1
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Tam C, Kukimoto-Niino M, Miyata-Yabuki Y, Tsuda K, Mishima-Tsumagari C, Ihara K, Inoue M, Yonemochi M, Hanada K, Matsumoto T, Shirouzu M, Zhang KYJ. Targeting Ras-binding domain of ELMO1 by computational nanobody design. Commun Biol 2023; 6:284. [PMID: 36932164 PMCID: PMC10023680 DOI: 10.1038/s42003-023-04657-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 03/02/2023] [Indexed: 03/19/2023] Open
Abstract
The control of cell movement through manipulation of cytoskeletal structure has therapeutic prospects notably in the development of novel anti-metastatic drugs. In this study, we determine the structure of Ras-binding domain (RBD) of ELMO1, a protein involved in cytoskeletal regulation, both alone and in complex with the activator RhoG and verify its targetability through computational nanobody design. Using our dock-and-design approach optimized with native-like initial pose selection, we obtain Nb01, a detectable binder from scratch in the first-round design. An affinity maturation step guided by structure-activity relationship at the interface generates 23 Nb01 sequence variants and 17 of them show enhanced binding to ELMO1-RBD and are modeled to form major spatial overlaps with RhoG. The best binder, Nb29, inhibited ELMO1-RBD/RhoG interaction. Molecular dynamics simulation of the flexibility of CDR2 and CDR3 of Nb29 reveal the design of stabilizing mutations at the CDR-framework junctions potentially confers the affinity enhancement.
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Affiliation(s)
- Chunlai Tam
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan
| | - Mutsuko Kukimoto-Niino
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan.
| | - Yukako Miyata-Yabuki
- Drug Discovery Structural Biology Platform Unit, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Kengo Tsuda
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Chiemi Mishima-Tsumagari
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Kentaro Ihara
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Mio Inoue
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Mayumi Yonemochi
- Drug Discovery Structural Biology Platform Unit, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Kazuharu Hanada
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Takehisa Matsumoto
- Drug Discovery Structural Biology Platform Unit, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Mikako Shirouzu
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
- Drug Discovery Structural Biology Platform Unit, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan.
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan.
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2
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Chauhan VM, Pantazes RJ. Analysis of conformational stability of interacting residues in protein binding interfaces. Protein Eng Des Sel 2023; 36:gzad016. [PMID: 37889566 PMCID: PMC10681001 DOI: 10.1093/protein/gzad016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 10/17/2023] [Accepted: 10/18/2023] [Indexed: 10/28/2023] Open
Abstract
After approximately 60 years of work, the protein folding problem has recently seen rapid advancement thanks to the inventions of AlphaFold and RoseTTAFold, which are machine-learning algorithms capable of reliably predicting protein structures from their sequences. A key component in their success was the inclusion of pairwise interaction information between residues. As research focus shifts towards developing algorithms to design and engineer binding proteins, it is likely that knowledge of interaction features at protein interfaces can improve predictions. Here, 574 protein complexes were analyzed to identify the stability features of their pairwise interactions, revealing that interactions between pre-stabilized residues are a selected feature in protein binding interfaces. In a retrospective analysis of 475 de novo designed binding proteins with an experimental success rate of 19%, inclusion of pairwise interaction pre-stabilization parameters increased the frequency of identifying experimentally successful binders to 40%.
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Affiliation(s)
- Varun M Chauhan
- Department of Chemical Engineering, Auburn University, Auburn, AL 36849, USA
| | - Robert J Pantazes
- Department of Chemical Engineering, Auburn University, Auburn, AL 36849, USA
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3
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Syrlybaeva R, Strauch EM. Deep learning of protein sequence design of protein-protein interactions. Bioinformatics 2023; 39:6827796. [PMID: 36377772 PMCID: PMC9947925 DOI: 10.1093/bioinformatics/btac733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 09/16/2022] [Accepted: 11/14/2022] [Indexed: 11/16/2022] Open
Abstract
MOTIVATION As more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence-structure relationships become more feasible. Within this space, the amino acid sequence design of protein-protein interactions is still a rather challenging subproblem with very low success rates-yet, it is central to most biological processes. RESULTS We developed an attention-based deep learning model inspired by algorithms used for image-caption assignments to design peptides or protein fragment sequences. Our trained model can be applied for the redesign of natural protein interfaces or the designed protein interaction fragments. Here, we validate the potential by recapitulating naturally occurring protein-protein interactions including antibody-antigen complexes. The designed interfaces accurately capture essential native interactions and have comparable native-like binding affinities in silico. Furthermore, our model does not need a precise backbone location, making it an attractive tool for working with de novo design of protein-protein interactions. AVAILABILITY AND IMPLEMENTATION The source code of the method is available at https://github.com/strauchlab/iNNterfaceDesign. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Raulia Syrlybaeva
- Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, GA 30602, USA
| | - Eva-Maria Strauch
- Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, GA 30602, USA.,Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
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4
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Chauhan VM, Pantazes RJ. MutDock: A computational docking approach for fixed-backbone protein scaffold design. Front Mol Biosci 2022; 9:933400. [PMID: 36106019 PMCID: PMC9465448 DOI: 10.3389/fmolb.2022.933400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Despite the successes of antibodies as therapeutic binding proteins, they still face production and design challenges. Alternative binding scaffolds of smaller size have been developed to overcome these issues. A subset of these alternative scaffolds recognizes target molecules through mutations to a set of surface resides, which does not alter their backbone structures. While the computational design of antibodies for target epitopes has been explored in depth, the same has not been done for alternative scaffolds. The commonly used dock-and-mutate approach for binding proteins, including antibodies, is limited because it uses a constant sequence and structure representation of the scaffold. Docking fixed-backbone scaffolds with a varied group of surface amino acids increases the chances of identifying superior starting poses that can be improved with subsequent mutations. In this work, we have developed MutDock, a novel computational approach that simultaneously docks and mutates fixed backbone scaffolds for binding a target epitope by identifying a minimum number of hydrogen bonds. The approach is broadly divided into two steps. The first step uses pairwise distance alignment of hydrogen bond-forming areas of scaffold residues and compatible epitope atoms. This step considers both native and mutated rotamers of scaffold residues. The second step mutates clashing variable interface residues and thermodynamically unfavorable residues to create additional strong interactions. MutDock was used to dock two scaffolds, namely, Affibodies and DARPins, with ten randomly selected antigens. The energies of the docked poses were minimized and binding energies were compared with docked poses from ZDOCK and HADDOCK. The top MutDock poses consisted of higher and comparable binding energies than the top ZDOCK and HADDOCK poses, respectively. This work contributes to the discovery of novel binders based on smaller-sized, fixed-backbone protein scaffolds.
