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11 results on '"Eva-Maria Strauch"'

1. A general computational design strategy for stabilizing viral class I fusion proteins

Author

Karen J. Gonzalez, Jiachen Huang, Miria F. Criado, Avik Banerjee, Stephen M. Tompkins, Jarrod J. Mousa, and Eva-Maria Strauch

Subjects
Science
Abstract

Abstract Many pathogenic viruses rely on class I fusion proteins to fuse their viral membrane with the host cell membrane. To drive the fusion process, class I fusion proteins undergo an irreversible conformational change from a metastable prefusion state to an energetically more stable postfusion state. Mounting evidence underscores that antibodies targeting the prefusion conformation are the most potent, making it a compelling vaccine candidate. Here, we establish a computational design protocol that stabilizes the prefusion state while destabilizing the postfusion conformation. With this protocol, we stabilize the fusion proteins of the RSV, hMPV, and SARS-CoV-2 viruses, testing fewer than a handful of designs. The solved structures of these designed proteins from all three viruses evidence the atomic accuracy of our approach. Furthermore, the humoral response of the redesigned RSV F protein compares to that of the recently approved vaccine in a mouse model. While the parallel design of two conformations allows the identification of energetically sub-optimal positions for one conformation, our protocol also reveals diverse molecular strategies for stabilization. Given the clinical significance of viruses using class I fusion proteins, our algorithm can substantially contribute to vaccine development by reducing the time and resources needed to optimize these immunogens.

Published
2024
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2. Systematic computer-aided disulfide design as a general strategy to stabilize prefusion class I fusion proteins

Author

Karen J. Gonzalez, Kevin C. Yim, Jorge C. G. Blanco, Marina S. Boukhvalova, and Eva-Maria Strauch

Subjects
vaccine development, class I fusion proteins, prefusion stabilization, disulfide design, computational modeling, Immunologic diseases. Allergy, RC581-607
Abstract

Numerous enveloped viruses, such as coronaviruses, influenza, and respiratory syncytial virus (RSV), utilize class I fusion proteins for cell entry. During this process, the proteins transition from a prefusion to a postfusion state, undergoing substantial and irreversible conformational changes. The prefusion conformation has repeatedly shown significant potential in vaccine development. However, the instability of this state poses challenges for its practical application in vaccines. While non-native disulfides have been effective in maintaining the prefusion structure, identifying stabilizing disulfide bonds remains an intricate task. Here, we present a general computational approach to systematically identify prefusion-stabilizing disulfides. Our method assesses the geometric constraints of disulfide bonds and introduces a ranking system to estimate their potential in stabilizing the prefusion conformation. We hypothesized that disulfides restricting the initial stages of the conformational switch could offer higher stability to the prefusion state than those preventing unfolding at a later stage. The implementation of our algorithm on the RSV F protein led to the discovery of prefusion-stabilizing disulfides that supported our hypothesis. Furthermore, the evaluation of our top design as a vaccine candidate in a cotton rat model demonstrated robust protection against RSV infection, highlighting the potential of our approach for vaccine development.

Published
2024
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3. Sampling of structure and sequence space of small protein folds

Author

Thomas W. Linsky, Kyle Noble, Autumn R. Tobin, Rachel Crow, Lauren Carter, Jeffrey L. Urbauer, David Baker, and Eva-Maria Strauch

Subjects
Science
Abstract

In this work the authors provide a computational workflow for the parallel, from scratch, design of proteins to rapidly explore the shape diversity of protein folds.

Published
2022
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4. Large-scale design and refinement of stable proteins using sequence-only models

Author

Jedediah M. Singer, Scott Novotney, Devin Strickland, Hugh K. Haddox, Nicholas Leiby, Gabriel J. Rocklin, Cameron M. Chow, Anindya Roy, Asim K. Bera, Francis C. Motta, Longxing Cao, Eva-Maria Strauch, Tamuka M. Chidyausiku, Alex Ford, Ethan Ho, Alexander Zaitzeff, Craig O. Mackenzie, Hamed Eramian, Frank DiMaio, Gevorg Grigoryan, Matthew Vaughn, Lance J. Stewart, David Baker, and Eric Klavins

Subjects
Medicine, Science
Abstract

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we use a high-throughput, low-fidelity assay to experimentally evaluate the stability of approximately 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We build a neural network model that predicts protein stability given only sequences of amino acids, and compare its performance to the assayed values. We also report another network model that is able to generate the amino acid sequences of novel stable proteins given requested secondary sequences. Finally, we show that the predictive model—despite weaknesses including a noisy data set—can be used to substantially increase the stability of both expert-designed and model-generated proteins.

