinity of protein-protein interactions is a challenging problem that is particularly important in the development of antibodies for diagnostic and clinical use. Here, we used structure-based computational methods to optimize the binding affinity of VHNAC1, a single-domain intracellular antibody (intrabody) from the camelid family that was selected for its specific binding to the nonamyloid component (NAC) of human α-synuclein (α-syn), a natively disordered protein, implicated in the pathogenesis of Parkinson's disease (PD) and related neurological disorders. Specifically, we performed ab initio modeling that revealed several possible modes of VHNAC1 binding to the NAC region of α-syn as well as mutations that potentially enhance the affinity between these interacting proteins. While our initial design strategy did not lead to improved affinity, it ultimately guided us towards a model that aligned more closely with experimental observations, revealing a key residue on the paratope and the participation of H4 loop residues in binding, as well as confirming the importance of electrostatic interactions. The binding activity of the best intrabody mutant, which involved just a single amino acid mutation compared to parental VHNAC1, was significantly enhanced primarily through a large increase in association rate. Our results indicate that structure-based computational design can be used to successfully improve the affinity of antibodies against natively disordered and weakly immunogenic antigens such as α-syn, even in cases such as ours where crystal structures are unavailable.
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118
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Cao Q, Shin WS, Chan H, Vuong CK, Dubois B, Li B, Murray KA, Sawaya MR, Feigon J, Black DL, Eisenberg DS, Jiang L. Inhibiting amyloid-β cytotoxicity through its interaction with the cell surface receptor LilrB2 by structure-based design. Nat Chem 2018; 10:1213-1221. [PMID: 30297750 PMCID: PMC6250578 DOI: 10.1038/s41557-018-0147-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Accepted: 08/28/2018] [Indexed: 12/31/2022]
Abstract
Inhibiting the interaction between amyloid-β (Aβ) and a neuronal cell surface receptor, LilrB2, has been suggested as a potential route for treating Alzheimer's disease. Supporting this approach, Alzheimer's-like symptoms are reduced in mouse models following genetic depletion of the LilrB2 homologue. In its pathogenic, oligomeric state, Aβ binds to LilrB2, triggering a pathway to synaptic loss. Here we identify the LilrB2 binding moieties of Aβ (16KLVFFA21) and identify its binding site on LilrB2 from a crystal structure of LilrB2 immunoglobulin domains D1D2 complexed to small molecules that mimic phenylalanine residues. In this structure, we observed two pockets that can accommodate the phenylalanine side chains of KLVFFA. These pockets were confirmed to be 16KLVFFA21 binding sites by mutagenesis. Rosetta docking revealed a plausible geometry for the Aβ-LilrB2 complex and assisted with the structure-guided selection of small molecule inhibitors. These molecules inhibit Aβ-LilrB2 interactions in vitro and on the cell surface and reduce Aβ cytotoxicity, which suggests these inhibitors are potential therapeutic leads against Alzheimer's disease.
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Affiliation(s)
- Qin Cao
- Departments of Chemistry and Biochemistry and Biological Chemistry, UCLA-DOE Institute and Howard Hughes Medical Institute, UCLA, Los Angeles, CA, USA
| | - Woo Shik Shin
- Department of Neurology, Molecular Biology Institute and Brain Research Institute, UCLA, Los Angeles, CA, USA
| | - Henry Chan
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA
| | - Celine K Vuong
- Department of Microbiology, Immunology and Molecular Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Systems Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bethany Dubois
- Department of Neurology, Molecular Biology Institute and Brain Research Institute, UCLA, Los Angeles, CA, USA
- Division of Applied Mathematics, Brown University, Providence, RI, USA
| | - Binsen Li
- Department of Neurology, Molecular Biology Institute and Brain Research Institute, UCLA, Los Angeles, CA, USA
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA
| | - Kevin A Murray
- Departments of Chemistry and Biochemistry and Biological Chemistry, UCLA-DOE Institute and Howard Hughes Medical Institute, UCLA, Los Angeles, CA, USA
| | - Michael R Sawaya
- Departments of Chemistry and Biochemistry and Biological Chemistry, UCLA-DOE Institute and Howard Hughes Medical Institute, UCLA, Los Angeles, CA, USA
| | - Juli Feigon
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA
| | - Douglas L Black
- Department of Microbiology, Immunology and Molecular Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - David S Eisenberg
- Departments of Chemistry and Biochemistry and Biological Chemistry, UCLA-DOE Institute and Howard Hughes Medical Institute, UCLA, Los Angeles, CA, USA.
| | - Lin Jiang
- Department of Neurology, Molecular Biology Institute and Brain Research Institute, UCLA, Los Angeles, CA, USA.
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Simoncini D, Zhang KYJ, Schiex T, Barbe S. A structural homology approach for computational protein design with flexible backbone. Bioinformatics 2018; 35:2418-2426. [DOI: 10.1093/bioinformatics/bty975] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 11/01/2018] [Accepted: 11/28/2018] [Indexed: 01/09/2023] Open
Abstract
Abstract
Motivation
Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs.
Results
We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to guide sequence design, while sampling side-chain and backbone conformations to accommodate mutations. Shades (Structural Homology Algorithm for protein DESign), is based on customized libraries of non-contiguous in-contact amino acid residue motifs. We have tested Shades on a public benchmark of 40 proteins selected from different protein families. When excluding homologous proteins, Shades achieved a protein sequence recovery of 30% and a protein sequence similarity of 46% on average, compared with the PFAM protein family of the target protein. When homologous structures were added, the wild-type sequence recovery rate achieved 93%.
Availability and implementation
Shades source code is available at https://bitbucket.org/satsumaimo/shades as a patch for Rosetta 3.8 with a curated protein structure database and ITEM library creation software.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David Simoncini
- Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés, LISBP, Université de Toulouse, CNRS, INRA, INSA, F Toulouse cedex 04, France
- Institut de recherche en informatique de Toulouse, IRIT, UMR 5505-CNRS, Université de Toulouse, Cedex 9, France
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa, Japan
| | - Thomas Schiex
- Institut de recherche en informatique de Toulouse, UMR 5505-CNRS, Université de Toulouse, Cedex 9, France
| | - Sophie Barbe
- Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés, LISBP, Université de Toulouse, CNRS, INRA, INSA, F Toulouse cedex 04, France
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120
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Rosetta FunFolDes - A general framework for the computational design of functional proteins. PLoS Comput Biol 2018; 14:e1006623. [PMID: 30452434 PMCID: PMC6277116 DOI: 10.1371/journal.pcbi.1006623] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 12/03/2018] [Accepted: 11/06/2018] [Indexed: 01/11/2023] Open
Abstract
The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability. The low success rates of computational design protocols and the extensive in vitro optimization often required, highlight the challenge of designing proteins that perform essential biochemical functions, such as binding or catalysis. One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins. The structural complexity of the functional motif largely determines how readily one can find host protein structures that are "designable", meaning that are likely to present the functional motif in the desired conformation. One promising route to enhance the "designability" of protein structures is to allow backbone flexibility. Here, we present a computational approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins-Rosetta Functional Folding and Design (FunFolDes). We performed extensive computational benchmarks, where we observed that the enforcement of functional requirements resulted in designs distant from the global energetic minimum of the protein. An observation consistent with several experimental studies that have revealed function-stability tradeoffs. To test the design capabilities of FunFolDes we transplanted two viral epitopes into distant structural templates including one de novo "functionless" fold, which represent two typical challenges where the designability problem arises. The designed proteins were experimentally characterized showing high binding affinities to monoclonal antibodies, making them valuable candidates for vaccine design endeavors. Overall, we present an accessible strategy to repurpose old protein folds for new functions. This may lead to important improvements on the computational design of proteins, with structurally complex functional sites, that can perform elaborate biochemical functions related to binding and catalysis.