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5
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Marchand A, Van Hall-Beauvais AK, Correia BE. Computational design of novel protein–protein interactions – An overview on methodological approaches and applications. Curr Opin Struct Biol 2022; 74:102370. [DOI: 10.1016/j.sbi.2022.102370] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/21/2022] [Accepted: 02/28/2022] [Indexed: 11/27/2022]
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6
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Abstract
Proteins have shown promise as therapeutics and diagnostics, but their effectiveness is limited by our inability to spatially target their activity. To overcome this limitation, we developed a computationally guided method to design inactive proenzymes or zymogens, which are activated through cleavage by a protease. Since proteases are differentially expressed in various tissues and disease states, including cancer, these proenzymes could be targeted to the desired microenvironment. We tested our method on the therapeutically relevant protein carboxypeptidase G2 (CPG2). We designed Pro-CPG2s that are inhibited by 80 to 98% and are partially to fully reactivatable following protease treatment. The developed methodology, with further refinements, could pave the way for routinely designing protease-activated protein-based therapeutics and diagnostics that act in a spatially controlled manner. Confining the activity of a designed protein to a specific microenvironment would have broad-ranging applications, such as enabling cell type-specific therapeutic action by enzymes while avoiding off-target effects. While many natural enzymes are synthesized as inactive zymogens that can be activated by proteolysis, it has been challenging to redesign any chosen enzyme to be similarly stimulus responsive. Here, we develop a massively parallel computational design, screening, and next-generation sequencing-based approach for proenzyme design. For a model system, we employ carboxypeptidase G2 (CPG2), a clinically approved enzyme that has applications in both the treatment of cancer and controlling drug toxicity. Detailed kinetic characterization of the most effectively designed variants shows that they are inhibited by ∼80% compared to the unmodified protein, and their activity is fully restored following incubation with site-specific proteases. Introducing disulfide bonds between the pro- and catalytic domains based on the design models increases the degree of inhibition to 98% but decreases the degree of restoration of activity by proteolysis. A selected disulfide-containing proenzyme exhibits significantly lower activity relative to the fully activated enzyme when evaluated in cell culture. Structural and thermodynamic characterization provides detailed insights into the prodomain binding and inhibition mechanisms. The described methodology is general and could enable the design of a variety of proproteins with precise spatial regulation.
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7
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Xu L, Zhang H, Wang Y, Lu X, Zhao Z, Ma C, Yang S, Yarov‐Yarovoy V, Tian Y, Zheng J, Yang F. De Novo Design of Peptidic Positive Allosteric Modulators Targeting TRPV1 with Analgesic Effects. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2101716. [PMID: 34247451 PMCID: PMC8425881 DOI: 10.1002/advs.202101716] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/30/2021] [Indexed: 05/03/2023]
Abstract
Transient receptor potential vanilloid 1 (TRPV1) ion channel is a nociceptor critically involved in pain sensation. Direct blockade of TRPV1 exhibits significant analgesic effects but also incurs severe side effects such as hyperthermia, causing failures of TRPV1 inhibitors in clinical trials. In order to selectively target TRPV1 channels that are actively involved in pain-sensing, peptidic positive allosteric modulators (PAMs) based on the high-resolution structure of the TRPV1 intracellular ankyrin-repeat like domain are de novo designed. The hotspot centric approach is optimized for protein design; its usage in Rosetta increases the success rate in protein binder design. It is demonstrated experimentally, with a combination of fluorescence resonance energy transfer (FRET) imaging, surface plasmon resonance, and patch-clamp recording, that the designed PAMs bind to TRPV1 with nanomolar affinity and allosterically enhance its response to ligand activation as it is designed. It is further demonstrated that the designed PAM exhibits long-lasting in vivo analgesic effects in rats without changing their body temperature, suggesting that they have potentials for developing into novel analgesics.