Published
2022

5. Targeting Viral Surface Proteins through Structure-Based Design

Author

Yogesh B Narkhede, Karen J Gonzalez, and Eva-Maria Strauch

Subjects
vaccine, glycoproteins, structural vaccinology, rational design, computational protein design, respiratory viruses, Microbiology, QR1-502
Abstract

The emergence of novel viral infections of zoonotic origin and mutations of existing human pathogenic viruses represent a serious concern for public health. It warrants the establishment of better interventions and protective therapies to combat the virus and prevent its spread. Surface glycoproteins catalyzing the fusion of viral particles and host cells have proven to be an excellent target for antivirals as well as vaccines. This review focuses on recent advances for computational structure-based design of antivirals and vaccines targeting viral fusion machinery to control seasonal and emerging respiratory viruses.

Published
2021
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6. RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite.

Author

Sarel J Fleishman, Andrew Leaver-Fay, Jacob E Corn, Eva-Maria Strauch, Sagar D Khare, Nobuyasu Koga, Justin Ashworth, Paul Murphy, Florian Richter, Gordon Lemmon, Jens Meiler, and David Baker

Subjects
Medicine, Science
Abstract

Macromolecular modeling and design are increasingly useful in basic research, biotechnology, and teaching. However, the absence of a user-friendly modeling framework that provides access to a wide range of modeling capabilities is hampering the wider adoption of computational methods by non-experts. RosettaScripts is an XML-like language for specifying modeling tasks in the Rosetta framework. RosettaScripts provides access to protocol-level functionalities, such as rigid-body docking and sequence redesign, and allows fast testing and deployment of complex protocols without need for modifying or recompiling the underlying C++ code. We illustrate these capabilities with RosettaScripts protocols for the stabilization of proteins, the generation of computationally constrained libraries for experimental selection of higher-affinity binding proteins, loop remodeling, small-molecule ligand docking, design of ligand-binding proteins, and specificity redesign in DNA-binding proteins.

Published
2011
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7. Skp1 Dimerization Conceals its F-box Protein Binding Site

Author

Christopher M. West, Alexander Eletsky, James H. Prestegard, Karen J. Gonzalez, Hyun W. Kim, Eva-Maria Strauch, and Hanke van der Wel

Subjects
Models, Molecular, Protein family, Stereochemistry, Dimer, 030303 biophysics, Protein degradation, Biochemistry, F-box protein, Article, 03 medical and health sciences, chemistry.chemical_compound, Skp1, Humans, Binding site, S-Phase Kinase-Associated Proteins, 030304 developmental biology, chemistry.chemical_classification, 0303 health sciences, Binding Sites, biology, F-Box Proteins, 030302 biochemistry & molecular biology, Amino acid, Monomer, chemistry, biology.protein, Dimerization
Abstract

Skp1 is an adapter that links F-box proteins to cullin-1 in the Skp1/cullin-1/F-box (SCF) protein family of E3 ubiquitin ligases that targets specific proteins for polyubiquitination and subsequent protein degradation. Skp1 from the amoebozoan Dictyostelium forms a stable homodimer in vitro with a Kd of 2.5 µM as determined by sedimentation velocity studies, yet is monomeric in crystal complexes with F-box proteins. To investigate the molecular basis for the difference, we determined the solution NMR structure of a doubly truncated Skp1 homodimer (Skp1ΔΔ). The solution structure of Skp1ΔΔ dimer reveals a 2-fold symmetry with an interface that buries ∼750 Å2 of predominantly hydrophobic surface. The dimer interface overlaps with subsite-1 of the F-box interaction area, explaining why only the Skp1 monomer binds F-box proteins (FBPs). To confirm the model, Rosetta was used to predict amino acid substitutions that might disrupt the dimer interface, and the F97E substitution was chosen to potentially minimize interference with F-box interactions. A nearly full-length version of Skp1 with this substitution (Skp1ΔF97E) behaved as a stable monomer at concentrations up to 500 µM and actively bound a model FBP, mammalian Fbs1, which suggests that the dimeric state is not required for Skp1 to carry out a basic biochemical function. Finally, Skp1ΔF97E is expected to serve as a monomer model for high-resolution NMR studies previously hindered by dimerization.