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121
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Hallen MA. PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design. Proteins 2018; 87:62-73. [PMID: 30378699 DOI: 10.1002/prot.25623] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 10/10/2018] [Accepted: 10/16/2018] [Indexed: 12/29/2022]
Abstract
Protein design algorithms must search an enormous conformational space to identify favorable conformations. As a result, those that perform this search with guarantees of accuracy generally start with a conformational pruning step, such as dead-end elimination (DEE). However, the mathematical assumptions of DEE-based pruning algorithms have up to now severely restricted the biophysical model that can feasibly be used in protein design. To lift these restrictions, I propose to prune local unrealistic geometries (PLUG) using a linear programming-based method. PLUG's biophysical model consists only of well-known lower bounds on interatomic distances. PLUG is intended as preprocessing for energy-based protein design calculations, whose biophysical model need not support DEE pruning. Based on 96 test cases, PLUG is at least as effective at pruning as DEE for larger protein designs-the type that most require pruning. When combined with the LUTE protein design algorithm, PLUG greatly facilitates designs that account for continuous entropy, large multistate designs with continuous flexibility, and designs with extensive continuous backbone flexibility and advanced nonpairwise energy functions. Many of these designs are tractable only with PLUG, either for empirical reasons (LUTE's machine learning step achieves an accurate fit only after PLUG pruning), or for theoretical reasons (many energy functions are fundamentally incompatible with DEE).
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Affiliation(s)
- Mark A Hallen
- Toyota Technological Institute at Chicago, Chicago, Illinois
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122
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Sun Z, Rokita SE. Toward a Halophenol Dehalogenase from Iodotyrosine Deiodinase via Computational Design. ACS Catal 2018. [DOI: 10.1021/acscatal.8b03587] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Zuodong Sun
- Department of Chemistry, Johns Hopkins University, 3400 N. Charles St., Baltimore, Maryland 21218, United States
| | - Steven E. Rokita
- Department of Chemistry, Johns Hopkins University, 3400 N. Charles St., Baltimore, Maryland 21218, United States
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123
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Greener JG, Moffat L, Jones DT. Design of metalloproteins and novel protein folds using variational autoencoders. Sci Rep 2018; 8:16189. [PMID: 30385875 PMCID: PMC6212568 DOI: 10.1038/s41598-018-34533-1] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 10/19/2018] [Indexed: 12/26/2022] Open
Abstract
The design of novel proteins has many applications but remains an attritional process with success in isolated cases. Meanwhile, deep learning technologies have exploded in popularity in recent years and are increasingly applicable to biology due to the rise in available data. We attempt to link protein design and deep learning by using variational autoencoders to generate protein sequences conditioned on desired properties. Potential copper and calcium binding sites are added to non-metal binding proteins without human intervention and compared to a hidden Markov model. In another use case, a grammar of protein structures is developed and used to produce sequences for a novel protein topology. One candidate structure is found to be stable by molecular dynamics simulation. The ability of our model to confine the vast search space of protein sequences and to scale easily has the potential to assist in a variety of protein design tasks.
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Affiliation(s)
- Joe G Greener
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK
- Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Lewis Moffat
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK
- Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - David T Jones
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.
- Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK.
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124
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Marcos E, Chidyausiku TM, McShan AC, Evangelidis T, Nerli S, Carter L, Nivón LG, Davis A, Oberdorfer G, Tripsianes K, Sgourakis NG, Baker D. De novo design of a non-local β-sheet protein with high stability and accuracy. Nat Struct Mol Biol 2018; 25:1028-1034. [PMID: 30374087 PMCID: PMC6219906 DOI: 10.1038/s41594-018-0141-6] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Accepted: 09/11/2018] [Indexed: 11/08/2022]
Abstract
β-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein design. Despite this potential, de novo design of all-β-sheet proteins from first principles lags far behind the design of all-α or mixed-αβ domains owing to their non-local nature and the tendency of exposed β-strand edges to aggregate. Through study of loops connecting unpaired β-strands (β-arches), we have identified a series of structural relationships between loop geometry, side chain directionality and β-strand length that arise from hydrogen bonding and packing constraints on regular β-sheet structures. We use these rules to de novo design jellyroll structures with double-stranded β-helices formed by eight antiparallel β-strands. The nuclear magnetic resonance structure of a hyperthermostable design closely matched the computational model, demonstrating accurate control over the β-sheet structure and loop geometry. Our results open the door to the design of a broad range of non-local β-sheet protein structures.
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Affiliation(s)
- Enrique Marcos
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona, Spain.
| | - Tamuka M Chidyausiku
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Andrew C McShan
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Thomas Evangelidis
- CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Santrupti Nerli
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, Santa Cruz, CA, USA
- Department of Computer Science, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Lauren Carter
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lucas G Nivón
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Audrey Davis
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Amazon, Seattle, WA, USA
| | - Gustav Oberdorfer
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Institute of Biochemistry, Graz University of Technology, Graz, Austria
| | | | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
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125
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Chen X, Sun X, Yang W, Yang B, Zhao X, Chen S, He L, Chen H, Yang C, Xiao L, Chang Z, Guo J, He J, Zhang F, Zheng F, Hu Z, Yang Z, Lou J, Zheng W, Qi H, Xu C, Zhang H, Shan H, Zhou XJ, Wang Q, Shi Y, Lai L, Li Z, Liu W. An autoimmune disease variant of IgG1 modulates B cell activation and differentiation. Science 2018; 362:700-705. [PMID: 30287618 DOI: 10.1126/science.aap9310] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 04/10/2018] [Accepted: 09/19/2018] [Indexed: 12/11/2022]
Abstract
The maintenance of autoreactive B cells in a quiescent state is crucial for preventing autoimmunity. Here we identify a variant of human immunoglobulin G1 (IgG1) with a Gly396→Arg substitution (hIgG1-G396R), which positively correlates with systemic lupus erythematosus. In induced lupus models, murine homolog Gly390→Arg (G390R) knockin mice generate excessive numbers of plasma cells, leading to a burst of broad-spectrum autoantibodies. This enhanced production of antibodies is also observed in hapten-immunized G390R mice, as well as in influenza-vaccinated human G396R homozygous carriers. This variant potentiates the phosphorylation of the IgG1 immunoglobulin tail tyrosine (ITT) motif. This, in turn, alters the availability of phospho-ITT to trigger longer adaptor protein Grb2 dwell times in immunological synapses, leading to hyper-Grb2-Bruton's tyrosine kinase (Btk) signaling upon antigen binding. Thus, the hIgG1-G396R variant is important for both lupus pathogenesis and antibody responses after vaccination.