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Affiliation(s)
- Lizhen Xu
- Kidney Disease CenterFirst Affiliated Hospital and Department of BiophysicsZhejiang University School of MedicineHangzhouZhejiang310058China
- Alibaba‐Zhejiang University Joint Research Center of Future Digital HealthcareHangzhouZhejiang310058China
- MOE Frontier Science Center for Brain Science and Brain‐Machine IntegrationZhejiang UniversityHangzhouZhejiang310027China
| | - Heng Zhang
- Kidney Disease CenterFirst Affiliated Hospital and Department of BiophysicsZhejiang University School of MedicineHangzhouZhejiang310058China
- Alibaba‐Zhejiang University Joint Research Center of Future Digital HealthcareHangzhouZhejiang310058China
- MOE Frontier Science Center for Brain Science and Brain‐Machine IntegrationZhejiang UniversityHangzhouZhejiang310027China
| | - Yunfei Wang
- College of Wildlife and Protected AreaNortheast Forestry UniversityHarbin150040China
| | - Xiancui Lu
- College of Wildlife and Protected AreaNortheast Forestry UniversityHarbin150040China
| | - Zhenye Zhao
- Kidney Disease CenterFirst Affiliated Hospital and Department of BiophysicsZhejiang University School of MedicineHangzhouZhejiang310058China
| | - Cheng Ma
- Protein facilitySchool of MedicineZhejiang UniversityHangzhouZhejiang310027China
| | - Shilong Yang
- College of Wildlife and Protected AreaNortheast Forestry UniversityHarbin150040China
| | - Vladimir Yarov‐Yarovoy
- Department of Physiology and Membrane BiologyUniversity of CaliforniaDavisSchool of MedicineDavisCA95616USA
| | - Yuhua Tian
- Qingdao University School of PharmacyQingdaoShandong266101China
| | - Jie Zheng
- Department of Physiology and Membrane BiologyUniversity of CaliforniaDavisSchool of MedicineDavisCA95616USA
| | - Fan Yang
- Kidney Disease CenterFirst Affiliated Hospital and Department of BiophysicsZhejiang University School of MedicineHangzhouZhejiang310058China
- Alibaba‐Zhejiang University Joint Research Center of Future Digital HealthcareHangzhouZhejiang310058China
- MOE Frontier Science Center for Brain Science and Brain‐Machine IntegrationZhejiang UniversityHangzhouZhejiang310027China
- Department of Physiology and Membrane BiologyUniversity of CaliforniaDavisSchool of MedicineDavisCA95616USA
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8
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Schoeder C, Schmitz S, Adolf-Bryfogle J, Sevy AM, Finn JA, Sauer MF, Bozhanova NG, Mueller BK, Sangha AK, Bonet J, Sheehan JH, Kuenze G, Marlow B, Smith ST, Woods H, Bender BJ, Martina CE, del Alamo D, Kodali P, Gulsevin A, Schief WR, Correia BE, Crowe JE, Meiler J, Moretti R. Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design. Biochemistry 2021; 60:825-846. [PMID: 33705117 PMCID: PMC7992133 DOI: 10.1021/acs.biochem.0c00912] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 03/02/2021] [Indexed: 01/16/2023]
Abstract
Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.
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Affiliation(s)
- Clara
T. Schoeder
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Samuel Schmitz
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Jared Adolf-Bryfogle
- Department
of Immunology and Microbiology, The Scripps
Research Institute, La Jolla, California 92037, United States
- IAVI
Neutralizing Antibody Center, The Scripps
Research Institute, La Jolla, California 92037, United States
| | - Alexander M. Sevy
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
| | - Jessica A. Finn
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
- Department
of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Marion F. Sauer
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
| | - Nina G. Bozhanova
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Benjamin K. Mueller
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Amandeep K. Sangha
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Jaume Bonet
- Institute
of Bioengineering, École Polytechnique
Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Jonathan H. Sheehan
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Georg Kuenze
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Institute
for Drug Discovery, University Leipzig Medical
School, 04103 Leipzig, Germany
| | - Brennica Marlow
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Shannon T. Smith
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Hope Woods
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Brian J. Bender
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Cristina E. Martina
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Diego del Alamo
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Pranav Kodali
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Alican Gulsevin
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - William R. Schief
- Department
of Immunology and Microbiology, The Scripps
Research Institute, La Jolla, California 92037, United States
- IAVI
Neutralizing Antibody Center, The Scripps
Research Institute, La Jolla, California 92037, United States
| | - Bruno E. Correia
- Institute
of Bioengineering, École Polytechnique
Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - James E. Crowe
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
- Department
of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
- Department
of Pediatrics, Vanderbilt University Medical
Center, Nashville, Tennessee 37232, United States
| | - Jens Meiler
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Institute
for Drug Discovery, University Leipzig Medical
School, 04103 Leipzig, Germany
| | - Rocco Moretti
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
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9
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Bonadio A, Shifman JM. Computational design and experimental optimization of protein binders with prospects for biomedical applications. Protein Eng Des Sel 2021; 34:gzab020. [PMID: 34436606 PMCID: PMC8388154 DOI: 10.1093/protein/gzab020] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 07/11/2021] [Accepted: 07/11/2021] [Indexed: 11/12/2022] Open
Abstract
Protein-based binders have become increasingly more attractive candidates for drug and imaging agent development. Such binders could be evolved from a number of different scaffolds, including antibodies, natural protein effectors and unrelated small protein domains of different geometries. While both computational and experimental approaches could be utilized for protein binder engineering, in this review we focus on various computational approaches for protein binder design and demonstrate how experimental selection could be applied to subsequently optimize computationally-designed molecules. Recent studies report a number of designed protein binders with pM affinities and high specificities for their targets. These binders usually characterized with high stability, solubility, and low production cost. Such attractive molecules are bound to become more common in various biotechnological and biomedical applications in the near future.