Published
2020

8. Computational design of a pH-sensitive IgG binding protein

Author

Eva-Maria Strauch, Sarel J. Fleishman, and David Baker

Subjects
Models, Molecular, Protein Conformation, Plasma protein binding, Biology, Protein Engineering, Immunoglobulin G, Protein structure, Affinity chromatography, Protein Interaction Domains and Motifs, Cloning, Molecular, Gene Library, Multidisciplinary, Binding protein, Circular Dichroism, Computational Biology, High-Throughput Nucleotide Sequencing, Protein engineering, Biological Sciences, Hydrogen-Ion Concentration, Flow Cytometry, Interferometry, Biochemistry, IgG binding, Mutagenesis, biology.protein, Protein ligand, Protein Binding
Abstract

Computational design provides the opportunity to program protein-protein interactions for desired applications. We used de novo protein interface design to generate a pH-dependent Fc domain binding protein that buries immunoglobulin G (IgG) His-433. Using next-generation sequencing of naive and selected pools of a library of design variants, we generated a molecular footprint of the designed binding surface, confirming the binding mode and guiding further optimization of the balance between affinity and pH sensitivity. In biolayer interferometry experiments, the optimized design binds IgG with a Kd of ∼ 4 nM at pH 8.2, and approximately 500-fold more weakly at pH 5.5. The protein is extremely stable, heat-resistant and highly expressed in bacteria, and allows pH-based control of binding for IgG affinity purification and diagnostic devices.

Published
2013

9. Multiplex pairwise assembly of array-derived DNA oligonucleotides

Author

Jerrod J. Schwartz, Marc J. Lajoie, David Baker, Jorgen Nelson, Jay Shendure, Jason C. Klein, and Eva-Maria Strauch

Subjects
0301 basic medicine, Genetics, Expressed Sequence Tags, Oligonucleotide, Oligonucleotides, High-Throughput Nucleotide Sequencing, Computational biology, DNA, Oligonucleotide synthesis, Biology, Polymerase cycling assembly, Polymerase Chain Reaction, DNA sequencing, Sequencing by ligation, 03 medical and health sciences, 030104 developmental biology, Sequencing by hybridization, Methods Online, Multiplex, DNA microarray, Algorithms, DNA Primers, Oligonucleotide Array Sequence Analysis
Abstract

While the cost of DNA sequencing has dropped by five orders of magnitude in the past decade, DNA synthesis remains expensive for many applications. Although DNA microarrays have decreased the cost of oligonucleotide synthesis, the use of array-synthesized oligos in practice is limited by short synthesis lengths, high synthesis error rates, low yield and the challenges of assembling long constructs from complex pools. Toward addressing these issues, we developed a protocol for multiplex pairwise assembly of oligos from array-synthesized oligonucleotide pools. To evaluate the method, we attempted to assemble up to 2271 targets ranging in length from 192-252 bases using pairs of array-synthesized oligos. Within sets of complexity ranging from 131-250 targets, we observed error-free assemblies for 90.5% of all targets. When all 2271 targets were assembled in one reaction, we observed error-free constructs for 70.6%. While the assembly method intrinsically increased accuracy to a small degree, we further increased accuracy by using a high throughput 'Dial-Out PCR' protocol, which combines Illumina sequencing with an in-house set of unique PCR tags to selectively amplify perfect assemblies from complex synthetic pools. This approach has broad applicability to DNA assembly and high-throughput functional screens.