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Affiliation(s)
- Xiangjun Chen
- Ministry of Education Key Laboratory of Protein Sciences, Center for Life Sciences, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Institute for Immunology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xiaolin Sun
- Department of Rheumatology and Immunology, Peking University People's Hospital, Beijing Key Laboratory for Rheumatism and Immune Diagnosis (BZ0135), Peking-Tsinghua Center for Life Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing 100044, China
| | - Wei Yang
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Bing Yang
- Ministry of Education Key Laboratory of Protein Sciences, Center for Life Sciences, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Institute for Immunology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xiaozhen Zhao
- Department of Rheumatology and Immunology, Peking University People's Hospital, Beijing Key Laboratory for Rheumatism and Immune Diagnosis (BZ0135), Peking-Tsinghua Center for Life Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing 100044, China
| | - Shuting Chen
- Ministry of Education Key Laboratory of Protein Sciences, Center for Life Sciences, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Institute for Immunology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Lili He
- Ministry of Education Key Laboratory of Protein Sciences, Center for Life Sciences, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Institute for Immunology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Hui Chen
- Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Changmei Yang
- Ministry of Education Key Laboratory of Protein Sciences, Center for Life Sciences, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Institute for Immunology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Le Xiao
- Ministry of Education Key Laboratory of Protein Sciences, Center for Life Sciences, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Institute for Immunology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Zai Chang
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Jianping Guo
- Department of Rheumatology and Immunology, Peking University People's Hospital, Beijing Key Laboratory for Rheumatism and Immune Diagnosis (BZ0135), Peking-Tsinghua Center for Life Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing 100044, China
| | - Jing He
- Department of Rheumatology and Immunology, Peking University People's Hospital, Beijing Key Laboratory for Rheumatism and Immune Diagnosis (BZ0135), Peking-Tsinghua Center for Life Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing 100044, China
| | - Fuping Zhang
- Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Fang Zheng
- Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Zhiyong Yang
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jizhong Lou
- Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Wenjie Zheng
- Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Hai Qi
- Tsinghua-Peking Center for Life Sciences, Laboratory of Dynamic Immunobiology, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Chenqi Xu
- State Key Laboratory of Molecular Biology, Shanghai Science Research Center, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hong Zhang
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
| | - Hongying Shan
- Department of Rheumatism and Immunology, Peking University Shenzhen Hospital, Shenzhen 518036, China
| | - Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
| | - Qingwen Wang
- Department of Rheumatism and Immunology, Peking University Shenzhen Hospital, Shenzhen 518036, China
| | - Yi Shi
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100101, China.,Research Network of Immunity and Health (RNIH), Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Luhua Lai
- BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, and Peking-Tsinghua Center for Life Sciences at College of Chemistry and Molecular Engineering, Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Zhanguo Li
- Department of Rheumatology and Immunology, Peking University People's Hospital, Beijing Key Laboratory for Rheumatism and Immune Diagnosis (BZ0135), Peking-Tsinghua Center for Life Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing 100044, China.
| | - Wanli Liu
- Ministry of Education Key Laboratory of Protein Sciences, Center for Life Sciences, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Institute for Immunology, School of Life Sciences, Tsinghua University, Beijing 100084, China. .,Beijing Key Lab for Immunological Research on Chronic Diseases, Beijing 100084, China
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126
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Dułak D, Gadzała M, Banach M, Ptak M, Wiśniowski Z, Konieczny L, Roterman I. Filamentous Aggregates of Tau Proteins Fulfil Standard Amyloid Criteria Provided by the Fuzzy Oil Drop (FOD) Model. Int J Mol Sci 2018; 19:E2910. [PMID: 30257460 PMCID: PMC6213535 DOI: 10.3390/ijms19102910] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2018] [Revised: 09/12/2018] [Accepted: 09/20/2018] [Indexed: 01/02/2023] Open
Abstract
Abnormal filamentous aggregates that are formed by tangled tau protein turn out to be classic amyloid fibrils, meeting all the criteria defined under the fuzzy oil drop model in the context of amyloid characterization. The model recognizes amyloids as linear structures where local hydrophobicity minima and maxima propagate in an alternating manner along the fibril's long axis. This distribution of hydrophobicity differs greatly from the classic monocentric hydrophobic core observed in globular proteins. Rather than becoming a globule, the amyloid instead forms a ribbonlike (or cylindrical) structure.
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Affiliation(s)
- Dawid Dułak
- ABB Business Services Sp. z o.o. ul. Żegańska 1, 04-713 Warszawa, Poland.
| | | | - Mateusz Banach
- Department of Bioinformatics and Telemedicine, Medical College, Jagiellonian University, Łazarza 16, 31-530 Kraków, Poland.
| | - Magdalena Ptak
- Department of Bioinformatics and Telemedicine, Medical College, Jagiellonian University, Łazarza 16, 31-530 Kraków, Poland.
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków, Poland.
| | - Zdzisław Wiśniowski
- Department of Bioinformatics and Telemedicine, Medical College, Jagiellonian University, Łazarza 16, 31-530 Kraków, Poland.
| | - Leszek Konieczny
- Chair of Medical Biochemistry, Medical College, Jagiellonian University, Kopernika 7, 31-034 Kraków, Poland.
| | - Irena Roterman
- Department of Bioinformatics and Telemedicine, Medical College, Jagiellonian University, Łazarza 16, 31-530 Kraków, Poland.
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127
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Dauzhenka T, Kundrotas PJ, Vakser IA. Computational Feasibility of an Exhaustive Search of Side-Chain Conformations in Protein-Protein Docking. J Comput Chem 2018; 39:2012-2021. [PMID: 30226647 DOI: 10.1002/jcc.25381] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 03/24/2018] [Accepted: 05/26/2018] [Indexed: 11/07/2022]
Abstract
Protein-protein docking procedures typically perform the global scan of the proteins relative positions, followed by the local refinement of the putative matches. Because of the size of the search space, the global scan is usually implemented as rigid-body search, using computationally inexpensive intermolecular energy approximations. An adequate refinement has to take into account structural flexibility. Since the refinement performs conformational search of the interacting proteins, it is extremely computationally challenging, given the enormous amount of the internal degrees of freedom. Different approaches limit the search space by restricting the search to the side chains, rotameric states, coarse-grained structure representation, principal normal modes, and so on. Still, even with the approximations, the refinement presents an extreme computational challenge due to the very large number of the remaining degrees of freedom. Given the complexity of the search space, the advantage of the exhaustive search is obvious. The obstacle to such search is computational feasibility. However, the growing computational power of modern computers, especially due to the increasing utilization of Graphics Processing Unit (GPU) with large amount of specialized computing cores, extends the ranges of applicability of the brute-force search methods. This proof-of-concept study demonstrates computational feasibility of an exhaustive search of side-chain conformations in protein pocking. The procedure, implemented on the GPU architecture, was used to generate the optimal conformations in a large representative set of protein-protein complexes. © 2018 Wiley Periodicals, Inc.