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Affiliation(s)
- Alessandro Bonadio
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Julia M Shifman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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10
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Kundert K, Kortemme T. Computational design of structured loops for new protein functions. Biol Chem 2019; 400:275-288. [PMID: 30676995 PMCID: PMC6530579 DOI: 10.1515/hsz-2018-0348] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 12/18/2018] [Indexed: 12/20/2022]
Abstract
The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.
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Affiliation(s)
- Kale Kundert
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Tanja Kortemme
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158, USA
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11
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Adolf-Bryfogle J, Kalyuzhniy O, Kubitz M, Weitzner BD, Hu X, Adachi Y, Schief WR, Dunbrack RL. RosettaAntibodyDesign (RAbD): A general framework for computational antibody design. PLoS Comput Biol 2018; 14:e1006112. [PMID: 29702641 PMCID: PMC5942852 DOI: 10.1371/journal.pcbi.1006112] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 05/09/2018] [Accepted: 04/02/2018] [Indexed: 01/12/2023] Open
Abstract
A structural-bioinformatics-based computational methodology and framework have been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from a widely accepted set of the canonical clusters of CDRs (North et al., J. Mol. Biol., 406:228-256, 2011). It then performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. Starting from an existing experimental or computationally modeled antigen-antibody structure, RAbD can be used to redesign a single CDR or multiple CDRs with loops of different length, conformation, and sequence. We rigorously benchmarked RAbD on a set of 60 diverse antibody-antigen complexes, using two design strategies-optimizing total Rosetta energy and optimizing interface energy alone. We utilized two novel metrics for measuring success in computational protein design. The design risk ratio (DRR) is equal to the frequency of recovery of native CDR lengths and clusters divided by the frequency of sampling of those features during the Monte Carlo design procedure. Ratios greater than 1.0 indicate that the design process is picking out the native more frequently than expected from their sampled rate. We achieved DRRs for the non-H3 CDRs of between 2.4 and 4.0. The antigen risk ratio (ARR) is the ratio of frequencies of the native amino acid types, CDR lengths, and clusters in the output decoys for simulations performed in the presence and absence of the antigen. For CDRs, we achieved cluster ARRs as high as 2.5 for L1 and 1.5 for H2. For sequence design simulations without CDR grafting, the overall recovery for the native amino acid types for residues that contact the antigen in the native structures was 72% in simulations performed in the presence of the antigen and 48% in simulations performed without the antigen, for an ARR of 1.5. For the non-contacting residues, the ARR was 1.08. This shows that the sequence profiles are able to maintain the amino acid types of these conserved, buried sites, while recovery of the exposed, contacting residues requires the presence of the antigen-antibody interface. We tested RAbD experimentally on both a lambda and kappa antibody-antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.
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Affiliation(s)
- Jared Adolf-Bryfogle
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, United States of America
- Program in Molecular and Cell Biology and Genetics, Drexel University College of Medicine, Philadelphia, PA, United States of America
- The Scripps Research Institute, La Jolla, CA, United States of America
| | - Oleks Kalyuzhniy
- The Scripps Research Institute, La Jolla, CA, United States of America
- IAVI Neutralizing Antibody Center at TSRI, La Jolla, CA, United States of America
| | - Michael Kubitz
- The Scripps Research Institute, La Jolla, CA, United States of America
| | - Brian D. Weitzner
- Department of Biochemistry, University of Washington, Seattle, WA, United States of America
- Institute for Protein Design, University of Washington, Seattle, WA, United States of America
| | - Xiaozhen Hu
- The Scripps Research Institute, La Jolla, CA, United States of America
| | - Yumiko Adachi
- IAVI Neutralizing Antibody Center at TSRI, La Jolla, CA, United States of America
| | - William R. Schief
- The Scripps Research Institute, La Jolla, CA, United States of America
- IAVI Neutralizing Antibody Center at TSRI, La Jolla, CA, United States of America
| | - Roland L. Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, United States of America
- * E-mail:
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Setiawan D, Brender J, Zhang Y. Recent advances in automated protein design and its future challenges. Expert Opin Drug Discov 2018; 13:587-604. [PMID: 29695210 DOI: 10.1080/17460441.2018.1465922] [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] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.
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Affiliation(s)
- Dani Setiawan
- a Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , MI , USA
| | - Jeffrey Brender
- b Radiation Biology Branch , Center for Cancer Research, National Cancer Institute - NIH , Bethesda , MD , USA
| | - Yang Zhang
- a Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , MI , USA.,c Department of Biological Chemistry , University of Michigan , Ann Arbor , MI , USA
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13
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Leong SW, Lim TS, Ismail A, Choong YS. Integration of molecular dynamics simulation and hotspot residues grafting for de novo scFv design against Salmonella Typhi TolC protein. J Mol Recognit 2017; 31:e2695. [PMID: 29230887 DOI: 10.1002/jmr.2695] [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: 08/02/2017] [Revised: 11/13/2017] [Accepted: 11/19/2017] [Indexed: 01/10/2023]
Abstract
With the development of de novo binders for protein targets from non-related scaffolds, many possibilities for therapeutics and diagnostics have been created. In this study, we described the use of de novo design approach to create single-chain fragment variable (scFv) for Salmonella enterica subspecies enterica serovar Typhi TolC protein. Typhoid fever is a global health concern in developing and underdeveloped countries. Rapid typhoid diagnostics will improve disease management and therapy. In this work, molecular dynamics simulation was first performed on a homology model of TolC protein in POPE membrane bilayer to obtain the central structure that was subsequently used as the target for scFv design. Potential hotspot residues capable of anchoring the binders to the target were identified by docking "disembodied" amino acid residues against TolC surface. Next, scFv scaffolds were selected from Protein Data Bank to harbor the computed hotspot residues. The hotspot residues were then incorporated into the scFv scaffold complementarity determining regions. The designs recapitulated binding energy, shape complementarity, and interface surface area of natural protein-antibody interfaces. This approach has yielded 5 designs with high binding affinity against TolC that may be beneficial for the future development of antigen-based detection agents for typhoid diagnostics.