Published
2015

10. Community-Wide Assessment of Protein-Interface Modeling Suggests Improvements to Design Methodology

Author

Libin Cao, Anne Poupon, Brian G. Pierce, Howook Hwang, Ying Chen, Victor L. Hsu, Hasup Lee, Yangyu Huang, Daisuke Kihara, Juan Fernández-Recio, Vladimir Potapov, Aroop Sircar, Chaok Seok, Timothy A. Whitehead, Jérôme Azé, Nir Ben Tal, Seren Soner, Brian Kuhlman, P. Benjamin Stranges, Nobuyuki Uchikoga, Sanbo Qin, Xinqi Gong, Yi Xiao, Carlos J. Camacho, Yaoqi Zhou, Gideon Schreiber, Ora Schueler-Furman, Paul A. Bates, Krishna Praneeth Kilambi, Joël Janin, Mati Cohen, Julie C. Mitchell, Panwen Wang, Cunxin Wang, Raed Khashan, Mayuko Takeda-Shitaka, Lin Li, Martin Zacharias, Alexander Tropsha, Genki Terashi, Xiaofan Li, David Baker, Jian Zhan, Julie Bernauer, Zohar Itzhaki, Mainak Guharoy, Eva-Maria Strauch, Xiaoqin Zou, Thom Vreven, Hahnbeom Park, Sheng-You Huang, Stephen Bush, Daron M. Standley, Feng Yang, Yuko Tsuchiya, Fan Jiang, Jacob E. Corn, Takashi Ishida, Chunhua Li, Junsu Ko, Robert G. Hall, Thomas Bourquard, Iain H. Moal, Weiyi Zhang, C.M. Driggers, Nir London, Jessica L. Morgan, Ron Jacak, Haruki Nakamura, Laura Pérez-Cano, Denis Fouches, Bin Liu, Yutaka Akiyama, Omar N. A. Demerdash, Yuval Inbar, Xianjin Xu, Yuedong Yang, Dachuan Guo, Masahito Ohue, Turkan Haliloglu, Jeffrey J. Gray, Juan Esquivel-Rodríguez, Alexandre M. J. J. Bonvin, Pemra Ozbek, Sarel J. Fleishman, Şefik Kerem Ovali, Charles H. Robert, Huan-Xiang Zhou, Eiji Kanamori, Yuri Matsuzaki, Carles Pons, Zhiping Weng, Kengo Kinoshita, Shoshana J. Wodak, Shiyong Liu, Panagiotis L. Kastritis, University of Washington [Seattle], Institute of Molecular Biophysics [Tallahassee], Florida State University [Tallahassee] (FSU), University of Wisconsin Whitewater, Kitasato University, Biomolecular Modelling laboratory [London], Cancer Research UK London Research Institute, Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Seoul National University [Seoul] (SNU), Knowledge representation, reasonning (ORPAILLEUR), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Algorithms and Models for Integrative Biology (AMIB ), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Physiologie de la reproduction et des comportements [Nouzilly] (PRC), Institut National de la Recherche Agronomique (INRA)-Institut Français du Cheval et de l'Equitation [Saumur]-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), Department of Chemical Engineering [Bogazici] (ChE), Boǧaziçi üniversitesi = Boğaziçi University [Istanbul], Tel Aviv University [Tel Aviv], University of Massachusetts Medical School [Worcester] (UMASS), University of Massachusetts System (UMASS), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Chinese Academy of Sciences [Beijing] (CAS), Beijing University of Technology, Laboratoire de biochimie théorique [Paris] (LBT (UPR_9080)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Institut de biologie physico-chimique (IBPC (FR_550)), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Huazhong University of Science and Technology [Wuhan] (HUST), Hadassah Hebrew University Medical Center [Jerusalem], Weizmann Institute of Science [Rehovot, Israël], Institute for Protein Research [Osaka], Osaka University [Osaka], Japan Biological Informatics Consortium [Tokyo], WPI Immunology Frontier Research Center (IFREC), Graduate School of Information Sciences [Sendai], Tohoku University [Sendai], Oregon State University (OSU), Indiana University - Purdue University Indianapolis (IUPUI), Indiana University System, Bijvoet Center for Biomolecular Research [Utrecht], Utrecht University [Utrecht], University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), Johns Hopkins University (JHU), Tokyo Institute of Technology [Tokyo] (TITECH), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Purdue University [West Lafayette], Dalton Cardiovascular Research Center [Columbia], University of Missouri [Columbia] (Mizzou), University of Missouri System-University of Missouri System, The Hospital for sick children [Toronto] (SickKids), Institut de biochimie et biophysique moléculaire et cellulaire (IBBMC), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), The authors thank Sameer Velankar and Marc Lensink for their help in coordinating this experiment and Raik Grunberg for many helpful suggestions on a draft. S.J.F. was supported by a long-term fellowship from the Human Frontier Science Program. S.J.W. is Canada Research Chair Tier 1, funded by the Canadian Institutes of Health Research. Research in the Baker laboratory was supported by the Howard Hughes Medical Institute, the Defense Advanced Research Projects Agency, the National Institutes of Health Yeast Resource Center, and the Defense Threat Reduction Agency., Technical University of Munich (TUM), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Laboratoire de Recherche en Informatique (LRI), Centre National de la Recherche Scientifique (CNRS)-Université de Tours-Institut Français du Cheval et de l'Equitation [Saumur]-Institut National de la Recherche Agronomique (INRA), Boğaziçi University [Istanbul], Centre National de la Recherche Scientifique (CNRS)-Institut de biologie physico-chimique (IBPC), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), Weizmann Institute of Science, University of Missouri [Columbia], Institut National de la Recherche Agronomique (INRA)-Institut Français du Cheval et de l'Equitation [Saumur] (IFCE)-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), Tel Aviv University (TAU), Institut de biologie physico-chimique (IBPC (FR_550)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut Français du Cheval et de l'Equitation [Saumur]-Université de Tours-Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot - Paris 7 (UPD7)-Institut de biologie physico-chimique (IBPC), and Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Models, Molecular, biochemistry and molecular biology, Computer science, Protein design, Nanotechnology, Machine learning, computer.software_genre, Article, 03 medical and health sciences, Structural Biology, protein protein interactions, Taverne, conformational plasticity, Computational design, Macromolecular docking, CASP, Design methods, Molecular Biology, 030304 developmental biology, Protein interface, 0303 health sciences, Binding Sites, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM], business.industry, 030302 biochemistry & molecular biology, Proteins, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biomolecules [q-bio.BM], negative design, Docking (molecular), computational protein design, Critical assessment, Artificial intelligence, business, computer, Protein Binding
Abstract