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Affiliation(s)
- Taras Dauzhenka
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Petras J Kundrotas
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Ilya A Vakser
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047.,Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047
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128
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129
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Anishchenko I, Kundrotas PJ, Vakser IA. Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. Biophys J 2018; 115:809-821. [PMID: 30122295 DOI: 10.1016/j.bpj.2018.07.035] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 07/16/2018] [Accepted: 07/31/2018] [Indexed: 12/18/2022] Open
Abstract
The energy function is the key component of protein modeling methodology. This work presents a semianalytical approach to the development of contact potentials for protein structure modeling. Residue-residue and atom-atom contact energies were derived by maximizing the probability of observing native sequences in a nonredundant set of protein structures. The optimization task was formulated as an inverse statistical mechanics problem applied to the Potts model. Its solution by pseudolikelihood maximization provides consistent estimates of coupling constants at atomic and residue levels. The best performance was achieved when interacting atoms were grouped according to their physicochemical properties. For individual protein structures, the performance of the contact potentials in distinguishing near-native structures from the decoys is similar to the top-performing scoring functions. The potentials also yielded significant improvement in the protein docking success rates. The potentials recapitulated experimentally determined protein stability changes upon point mutations and protein-protein binding affinities. The approach offers a different perspective on knowledge-based potentials and may serve as the basis for their further development.
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Affiliation(s)
- Ivan Anishchenko
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas
| | - Petras J Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas.
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas.
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130
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Designed peptides that assemble into cross-α amyloid-like structures. Nat Chem Biol 2018; 14:870-875. [PMID: 30061717 DOI: 10.1038/s41589-018-0105-5] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 06/19/2018] [Indexed: 11/08/2022]
Abstract
Amyloids adopt 'cross-β' structures composed of long, twisted fibrils with β-strands running perpendicular to the fibril axis. Recently, a toxic peptide was proposed to form amyloid-like cross-α structures in solution, with a planar bilayer-like assembly observed in the crystal structure. Here we crystallographically characterize designed peptides that assemble into spiraling cross-α amyloid-like structures, which resemble twisted β-amyloid fibrils. The peptides form helical dimers, stabilized by packing of small and apolar residues, and the dimers further assemble into cross-α amyloid-like fibrils with superhelical pitches ranging from 170 Å to 200 Å. When a small residue that appeared critical for packing was converted to leucine, it resulted in structural rearrangement to a helical polymer. Fluorescently tagged versions of the designed peptides form puncta in mammalian cells, which recover from photobleaching with markedly different kinetics. These structural folds could be potentially useful for directing in vivo protein assemblies with predetermined spacing and stabilities.
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131
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Marcos E, Silva D. Essentials of
de novo
protein design: Methods and applications. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2018. [DOI: 10.1002/wcms.1374] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Enrique Marcos
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Daniel‐Adriano Silva
- Department of BiochemistryUniversity of WashingtonSeattleWashington
- Institute for Protein DesignUniversity of WashingtonSeattleWashington
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132
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Correa L, Borguesan B, Farfan C, Inostroza-Ponta M, Dorn M. A Memetic Algorithm for 3-D Protein Structure Prediction Problem. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:690-704. [PMID: 27925594 DOI: 10.1109/tcbb.2016.2635143] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Memetic Algorithms are population-based metaheuristics intrinsically concerned with exploiting all available knowledge about the problem under study. The incorporation of problem domain knowledge is not an optional mechanism, but a fundamental feature of the Memetic Algorithms. In this paper, we present a Memetic Algorithm to tackle the three-dimensional protein structure prediction problem. The method uses a structured population and incorporates a Simulated Annealing algorithm as a local search strategy, as well as ad-hoc crossover and mutation operators to deal with the problem. It takes advantage of structural knowledge stored in the Protein Data Bank, by using an Angle Probability List that helps to reduce the search space and to guide the search strategy. The proposed algorithm was tested on nineteen protein sequences of amino acid residues, and the results show the ability of the algorithm to find native-like protein structures. Experimental results have revealed that the proposed algorithm can find good solutions regarding root-mean-square deviation and global distance total score test in comparison with the experimental protein structures. We also show that our results are comparable in terms of folding organization with state-of-the-art prediction methods, corroborating the effectiveness of our proposal.
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133
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Combs SA, Mueller BK, Meiler J. Holistic Approach to Partial Covalent Interactions in Protein Structure Prediction and Design with Rosetta. J Chem Inf Model 2018; 58:1021-1036. [PMID: 29641200 DOI: 10.1021/acs.jcim.7b00398] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Partial covalent interactions (PCIs) in proteins, which include hydrogen bonds, salt bridges, cation-π, and π-π interactions, contribute to thermodynamic stability and facilitate interactions with other biomolecules. Several score functions have been developed within the Rosetta protein modeling framework that identify and evaluate these PCIs through analyzing the geometry between participating atoms. However, we hypothesize that PCIs can be unified through a simplified electron orbital representation. To test this hypothesis, we have introduced orbital based chemical descriptors for PCIs into Rosetta, called the PCI score function. Optimal geometries for the PCIs are derived from a statistical analysis of high-quality protein structures obtained from the Protein Data Bank (PDB), and the relative orientation of electron deficient hydrogen atoms and electron-rich lone pair or π orbitals are evaluated. We demonstrate that nativelike geometries of hydrogen bonds, salt bridges, cation-π, and π-π interactions are recapitulated during minimization of protein conformation. The packing density of tested protein structures increased from the standard score function from 0.62 to 0.64, closer to the native value of 0.70. Overall, rotamer recovery improved when using the PCI score function (75%) as compared to the standard Rosetta score function (74%). The PCI score function represents an improvement over the standard Rosetta score function for protein model scoring; in addition, it provides a platform for future directions in the analysis of small molecule to protein interactions, which depend on partial covalent interactions.