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Affiliation(s)
- Siew Wen Leong
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Theam Soon Lim
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Asma Ismail
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Health Campus, Kubang Kerian, Kelantan, Malaysia
| | - Yee Siew Choong
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Minden, Penang, Malaysia
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14
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The design of target specific antibodies (scFv) by applying de novo workflow: Case study on BmR1 antigen from Brugia malayi. J Mol Graph Model 2017; 76:543-550. [DOI: 10.1016/j.jmgm.2017.07.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 06/02/2017] [Accepted: 07/05/2017] [Indexed: 11/24/2022]
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15
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Liu X, Taylor RD, Griffin L, Coker SF, Adams R, Ceska T, Shi J, Lawson ADG, Baker T. Computational design of an epitope-specific Keap1 binding antibody using hotspot residues grafting and CDR loop swapping. Sci Rep 2017; 7:41306. [PMID: 28128368 PMCID: PMC5269676 DOI: 10.1038/srep41306] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 12/19/2016] [Indexed: 12/20/2022] Open
Abstract
Therapeutic and diagnostic applications of monoclonal antibodies often require careful selection of binders that recognize specific epitopes on the target molecule to exert a desired modulation of biological function. Here we present a proof-of-concept application for the rational design of an epitope-specific antibody binding with the target protein Keap1, by grafting pre-defined structural interaction patterns from the native binding partner protein, Nrf2, onto geometrically matched positions of a set of antibody scaffolds. The designed antibodies bind to Keap1 and block the Keap1-Nrf2 interaction in an epitope-specific way. One resulting antibody is further optimised to achieve low-nanomolar binding affinity by in silico redesign of the CDRH3 sequences. An X-ray co-crystal structure of one resulting design reveals that the actual binding orientation and interface with Keap1 is very close to the design model, despite an unexpected CDRH3 tilt and VH/VL interface deviation, which indicates that the modelling precision may be improved by taking into account simultaneous CDR loops conformation and VH/VL orientation optimisation upon antibody sequence change. Our study confirms that, given a pre-existing crystal structure of the target protein-protein interaction, hotspots grafting with CDR loop swapping is an attractive route to the rational design of an antibody targeting a pre-selected epitope.
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Affiliation(s)
- Xiaofeng Liu
- UCB Celltech, 216 Bath Road, Slough, United Kingdom
| | | | | | | | - Ralph Adams
- UCB Celltech, 216 Bath Road, Slough, United Kingdom
| | - Tom Ceska
- UCB Celltech, 216 Bath Road, Slough, United Kingdom
| | - Jiye Shi
- UCB Pharma, Chemin du Foriest 1, B-1420 Braine-l'Alleud, Belgium
| | | | - Terry Baker
- UCB Celltech, 216 Bath Road, Slough, United Kingdom
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16
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Fraser NJ, Liu JW, Mabbitt PD, Correy GJ, Coppin CW, Lethier M, Perugini MA, Murphy JM, Oakeshott JG, Weik M, Jackson CJ. Evolution of Protein Quaternary Structure in Response to Selective Pressure for Increased Thermostability. J Mol Biol 2016; 428:2359-2371. [DOI: 10.1016/j.jmb.2016.03.014] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 03/07/2016] [Accepted: 03/16/2016] [Indexed: 10/22/2022]
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17
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Two common structural motifs for TCR recognition by staphylococcal enterotoxins. Sci Rep 2016; 6:25796. [PMID: 27180909 PMCID: PMC4867771 DOI: 10.1038/srep25796] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 04/18/2016] [Indexed: 12/29/2022] Open
Abstract
Superantigens are toxins produced by Staphylococcus aureus, called staphylococcal enterotoxins (abbreviated SEA to SEU). They can cross-link the T cell receptor (TCR) and major histocompatibility complex class II, triggering a massive T cell activation and hence disease. Due to high stability and toxicity, superantigens are potential agents of bioterrorism. Hence, antagonists may not only be useful in the treatment of disease but also serve as countermeasures to biological warfare. Of particular interest are inhibitors against SEA and SEB. SEA is the main cause of food poisoning, while SEB is a common toxin manufactured as a biological weapon. Here, we present the crystal structures of SEA in complex with TCR and SEE in complex with the same TCR, complemented with computational alanine-scanning mutagenesis of SEA, SEB, SEC3, SEE, and SEH. We have identified two common areas that contribute to the general TCR binding for these superantigens. This paves the way for design of single antagonists directed towards multiple toxins.