International audience; The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.

Published
2011

11. Computational design of proteins targeting the conserved stem region of influenza hemagglutinin

Author

Sarel J. Fleishman, David Baker, C. Dreyfus, Eva-Maria Strauch, Ian A. Wilson, Damian C. Ekiert, Jacob E. Corn, and Timothy A. Whitehead

Subjects
Models, Molecular, Protein Conformation, Orthomyxoviridae, Molecular Sequence Data, Hemagglutinin (influenza), Hemagglutinin Glycoproteins, Influenza Virus, Plasma protein binding, Protein Engineering, Protein Structure, Secondary, Article, Affinity maturation, Protein structure, Peptide Library, Protein Interaction Domains and Motifs, Computer Simulation, Amino Acid Sequence, Binding site, Peptide library, Multidisciplinary, Binding Sites, biology, Computational Biology, Proteins, Hydrogen Bonding, Protein engineering, Hydrogen-Ion Concentration, biology.organism_classification, Biochemistry, Mutation, Biophysics, biology.protein, Hydrophobic and Hydrophilic Interactions, Algorithms, Software, Protein Binding
Abstract

We describe a general computational method for designing proteins that bind a surface patch of interest on a target macromolecule. Favorable interactions between disembodied amino acid residues and the target surface are identified and used to anchor de novo designed interfaces. The method was used to design proteins that bind a conserved surface patch on the stem of the influenza hemagglutinin (HA) from the 1918 H1N1 pandemic virus. After affinity maturation, two of the designed proteins, HB36 and HB80, bind H1 and H5 HAs with low nanomolar affinity. Further, HB80 inhibits the HA fusogenic conformational changes induced at low pH. The crystal structure of HB36 in complex with 1918/H1 HA revealed that the actual binding interface is nearly identical to that in the computational design model. Such designed binding proteins may be useful for both diagnostics and therapeutics.

Published
2011

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