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Affiliation(s)
- Steven A Combs
- Department of Chemistry , Vanderbilt University , 7330 Stevenson Center, Station B 351822 , Nashville , Tennessee 37235 , United States
| | - Benjamin K Mueller
- Department of Chemistry , Vanderbilt University , 7330 Stevenson Center, Station B 351822 , Nashville , Tennessee 37235 , United States
| | - Jens Meiler
- Department of Chemistry , Vanderbilt University , 7330 Stevenson Center, Station B 351822 , Nashville , Tennessee 37235 , United States
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134
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Hallen MA, Donald BR. CATS (Coordinates of Atoms by Taylor Series): protein design with backbone flexibility in all locally feasible directions. Bioinformatics 2018; 33:i5-i12. [PMID: 28882005 PMCID: PMC5870559 DOI: 10.1093/bioinformatics/btx277] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Motivation When proteins mutate or bind to ligands, their backbones often move significantly, especially in loop regions. Computational protein design algorithms must model these motions in order to accurately optimize protein stability and binding affinity. However, methods for backbone conformational search in design have been much more limited than for sidechain conformational search. This is especially true for combinatorial protein design algorithms, which aim to search a large sequence space efficiently and thus cannot rely on temporal simulation of each candidate sequence. Results We alleviate this difficulty with a new parameterization of backbone conformational space, which represents all degrees of freedom of a specified segment of protein chain that maintain valid bonding geometry (by maintaining the original bond lengths and angles and ω dihedrals). In order to search this space, we present an efficient algorithm, CATS, for computing atomic coordinates as a function of our new continuous backbone internal coordinates. CATS generalizes the iMinDEE and EPIC protein design algorithms, which model continuous flexibility in sidechain dihedrals, to model continuous, appropriately localized flexibility in the backbone dihedrals ϕ and ψ as well. We show using 81 test cases based on 29 different protein structures that CATS finds sequences and conformations that are significantly lower in energy than methods with less or no backbone flexibility do. In particular, we show that CATS can model the viability of an antibody mutation known experimentally to increase affinity, but that appears sterically infeasible when modeled with less or no backbone flexibility. Availability and implementation Our code is available as free software at https://github.com/donaldlab/OSPREY_refactor. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mark A Hallen
- Department of Computer Science, Duke University, Durham, NC, USA.,Toyota Technological Institute at Chicago, Chicago, IL, USA
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC, USA.,Department of Chemistry, Duke University, Durham, NC, USA.,Department of Biochemistry, Duke University Medical Center, Durham, NC, USA
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135
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Ojewole AA, Jou JD, Fowler VG, Donald BR. BBK* (Branch and Bound Over K*): A Provable and Efficient Ensemble-Based Protein Design Algorithm to Optimize Stability and Binding Affinity Over Large Sequence Spaces. J Comput Biol 2018; 25:726-739. [PMID: 29641249 DOI: 10.1089/cmb.2017.0267] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Computational protein design (CPD) algorithms that compute binding affinity, Ka, search for sequences with an energetically favorable free energy of binding. Recent work shows that three principles improve the biological accuracy of CPD: ensemble-based design, continuous flexibility of backbone and side-chain conformations, and provable guarantees of accuracy with respect to the input. However, previous methods that use all three design principles are single-sequence (SS) algorithms, which are very costly: linear in the number of sequences and thus exponential in the number of simultaneously mutable residues. To address this computational challenge, we introduce BBK*, a new CPD algorithm whose key innovation is the multisequence (MS) bound: BBK* efficiently computes a single provable upper bound to approximate Ka for a combinatorial number of sequences, and avoids SS computation for all provably suboptimal sequences. Thus, to our knowledge, BBK* is the first provable, ensemble-based CPD algorithm to run in time sublinear in the number of sequences. Computational experiments on 204 protein design problems show that BBK* finds the tightest binding sequences while approximating Ka for up to 105-fold fewer sequences than the previous state-of-the-art algorithms, which require exhaustive enumeration of sequences. Furthermore, for 51 protein-ligand design problems, BBK* provably approximates Ka up to 1982-fold faster than the previous state-of-the-art iMinDEE/[Formula: see text]/[Formula: see text] algorithm. Therefore, BBK* not only accelerates protein designs that are possible with previous provable algorithms, but also efficiently performs designs that are too large for previous methods.
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Affiliation(s)
- Adegoke A Ojewole
- 1 Department of Computer Science, Duke University , Durham, North Carolina.,2 Computational Biology and Bioinformatics Program, Duke University , Durham, North Carolina
| | - Jonathan D Jou
- 1 Department of Computer Science, Duke University , Durham, North Carolina
| | - Vance G Fowler
- 3 Division of Infectious Diseases, Duke University Medical Center , Durham, North Carolina
| | - Bruce R Donald
- 1 Department of Computer Science, Duke University , Durham, North Carolina.,4 Department of Biochemistry, Duke University Medical Center , Durham North Carolina
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136
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Do TD, Sangwan S, de Almeida NEC, Ilitchev AI, Giammona M, Sawaya MR, Buratto SK, Eisenberg DS, Bowers MT. Distal amyloid β-protein fragments template amyloid assembly. Protein Sci 2018; 27:1181-1190. [PMID: 29349888 DOI: 10.1002/pro.3375] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 01/16/2018] [Accepted: 01/16/2018] [Indexed: 01/06/2023]
Abstract
Amyloid formation is associated with devastating diseases such as Alzheimer's, Parkinson's and Type-2 diabetes. The large amyloid deposits found in patients suffering from these diseases have remained difficult to probe by structural means. Recent NMR models also predict heterotypic interactions from distinct peptide fragments but limited evidence of heterotypic packed sheets is observed in solution. Here we characterize two segments of the protein amyloid β (Aβ) known to form fibrils in Alzheimer's disease patients. We designed two variants of Aβ(19-24) and Aβ(27-32), IFAEDV (I6V) and NKGAIF (N6F) to lower the aggregation propensity of individual peptides while maintaining the similar interactions between the two segments in their native forms. We found that the variants do not form significant amyloid fibrils individually but a 1:1 mixture forms abundant fibrils. Using ion mobility-mass spectrometry (IM-MS), hetero-oligomers up to decamers were found in the mixture while the individual peptides formed primarily dimers and some tetramers consistent with a strong heterotypic interaction between the two segments. We showed by X-ray crystallography that I6V formed a Class 7 zipper with a weakly packed pair of β-sheets and no segregated dry interface, while N6F formed a more stable Class 1 zipper. In a mixture of equimolar N6F:I6V, I6V forms a more stable zipper than in I6V alone while no N6F or hetero-typic zippers are observed. These data are consistent with a mechanism where N6F catalyzes assembly of I6V into a stable zipper and perhaps into stable, pure I6V fibrils that are observed in AFM measurements.
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Affiliation(s)
- Thanh D Do
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California
| | - Smriti Sangwan
- Departments of Chemistry and Biochemistry and Biological Chemistry, Howard Hughes Medical Institute, UCLA-DOE Institute for Genomics and Proteomics, University of California, 611 Charles Young Drive East, Los Angeles, California
| | - Natália E C de Almeida
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California
| | - Alexandre I Ilitchev
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California
| | - Maxwell Giammona
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California
| | - Michael R Sawaya
- Departments of Chemistry and Biochemistry and Biological Chemistry, Howard Hughes Medical Institute, UCLA-DOE Institute for Genomics and Proteomics, University of California, 611 Charles Young Drive East, Los Angeles, California
| | - Steven K Buratto
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California
| | - David S Eisenberg
- Departments of Chemistry and Biochemistry and Biological Chemistry, Howard Hughes Medical Institute, UCLA-DOE Institute for Genomics and Proteomics, University of California, 611 Charles Young Drive East, Los Angeles, California
| | - Michael T Bowers
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California
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137
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Kinjo AR. Cooperative "folding transition" in the sequence space facilitates function-driven evolution of protein families. J Theor Biol 2018; 443:18-27. [PMID: 29355538 DOI: 10.1016/j.jtbi.2018.01.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2017] [Revised: 01/16/2018] [Accepted: 01/17/2018] [Indexed: 12/23/2022]
Abstract
In the protein sequence space, natural proteins form clusters of families which are characterized by their unique native folds whereas the great majority of random polypeptides are neither clustered nor foldable to unique structures. Since a given polypeptide can be either foldable or unfoldable, a kind of "folding transition" is expected at the boundary of a protein family in the sequence space. By Monte Carlo simulations of a statistical mechanical model of protein sequence alignment that coherently incorporates both short-range and long-range interactions as well as variable-length insertions to reproduce the statistics of the multiple sequence alignment of a given protein family, we demonstrate the existence of such transition between natural-like sequences and random sequences in the sequence subspaces for 15 domain families of various folds. The transition was found to be highly cooperative and two-state-like. Furthermore, enforcing or suppressing consensus residues on a few of the well-conserved sites enhanced or diminished, respectively, the natural-like pattern formation over the entire sequence. In most families, the key sites included ligand binding sites. These results suggest some selective pressure on the key residues, such as ligand binding activity, may cooperatively facilitate the emergence of a protein family during evolution. From a more practical aspect, the present results highlight an essential role of long-range effects in precisely defining protein families, which are absent in conventional sequence models.