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18
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Lapidoth GD, Baran D, Pszolla GM, Norn C, Alon A, Tyka MD, Fleishman SJ. AbDesign: An algorithm for combinatorial backbone design guided by natural conformations and sequences. Proteins 2015; 83:1385-406. [PMID: 25670500 PMCID: PMC4881815 DOI: 10.1002/prot.24779] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Revised: 01/13/2015] [Accepted: 01/26/2015] [Indexed: 12/20/2022]
Abstract
Computational design of protein function has made substantial progress, generating new enzymes, binders, inhibitors, and nanomaterials not previously seen in nature. However, the ability to design new protein backbones for function--essential to exert control over all polypeptide degrees of freedom--remains a critical challenge. Most previous attempts to design new backbones computed the mainchain from scratch. Here, instead, we describe a combinatorial backbone and sequence optimization algorithm called AbDesign, which leverages the large number of sequences and experimentally determined molecular structures of antibodies to construct new antibody models, dock them against target surfaces and optimize their sequence and backbone conformation for high stability and binding affinity. We used the algorithm to produce antibody designs that target the same molecular surfaces as nine natural, high-affinity antibodies; in five cases interface sequence identity is above 30%, and in four of those the backbone conformation at the core of the antibody binding surface is within 1 Å root-mean square deviation from the natural antibodies. Designs recapitulate polar interaction networks observed in natural complexes, and amino acid sidechain rigidity at the designed binding surface, which is likely important for affinity and specificity, is high compared to previous design studies. In designed anti-lysozyme antibodies, complementarity-determining regions (CDRs) at the periphery of the interface, such as L1 and H2, show greater backbone conformation diversity than the CDRs at the core of the interface, and increase the binding surface area compared to the natural antibody, potentially enhancing affinity and specificity.
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Affiliation(s)
- Gideon D. Lapidoth
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Dror Baran
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Gabriele M. Pszolla
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Christoffer Norn
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Assaf Alon
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Michael D. Tyka
- Google Inc., 1600 Amphitheatre Pkwy, Mountain View, CA 94043
| | - Sarel J. Fleishman
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
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19
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Moal IH, Dapkūnas J, Fernández-Recio J. Inferring the microscopic surface energy of protein-protein interfaces from mutation data. Proteins 2015; 83:640-50. [PMID: 25586563 DOI: 10.1002/prot.24761] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/04/2014] [Accepted: 12/21/2014] [Indexed: 11/11/2022]
Abstract
Mutations at protein-protein recognition sites alter binding strength by altering the chemical nature of the interacting surfaces. We present a simple surface energy model, parameterized with empirical ΔΔG values, yielding mean energies of -48 cal mol(-1) Å(-2) for interactions between hydrophobic surfaces, -51 to -80 cal mol(-1) Å(-2) for surfaces of complementary charge, and 66-83 cal mol(-1) Å(-2) for electrostatically repelling surfaces, relative to the aqueous phase. This places the mean energy of hydrophobic surface burial at -24 cal mol(-1) Å(-2) . Despite neglecting configurational entropy and intramolecular changes, the model correlates with empirical binding free energies of a functionally diverse set of rigid-body interactions (r = 0.66). When used to rerank docking poses, it can place near-native solutions in the top 10 for 37% of the complexes evaluated, and 82% in the top 100. The method shows that hydrophobic burial is the driving force for protein association, accounting for 50-95% of the cohesive energy. The model is available open-source from http://life.bsc.es/pid/web/surface_energy/ and via the CCharpPPI web server http://life.bsc.es/pid/ccharppi/.
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Affiliation(s)
- Iain H Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, Barcelona, 08034, Spain
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20
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Abstract
Efforts to generate biologically active proteins by de novo computational design have been limited to creating functional sites within pre-existing scaffolds. Procko et al. use an innovative computational design approach coupled with in-vitro-targeted evolution to produce a potent polypeptide inhibitor of a viral Bcl-2-like protein. This novel inhibitor triggers apoptosis of virus-infected cells.
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Affiliation(s)
- Wayne J Fairbrother
- Department of Early Discovery Biochemistry, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA.
| | - Avi Ashkenazi
- Cancer Immunology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA.
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21
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Sudha G, Nussinov R, Srinivasan N. An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 116:141-50. [PMID: 25077409 DOI: 10.1016/j.pbiomolbio.2014.07.004] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Accepted: 07/13/2014] [Indexed: 12/20/2022]
Abstract
Rich data bearing on the structural and evolutionary principles of protein-protein interactions are paving the way to a better understanding of the regulation of function in the cell. This is particularly the case when these interactions are considered in the framework of key pathways. Knowledge of the interactions may provide insights into the mechanisms of crucial 'driver' mutations in oncogenesis. They also provide the foundation toward the design of protein-protein interfaces and inhibitors that can abrogate their formation or enhance them. The main features to learn from known 3-D structures of protein-protein complexes and the extensive literature which analyzes them computationally and experimentally include the interaction details which permit undertaking structure-based drug discovery, the evolution of complexes and their interactions, the consequences of alterations such as post-translational modifications, ligand binding, disease causing mutations, host pathogen interactions, oligomerization, aggregation and the roles of disorder, dynamics, allostery and more to the protein and the cell. This review highlights some of the recent advances in these areas, including design, inhibition and prediction of protein-protein complexes. The field is broad, and much work has been carried out in these areas, making it challenging to cover it in its entirety. Much of this is due to the fast increase in the number of molecules whose structures have been determined experimentally and the vast increase in computational power. Here we provide a concise overview.
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Affiliation(s)
- Govindarajan Sudha
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India.
| | - Ruth Nussinov
- Cancer and Inflammation Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., National Cancer Institute, Frederick, MD 21702, USA; Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
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22
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An accurate binding interaction model in de novo computational protein design of interactions: If you build it, they will bind. J Struct Biol 2014; 185:136-46. [DOI: 10.1016/j.jsb.2013.03.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2012] [Revised: 03/15/2013] [Accepted: 03/21/2013] [Indexed: 01/07/2023]
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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 naïve 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.