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Affiliation(s)
- Akira R Kinjo
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan.
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138
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Hánová I, Brynda J, Houštecká R, Alam N, Sojka D, Kopáček P, Marešová L, Vondrášek J, Horn M, Schueler-Furman O, Mareš M. Novel Structural Mechanism of Allosteric Regulation of Aspartic Peptidases via an Evolutionarily Conserved Exosite. Cell Chem Biol 2018; 25:318-329.e4. [PMID: 29396291 DOI: 10.1016/j.chembiol.2018.01.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 12/04/2017] [Accepted: 12/28/2017] [Indexed: 11/25/2022]
Abstract
Pepsin-family aspartic peptidases are biosynthesized as inactive zymogens in which the propeptide blocks the active site until its proteolytic removal upon enzyme activation. Here, we describe a novel dual regulatory function for the propeptide using a set of crystal structures of the parasite cathepsin D IrCD1. In the IrCD1 zymogen, intramolecular autoinhibition by the intact propeptide is mediated by an evolutionarily conserved exosite on the enzyme core. After activation, the mature enzyme employs the same exosite to rebind a small fragment derived from the cleaved propeptide. This fragment functions as an effective natural inhibitor of mature IrCD1 that operates in a pH-dependent manner through a unique allosteric inhibition mechanism. The study uncovers the propeptide-binding exosite as a target for the regulation of pepsin-family aspartic peptidases and defines the structural requirements for exosite inhibition.
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Affiliation(s)
- Iva Hánová
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 16610 Prague, Czech Republic; Department of Biochemistry, Faculty of Science, Charles University, 12840 Prague, Czech Republic
| | - Jiří Brynda
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 16610 Prague, Czech Republic
| | - Radka Houštecká
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 16610 Prague, Czech Republic; First Faculty of Medicine, Charles University, 12108 Prague, Czech Republic
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, Institute for Biomedical Research IMRIC, Hebrew University, Hadassah Medical School, 91120 Jerusalem, Israel
| | - Daniel Sojka
- Institute of Parasitology, Biology Centre of the Czech Academy of Sciences, 37005 Ceske Budejovice, Czech Republic
| | - Petr Kopáček
- Institute of Parasitology, Biology Centre of the Czech Academy of Sciences, 37005 Ceske Budejovice, Czech Republic
| | - Lucie Marešová
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 16610 Prague, Czech Republic
| | - Jiří Vondrášek
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 16610 Prague, Czech Republic
| | - Martin Horn
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 16610 Prague, Czech Republic
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Biomedical Research IMRIC, Hebrew University, Hadassah Medical School, 91120 Jerusalem, Israel
| | - Michael Mareš
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 16610 Prague, Czech Republic.
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139
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Duran AM, Meiler J. Computational design of membrane proteins using RosettaMembrane. Protein Sci 2018; 27:341-355. [PMID: 29090504 PMCID: PMC5734395 DOI: 10.1002/pro.3335] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 10/27/2017] [Accepted: 10/30/2017] [Indexed: 11/11/2022]
Abstract
Computational membrane protein design is challenging due to the small number of high-resolution structures available to elucidate the physical basis of membrane protein structure, multiple functionally important conformational states, and a limited number of high-throughput biophysical assays to monitor function. However, structural determination of membrane proteins has made tremendous progress in the past years. Concurrently the field of soluble computational design has made impressive inroads. These developments allow us to tackle the formidable challenge of designing functional membrane proteins. Herein, Rosetta is benchmarked for membrane protein design. We evaluate strategies to cope with the often reduced quality of experimental membrane protein structures. Further, we test the usage of symmetry in design protocols, which is particularly important as many membrane proteins exist as homo-oligomers. We compare a soluble scoring function with a scoring function optimized for membrane proteins, RosettaMembrane. Both scoring functions recovered around half of the native sequence when completely redesigning membrane proteins. However, RosettaMembrane recovered the most native-like amino acid property composition. While leucine was overrepresented in the inner and outer-hydrophobic regions of RosettaMembrane designs, it resulted in a native-like surface hydrophobicity indicating that it is currently the best option for designing membrane proteins with Rosetta.
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Affiliation(s)
- Amanda M. Duran
- Department of ChemistryVanderbilt UniversityNashvilleTennessee37235
- Center for Structural BiologyVanderbilt UniversityNashvilleTennessee37240
| | - Jens Meiler
- Department of ChemistryVanderbilt UniversityNashvilleTennessee37235
- Center for Structural BiologyVanderbilt UniversityNashvilleTennessee37240
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140
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Summers TJ, Cheng Q, DeYonker NJ. A transition state “trapped”? QM-cluster models of engineered threonyl-tRNA synthetase. Org Biomol Chem 2018; 16:4090-4100. [DOI: 10.1039/c8ob00540k] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
QM-cluster models demonstrate how protein bioengineering alters the local energy landscape of p-biphenylalanine to stabilize a transition state analogue.
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Affiliation(s)
| | - Qianyi Cheng
- The Department of Chemistry
- The University of Memphis
- Memphis
- USA
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141
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Blacklock KM, Yachnin BJ, Woolley GA, Khare SD. Computational Design of a Photocontrolled Cytosine Deaminase. J Am Chem Soc 2017; 140:14-17. [PMID: 29251923 DOI: 10.1021/jacs.7b08709] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
There is growing interest in designing spatiotemporal control over enzyme activities using noninvasive stimuli such as light. Here, we describe a structure-based, computation-guided predictive method for reversibly controlling enzyme activity using covalently attached photoresponsive azobenzene groups. Applying the method to the therapeutically useful enzyme yeast cytosine deaminase, we obtained a ∼3-fold change in enzyme activity by the photocontrolled modulation of the enzyme's active site lid structure, while fully maintaining thermostability. Multiple cycles of switching, controllable in real time, are possible. The predictiveness of the method is demonstrated by the construction of a variant that does not photoswitch as expected from computational modeling. Our design approach opens new avenues for optically controlling enzyme function. The designed photocontrolled cytosine deaminases may also aid in improving chemotherapy approaches that utilize this enzyme.
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Affiliation(s)
- Kristin M Blacklock
- Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers University , New Brunswick, New Jersey 08854, United States
| | - Brahm J Yachnin
- Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers University , New Brunswick, New Jersey 08854, United States
| | - G Andrew Woolley
- Department of Chemistry, University of Toronto , Toronto, Ontario M5S 3H6, Canada
| | - Sagar D Khare
- Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers University , New Brunswick, New Jersey 08854, United States
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142
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Özen A, Rougé L, Bashore C, Hearn BR, Skelton NJ, Dueber EC. Selectively Modulating Conformational States of USP7 Catalytic Domain for Activation. Structure 2017; 26:72-84.e7. [PMID: 29249604 DOI: 10.1016/j.str.2017.11.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 08/31/2017] [Accepted: 11/15/2017] [Indexed: 12/11/2022]
Abstract
Ubiquitin-specific protease 7 (USP7) deubiquitinase activity is controlled by a number of regulatory factors, including stimulation by intramolecular accessory domains. Alone, the USP7 catalytic domain (USP7cd) shows limited activity and apo USP7cd crystal structures reveal a disrupted catalytic triad. By contrast, ubiquitin-conjugated USP7cd structures demonstrate the canonical cysteine protease active-site geometry; however, the structural features of the USP7cd that stabilize the inactive conformation and the mechanism of transition between inactive and active states remain unclear. Here we use comparative structural analyses, molecular dynamics simulations, and in silico sequence re-engineering via directed sampling by RosettaDesign to identify key molecular determinants of USP7cd activation and successfully engineer USP7cd for improved activity. Full kinetic analysis and multiple X-ray crystal structures of our designs indicate that electrostatic interactions in the distal "switching loop" region and local packing in the hydrophobic core mediate subtle but significant conformational changes that modulate USP7cd activation.
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Affiliation(s)
- Ayşegül Özen
- Department of Early Discovery Biochemistry, Genentech Inc., South San Francisco, CA 94080, USA; Department of Discovery Chemistry, Genentech Inc., South San Francisco, CA 94080, USA
| | - Lionel Rougé
- Department of Structural Biology, Genentech Inc., South San Francisco, CA 94080, USA
| | - Charlene Bashore
- Department of Early Discovery Biochemistry, Genentech Inc., South San Francisco, CA 94080, USA
| | - Brian R Hearn
- Department of Pharmaceutical Chemistry and Small Molecule Discovery Center, University of California, San Francisco, San Francisco, CA 94618, USA
| | - Nicholas J Skelton
- Department of Discovery Chemistry, Genentech Inc., South San Francisco, CA 94080, USA.
| | - Erin C Dueber
- Department of Early Discovery Biochemistry, Genentech Inc., South San Francisco, CA 94080, USA.
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143
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Brunecky R, Alahuhta M, Sammond DW, Xu Q, Chen M, Wilson DB, Brady JW, Himmel ME, Bomble YJ, Lunin VV. Natural diversity of glycoside hydrolase family 48 exoglucanases: insights from structure. BIOTECHNOLOGY FOR BIOFUELS 2017; 10:274. [PMID: 29213319 PMCID: PMC5707792 DOI: 10.1186/s13068-017-0951-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 11/02/2017] [Indexed: 05/27/2023]
Abstract
Glycoside hydrolase (GH) family 48 is an understudied and increasingly important exoglucanase family found in the majority of bacterial cellulase systems. Moreover, many thermophilic enzyme systems contain GH48 enzymes. Deletion of GH48 enzymes in these microorganisms results in drastic reduction in biomass deconstruction. Surprisingly, given their importance for these microorganisms, GH48s have intrinsically low cellulolytic activity but even in low ratios synergize greatly with GH9 endoglucanases. In this study, we explore the structural and enzymatic diversity of these enzymes across a wide range of temperature optima. We have crystallized one new GH48 module from Bacillus pumilus in a complex with cellobiose and cellohexaose (BpumGH48). We compare this structure to other known GH48 enzymes in an attempt to understand GH48 structure/function relationships and draw general rules correlating amino acid sequences and secondary structures to thermostability in this GH family.
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Affiliation(s)
- Roman Brunecky
- Biosciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401 USA
| | - Markus Alahuhta
- Biosciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401 USA
| | - Deanne W. Sammond
- Biosciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401 USA
| | - Qi Xu
- Biosciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401 USA
| | - Mo Chen
- Department of Food Science, Cornell University, Ithaca, NY USA
| | - David B. Wilson
- Biochemistry, Molecular and Cell Biology, Cornell University, Ithaca, NY USA
| | - John W. Brady
- Department of Food Science, Cornell University, Ithaca, NY USA
| | - Michael E. Himmel
- Biosciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401 USA
| | - Yannick J. Bomble
- Biosciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401 USA
| | - Vladimir V. Lunin
- Biosciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401 USA
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144
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Abstract
Thermostabilization represents a critical and often obligatory step toward enhancing the robustness of enzymes for organic synthesis and other applications. While directed evolution methods have provided valuable tools for this purpose, these protocols are laborious and time-consuming and typically require the accumulation of several mutations, potentially at the expense of catalytic function. Here, we report a minimally invasive strategy for enzyme stabilization that relies on the installation of genetically encoded, nonreducible covalent staples in a target protein scaffold using computational design. This methodology enables the rapid development of myoglobin-based cyclopropanation biocatalysts featuring dramatically enhanced thermostability (ΔTm = +18.0 °C and ΔT50 = +16.0 °C) as well as increased stability against chemical denaturation [ΔCm (GndHCl) = 0.53 M], without altering their catalytic efficiency and stereoselectivity properties. In addition, the stabilized variants offer superior performance and selectivity compared with the parent enzyme in the presence of a high concentration of organic cosolvents, enabling the more efficient cyclopropanation of a water-insoluble substrate. This work introduces and validates an approach for protein stabilization which should be applicable to a variety of other proteins and enzymes.
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145
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Tian P, Best RB. How Many Protein Sequences Fold to a Given Structure? A Coevolutionary Analysis. Biophys J 2017; 113:1719-1730. [PMID: 29045866 PMCID: PMC5647607 DOI: 10.1016/j.bpj.2017.08.039] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Revised: 08/03/2017] [Accepted: 08/08/2017] [Indexed: 12/23/2022] Open
Abstract
Quantifying the relationship between protein sequence and structure is key to understanding the protein universe. A fundamental measure of this relationship is the total number of amino acid sequences that can fold to a target protein structure, known as the "sequence capacity," which has been suggested as a proxy for how designable a given protein fold is. Although sequence capacity has been extensively studied using lattice models and theory, numerical estimates for real protein structures are currently lacking. In this work, we have quantitatively estimated the sequence capacity of 10 proteins with a variety of different structures using a statistical model based on residue-residue co-evolution to capture the variation of sequences from the same protein family. Remarkably, we find that even for the smallest protein folds, such as the WW domain, the number of foldable sequences is extremely large, exceeding the Avogadro constant. In agreement with earlier theoretical work, the calculated sequence capacity is positively correlated with the size of the protein, or better, the density of contacts. This allows the absolute sequence capacity of a given protein to be approximately predicted from its structure. On the other hand, the relative sequence capacity, i.e., normalized by the total number of possible sequences, is an extremely tiny number and is strongly anti-correlated with the protein length. Thus, although there may be more foldable sequences for larger proteins, it will be much harder to find them. Lastly, we have correlated the evolutionary age of proteins in the CATH database with their sequence capacity as predicted by our model. The results suggest a trade-off between the opposing requirements of high designability and the likelihood of a novel fold emerging by chance.
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Affiliation(s)
- Pengfei Tian
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Robert B Best
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland.