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24
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Computational design of protein–protein interactions. Curr Opin Struct Biol 2013; 23:903-10. [DOI: 10.1016/j.sbi.2013.08.003] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2013] [Revised: 08/06/2013] [Accepted: 08/08/2013] [Indexed: 11/23/2022]
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25
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London N, Raveh B, Schueler-Furman O. Peptide docking and structure-based characterization of peptide binding: from knowledge to know-how. Curr Opin Struct Biol 2013; 23:894-902. [PMID: 24138780 DOI: 10.1016/j.sbi.2013.07.006] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2013] [Revised: 07/04/2013] [Accepted: 07/08/2013] [Indexed: 11/25/2022]
Abstract
Peptide-mediated interactions are gaining increased attention due to their predominant roles in the many regulatory processes that involve dynamic interactions between proteins. The structures of such interactions provide an excellent starting point for their characterization and manipulation, and can provide leads for targeted inhibitor design. The relatively few experimentally determined structures of peptide-protein complexes can be complemented with an outburst of modeling approaches that have been introduced in recent years, with increasing accuracy and applicability to ever more systems. We review different methods to address the considerable challenges in modeling the binding of a short yet highly flexible peptide to its partner. These methods apply an array of sampling strategies and draw from a recent amassing of knowledge about the biophysical nature of peptide-protein interactions. We elaborate on applications of these structure-based approaches and in particular on the characterization of peptide binding specificity to different peptide-binding domains and enzymes. Such applications can identify new biological targets and thus complement our current view of protein-protein interactions in living organisms. Accurate peptide-protein docking is of particular importance in the light of increased appreciation of the crucial functional roles of disordered regions and the many linear binding motifs embedded within.
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Affiliation(s)
- Nir London
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
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26
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Agius R, Torchala M, Moal IH, Fernández-Recio J, Bates PA. Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organization. PLoS Comput Biol 2013; 9:e1003216. [PMID: 24039569 PMCID: PMC3764008 DOI: 10.1371/journal.pcbi.1003216] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2013] [Accepted: 07/25/2013] [Indexed: 12/21/2022] Open
Abstract
Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects.
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Affiliation(s)
- Rudi Agius
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
| | - Mieczyslaw Torchala
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
| | - Iain H. Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, Barcelona, Spain
| | - Juan Fernández-Recio
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, Barcelona, Spain
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
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27
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Der BS, Kuhlman B. Strategies to control the binding mode of de novo designed protein interactions. Curr Opin Struct Biol 2013; 23:639-46. [PMID: 23731800 PMCID: PMC3737258 DOI: 10.1016/j.sbi.2013.04.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2013] [Accepted: 04/19/2013] [Indexed: 12/14/2022]
Abstract
There has been significant recent progress in the computational design of protein interactions including the creation of novel heterodimers, homodimers, nanohedra, fibril caps and a protein crystal. Essential to these successes has been the use of innovative strategies for finding binding modes that are achievable, that is, identifying binding partners and docked conformations that can be successfully stabilized via sequence optimization and backbone refinement. In many cases this has involved the use of structural motifs commonly found at naturally occurring interfaces including alpha helices inserted into hydrophobic grooves, beta-strand pairing, metal binding, established helix packing motifs, and the use of symmetry to form cooperative interactions. Future challenges include the creation of hydrogen bond networks and antibody-like interactions based on the redesign of protein surface loops.
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Affiliation(s)
- Bryan S. Der
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
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28
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Moal IH, Moretti R, Baker D, Fernández-Recio J. Scoring functions for protein-protein interactions. Curr Opin Struct Biol 2013; 23:862-7. [PMID: 23871100 DOI: 10.1016/j.sbi.2013.06.017] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2013] [Revised: 06/26/2013] [Accepted: 06/29/2013] [Indexed: 12/24/2022]
Abstract
The computational evaluation of protein-protein interactions will play an important role in organising the wealth of data being generated by high-throughput initiatives. Here we discuss future applications, report recent developments and identify areas requiring further investigation. Many functions have been developed to quantify the structural and energetic properties of interacting proteins, finding use in interrelated challenges revolving around the relationship between sequence, structure and binding free energy. These include loop modelling, side-chain refinement, docking, multimer assembly, affinity prediction, affinity change upon mutation, hotspots location and interface design. Information derived from models optimised for one of these challenges can be used to benefit the others, and can be unified within the theoretical frameworks of multi-task learning and Pareto-optimal multi-objective learning.
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Affiliation(s)
- Iain H Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, C/ Jordi Girona 29, 08034 Barcelona, Spain
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Procko E, Hedman R, Hamilton K, Seetharaman J, Fleishman SJ, Su M, Aramini J, Kornhaber G, Hunt JF, Tong L, Montelione GT, Baker D. Computational design of a protein-based enzyme inhibitor. J Mol Biol 2013; 425:3563-75. [PMID: 23827138 DOI: 10.1016/j.jmb.2013.06.035] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2013] [Revised: 06/15/2013] [Accepted: 06/25/2013] [Indexed: 01/30/2023]
Abstract
While there has been considerable progress in designing protein-protein interactions, the design of proteins that bind polar surfaces is an unmet challenge. We describe the computational design of a protein that binds the acidic active site of hen egg lysozyme and inhibits the enzyme. The design process starts with two polar amino acids that fit deep into the enzyme active site, identifies a protein scaffold that supports these residues and is complementary in shape to the lysozyme active-site region, and finally optimizes the surrounding contact surface for high-affinity binding. Following affinity maturation, a protein designed using this method bound lysozyme with low nanomolar affinity, and a combination of NMR studies, crystallography, and knockout mutagenesis confirmed the designed binding surface and orientation. Saturation mutagenesis with selection and deep sequencing demonstrated that specific designed interactions extending well beyond the centrally grafted polar residues are critical for high-affinity binding.
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Affiliation(s)
- Erik Procko
- Department of Biochemistry and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
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Der BS, Jha RK, Jha RK, Lewis SM, Thompson PM, Guntas G, Kuhlman B. Combined computational design of a zinc-binding site and a protein-protein interaction: one open zinc coordination site was not a robust hotspot for de novo ubiquitin binding. Proteins 2013; 81:1245-55. [PMID: 23504819 DOI: 10.1002/prot.24280] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2012] [Revised: 02/13/2013] [Accepted: 02/26/2013] [Indexed: 11/11/2022]
Abstract
We computationally designed a de novo protein-protein interaction between wild-type ubiquitin and a redesigned scaffold. Our strategy was to incorporate zinc at the designed interface to promote affinity and orientation specificity. A large set of monomeric scaffold surfaces were computationally engineered with three-residue zinc coordination sites, and the ubiquitin residue H68 was docked to the open coordination site to complete a tetrahedral zinc site. This single coordination bond was intended as a hotspot and polar interaction for ubiquitin binding, and surrounding residues on the scaffold were optimized primarily as hydrophobic residues using a rotamer-based sequence design protocol in Rosetta. From thousands of independent design simulations, four sequences were selected for experimental characterization. The best performing design, called Spelter, binds tightly to zinc (Kd < 10 nM) and binds ubiquitin with a Kd of 20 µM in the presence of zinc and 68 µM in the absence of zinc. Mutagenesis studies and nuclear magnetic resonance chemical shift perturbation experiments indicate that Spelter interacts with H68 and the target surface on ubiquitin; however, H68 does not form a hotspot as intended. Instead, mutation of H68 to alanine results in tighter binding. Although a 3/1 zinc coordination arrangement at an interface cannot be ruled out as a means to improve affinity, our study led us to conclude that 2/2 coordination arrangements or multiple-zinc designs are more likely to promote high-affinity protein interactions.
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Affiliation(s)
- Bryan S Der
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7260, USA
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31
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Whitehead TA, Baker D, Fleishman SJ. Computational Design of Novel Protein Binders and Experimental Affinity Maturation. Methods Enzymol 2013; 523:1-19. [DOI: 10.1016/b978-0-12-394292-0.00001-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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33
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Emerging themes in the computational design of novel enzymes and protein-protein interfaces. FEBS Lett 2012; 587:1147-54. [DOI: 10.1016/j.febslet.2012.12.009] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2012] [Revised: 12/10/2012] [Accepted: 12/10/2012] [Indexed: 11/21/2022]
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34
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Fleishman SJ, Baker D. Role of the biomolecular energy gap in protein design, structure, and evolution. Cell 2012; 149:262-73. [PMID: 22500796 DOI: 10.1016/j.cell.2012.03.016] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2011] [Indexed: 01/29/2023]
Abstract
The folding of natural biopolymers into unique three-dimensional structures that determine their function is remarkable considering the vast number of alternative states and requires a large gap in the energy of the functional state compared to the many alternatives. This Perspective explores the implications of this energy gap for computing the structures of naturally occurring biopolymers, designing proteins with new structures and functions, and optimally integrating experiment and computation in these endeavors. Possible parallels between the generation of functional molecules in computational design and natural evolution are highlighted.
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Affiliation(s)
- Sarel J Fleishman
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel.
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35
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King NP, Sheffler W, Sawaya MR, Vollmar BS, Sumida JP, André I, Gonen T, Yeates TO, Baker D. Computational design of self-assembling protein nanomaterials with atomic level accuracy. Science 2012; 336:1171-4. [PMID: 22654060 PMCID: PMC4138882 DOI: 10.1126/science.1219364] [Citation(s) in RCA: 488] [Impact Index Per Article: 40.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
We describe a general computational method for designing proteins that self-assemble to a desired symmetric architecture. Protein building blocks are docked together symmetrically to identify complementary packing arrangements, and low-energy protein-protein interfaces are then designed between the building blocks in order to drive self-assembly. We used trimeric protein building blocks to design a 24-subunit, 13-nm diameter complex with octahedral symmetry and a 12-subunit, 11-nm diameter complex with tetrahedral symmetry. The designed proteins assembled to the desired oligomeric states in solution, and the crystal structures of the complexes revealed that the resulting materials closely match the design models. The method can be used to design a wide variety of self-assembling protein nanomaterials.
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Affiliation(s)
- Neil P. King
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - William Sheffler
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Michael R. Sawaya
- Howard Hughes Medical Institute, UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA
| | - Breanna S. Vollmar
- Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - John P. Sumida
- Department of Medicinal Chemistry, University of Washington, Seattle, WA 98177, USA
| | - Ingemar André
- Department of Biochemistry and Structural Biology, Lund University, Lund, Sweden
| | - Tamir Gonen
- Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Todd O. Yeates
- UCLA Department of Chemistry and Biochemistry, Los Angeles, CA 90095, USA,UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA,Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA,To whom correspondence should be addressed.
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