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146
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Gaillard T, Simonson T. Full Protein Sequence Redesign with an MMGBSA Energy Function. J Chem Theory Comput 2017; 13:4932-4943. [DOI: 10.1021/acs.jctc.7b00202] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Thomas Gaillard
- Laboratoire de Biochimie
(CNRS UMR7654), Department of Biology, Ecole Polytechnique, 91128 Palaiseau, France
| | - Thomas Simonson
- Laboratoire de Biochimie
(CNRS UMR7654), Department of Biology, Ecole Polytechnique, 91128 Palaiseau, France
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147
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In silico methods for design of biological therapeutics. Methods 2017; 131:33-65. [PMID: 28958951 DOI: 10.1016/j.ymeth.2017.09.008] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 09/21/2017] [Accepted: 09/23/2017] [Indexed: 12/18/2022] Open
Abstract
It has been twenty years since the first rationally designed small molecule drug was introduced into the market. Since then, we have progressed from designing small molecules to designing biotherapeutics. This class of therapeutics includes designed proteins, peptides and nucleic acids that could more effectively combat drug resistance and even act in cases where the disease is caused because of a molecular deficiency. Computational methods are crucial in this design exercise and this review discusses the various elements of designing biotherapeutic proteins and peptides. Many of the techniques discussed here, such as the deterministic and stochastic design methods, are generally used in protein design. We have devoted special attention to the design of antibodies and vaccines. In addition to the methods for designing these molecules, we have included a comprehensive list of all biotherapeutics approved for clinical use. Also included is an overview of methods that predict the binding affinity, cell penetration ability, half-life, solubility, immunogenicity and toxicity of the designed therapeutics. Biotherapeutics are only going to grow in clinical importance and are set to herald a new generation of disease management and cure.
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148
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Liu Y, Liu D, Yang W, Wu XL, Lai L, Zhang WB. Tuning SpyTag-SpyCatcher mutant pairs toward orthogonal reactivity encryption. Chem Sci 2017; 8:6577-6582. [PMID: 28989685 PMCID: PMC5627348 DOI: 10.1039/c7sc02686b] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 07/18/2017] [Indexed: 12/27/2022] Open
Abstract
Genetically encoded covalent peptide tagging technology, such as the SpyTag-SpyCatcher reaction, has emerged as a unique way to do chemistry with proteins. Herein, we report the reactivity engineering of SpyTag-SpyCatcher mutant pairs and show that distinct reactivity can be encrypted for the same reaction based on protein sequences of high similarity. Valuable features, including high selectivity, inverse temperature dependence and (nearly) orthogonal reactivity, could be achieved based on as few as three mutations. This demonstrates the robustness of the SpyTag-SpyCatcher reaction and the plasticity of its sequence specificity, pointing to a family of engineered protein chemistry tools.
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Affiliation(s)
- Yajie Liu
- Key Laboratory of Polymer Chemistry & Physics of Ministry of Education , Center for Soft Matter Science and Engineering , College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , P. R. China .
| | - Dong Liu
- Key Laboratory of Polymer Chemistry & Physics of Ministry of Education , Center for Soft Matter Science and Engineering , College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , P. R. China .
| | - Wei Yang
- School of Life Sciences , Tsinghua University , Beijing 100084 , P. R. China
| | - Xia-Ling Wu
- Key Laboratory of Polymer Chemistry & Physics of Ministry of Education , Center for Soft Matter Science and Engineering , College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , P. R. China .
| | - Luhua Lai
- BNLMS , Peking-Tsinghua Center for Life Sciences , Academy for Advanced Interdisciplinary Studies (AAIS) , Center for Quantitative Biology , State Key Laboratory for Structural Chemistry of Unstable and Stable Species , College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , P. R. China .
| | - Wen-Bin Zhang
- Key Laboratory of Polymer Chemistry & Physics of Ministry of Education , Center for Soft Matter Science and Engineering , College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , P. R. China .
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149
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Banadyga L, Hoenen T, Ambroggio X, Dunham E, Groseth A, Ebihara H. Ebola virus VP24 interacts with NP to facilitate nucleocapsid assembly and genome packaging. Sci Rep 2017; 7:7698. [PMID: 28794491 PMCID: PMC5550494 DOI: 10.1038/s41598-017-08167-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 07/06/2017] [Indexed: 02/03/2023] Open
Abstract
Ebola virus causes devastating hemorrhagic fever outbreaks for which no approved therapeutic exists. The viral nucleocapsid, which is minimally composed of the proteins NP, VP35, and VP24, represents an attractive target for drug development; however, the molecular determinants that govern the interactions and functions of these three proteins are still unknown. Through a series of mutational analyses, in combination with biochemical and bioinformatics approaches, we identified a region on VP24 that was critical for its interaction with NP. Importantly, we demonstrated that the interaction between VP24 and NP was required for both nucleocapsid assembly and genome packaging. Not only does this study underscore the critical role that these proteins play in the viral replication cycle, but it also identifies a key interaction interface on VP24 that may serve as a novel target for antiviral therapeutic intervention.
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Affiliation(s)
- Logan Banadyga
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, 59840, USA
| | - Thomas Hoenen
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, 59840, USA.,Friedrich-Loeffler-Institut, Greifswald, Insel Riems, Germany
| | - Xavier Ambroggio
- Bioinformatics and Computational Biosciences Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA.,Rosetta Design Group, Burlington, VT, 05401, USA
| | - Eric Dunham
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, 59840, USA
| | - Allison Groseth
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, 59840, USA.,Friedrich-Loeffler-Institut, Greifswald, Insel Riems, Germany
| | - Hideki Ebihara
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, 59840, USA. .,Department of Molecular Medicine, Mayo Clinic, Rochester, MN, 55905, USA.
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Villa F, Mignon D, Polydorides S, Simonson T. Comparing pairwise-additive and many-body generalized Born models for acid/base calculations and protein design. J Comput Chem 2017; 38:2396-2410. [PMID: 28749575 DOI: 10.1002/jcc.24898] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Revised: 06/30/2017] [Accepted: 07/06/2017] [Indexed: 12/13/2022]
Abstract
Generalized Born (GB) solvent models are common in acid/base calculations and protein design. With GB, the interaction between a pair of solute atoms depends on the shape of the protein/solvent boundary and, therefore, the positions of all solute atoms, so that GB is a many-body potential. For compute-intensive applications, the model is often simplified further, by introducing a mean, native-like protein/solvent boundary, which removes the many-body property. We investigate a method for both acid/base calculations and protein design that uses Monte Carlo simulations in which side chains can explore rotamers, bind/release protons, or mutate. The fluctuating protein/solvent dielectric boundary is treated in a way that is numerically exact (within the GB framework), in contrast to a mean boundary. Its originality is that it captures the many-body character while retaining the residue-pairwise complexity given by a fixed boundary. The method is implemented in the Proteus protein design software. It yields a slight but systematic improvement for acid/base constants in nine proteins and a significant improvement for the computational design of three PDZ domains. It eliminates a source of model uncertainty, which will facilitate the analysis of other model limitations. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Francesco Villa
- Ecole Polytechnique, Laboratoire de Biochimie (CNRS UMR7654), Palaiseau, 91128, France
| | - David Mignon
- Ecole Polytechnique, Laboratoire de Biochimie (CNRS UMR7654), Palaiseau, 91128, France
| | - Savvas Polydorides
- Ecole Polytechnique, Laboratoire de Biochimie (CNRS UMR7654), Palaiseau, 91128, France
| | - Thomas Simonson
- Ecole Polytechnique, Laboratoire de Biochimie (CNRS UMR7654), Palaiseau, 91128, France
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