1
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Wells NGM, Smith CA. Predicting binding affinity changes from long-distance mutations using molecular dynamics simulations and Rosetta. Proteins 2023. [PMID: 36757060 DOI: 10.1002/prot.26477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/20/2023] [Accepted: 02/07/2023] [Indexed: 02/10/2023]
Abstract
Computationally modeling how mutations affect protein-protein binding not only helps uncover the biophysics of protein interfaces, but also enables the redesign and optimization of protein interactions. Traditional high-throughput methods for estimating binding free energy changes are currently limited to mutations directly at the interface due to difficulties in accurately modeling how long-distance mutations propagate their effects through the protein structure. However, the modeling and design of such mutations is of substantial interest as it allows for greater control and flexibility in protein design applications. We have developed a method that combines high-throughput Rosetta-based side-chain optimization with conformational sampling using classical molecular dynamics simulations, finding significant improvements in our ability to accurately predict long-distance mutational perturbations to protein binding. Our approach uses an analytical framework grounded in alchemical free energy calculations while enabling exploration of a vastly larger sequence space. When comparing to experimental data, we find that our method can predict internal long-distance mutational perturbations with a level of accuracy similar to that of traditional methods in predicting the effects of mutations at the protein-protein interface. This work represents a new and generalizable approach to optimize protein free energy landscapes for desired biological functions.
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Affiliation(s)
- Nicholas G M Wells
- Department of Chemistry, Wesleyan University, Middletown, Connecticut, USA
| | - Colin A Smith
- Department of Chemistry, Wesleyan University, Middletown, Connecticut, USA
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2
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Legault S, Fraser-Halberg DP, McAnelly RL, Eason MG, Thompson MC, Chica RA. Generation of bright monomeric red fluorescent proteins via computational design of enhanced chromophore packing. Chem Sci 2022; 13:1408-1418. [PMID: 35222925 PMCID: PMC8809391 DOI: 10.1039/d1sc05088e] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/07/2022] [Indexed: 12/11/2022] Open
Abstract
Red fluorescent proteins (RFPs) have found widespread application in chemical and biological research due to their longer emission wavelengths. Here, we use computational protein design to increase the quantum yield and thereby brightness of a dim monomeric RFP (mRojoA, quantum yield = 0.02) by optimizing chromophore packing with aliphatic residues, which we hypothesized would reduce torsional motions causing non-radiative decay. Experimental characterization of the top 10 designed sequences yielded mSandy1 (λ em = 609 nm, quantum yield = 0.26), a variant with equivalent brightness to mCherry, a widely used RFP. We next used directed evolution to further increase brightness, resulting in mSandy2 (λ em = 606 nm, quantum yield = 0.35), the brightest Discosoma sp. derived monomeric RFP with an emission maximum above 600 nm reported to date. Crystallographic analysis of mSandy2 showed that the chromophore p-hydroxybenzylidene moiety is sandwiched between the side chains of Leu63 and Ile197, a structural motif that has not previously been observed in RFPs, and confirms that aliphatic packing leads to chromophore rigidification. Our results demonstrate that computational protein design can be used to generate bright monomeric RFPs, which can serve as templates for the evolution of novel far-red fluorescent proteins.
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Affiliation(s)
- Sandrine Legault
- Department of Chemistry and Biomolecular Sciences, University of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| | - Derek P Fraser-Halberg
- Department of Chemistry and Biomolecular Sciences, University of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| | - Ralph L McAnelly
- Department of Chemistry and Biochemistry, University of California, Merced Merced California 95343 USA
| | - Matthew G Eason
- Department of Chemistry and Biomolecular Sciences, University of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| | - Michael C Thompson
- Department of Chemistry and Biochemistry, University of California, Merced Merced California 95343 USA
| | - Roberto A Chica
- Department of Chemistry and Biomolecular Sciences, University of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
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3
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Liang T, Chen H, Yuan J, Jiang C, Hao Y, Wang Y, Feng Z, Xie XQ. IsAb: a computational protocol for antibody design. Brief Bioinform 2021; 22:6238584. [PMID: 33876197 DOI: 10.1093/bib/bbab143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/24/2021] [Accepted: 03/23/2021] [Indexed: 12/15/2022] Open
Abstract
The design of therapeutic antibodies has attracted a large amount of attention over the years. Antibodies are widely used to treat many diseases due to their high efficiency and low risk of adverse events. However, the experimental methods of antibody design are time-consuming and expensive. Although computational antibody design techniques have had significant advances in the past years, there are still some challenges that need to be solved, such as the flexibility of antigen structure, the lack of antibody structural data and the absence of standard antibody design protocol. In the present work, we elaborated on an in silico antibody design protocol for users to easily perform computer-aided antibody design. First, the Rosetta web server will be applied to generate the 3D structure of query antibodies if there is no structural information available. Then, two-step docking will be used to identify the binding pose of an antibody-antigen complex when the binding information is unknown. ClusPro is the first method to be used to conduct the global docking, and SnugDock is applied for the local docking. Sequentially, based on the predicted binding poses, in silico alanine scanning will be used to predict the potential hotspots (or key residues). Finally, computational affinity maturation protocol will be used to modify the structure of antibodies to theoretically increase their affinity and stability, which will be further validated by the bioassays in the future. As a proof of concept, we redesigned antibody D44.1 and compared it with previously reported data in order to validate IsAb protocol. To further illustrate our proposed protocol, we used cemiplimab antibody, a PD-1 checkpoint inhibitor, as an example to showcase a step-by-step tutorial.
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Affiliation(s)
- Tianjian Liang
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Hui Chen
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Jiayi Yuan
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Chen Jiang
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yixuan Hao
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yuanqiang Wang
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Pittsburgh, PA 15261, USA
| | - Zhiwei Feng
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Xiang-Qun Xie
- Computational Drug Abuse Research and Computational Chemogenomics Screening Center at the University of Pittsburgh, Pittsburgh, PA 15261, USA
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4
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Ensemble-based enzyme design can recapitulate the effects of laboratory directed evolution in silico. Nat Commun 2020; 11:4808. [PMID: 32968058 PMCID: PMC7511930 DOI: 10.1038/s41467-020-18619-x] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 08/25/2020] [Indexed: 01/30/2023] Open
Abstract
The creation of artificial enzymes is a key objective of computational protein design. Although de novo enzymes have been successfully designed, these exhibit low catalytic efficiencies, requiring directed evolution to improve activity. Here, we use room-temperature X-ray crystallography to study changes in the conformational ensemble during evolution of the designed Kemp eliminase HG3 (kcat/KM 146 M−1s−1). We observe that catalytic residues are increasingly rigidified, the active site becomes better pre-organized, and its entrance is widened. Based on these observations, we engineer HG4, an efficient biocatalyst (kcat/KM 103,000 M−1s−1) containing key first and second-shell mutations found during evolution. HG4 structures reveal that its active site is pre-organized and rigidified for efficient catalysis. Our results show how directed evolution circumvents challenges inherent to enzyme design by shifting conformational ensembles to favor catalytically-productive sub-states, and suggest improvements to the design methodology that incorporate ensemble modeling of crystallographic data. Kemp eliminases are artificial enzymes that catalyze the concerted deprotonation and ring-opening of benzisoxazoles. Here, the authors use room-temperature X-ray crystallography to investigate changes to the conformational ensemble of the Kemp eliminase HG3 along a directed evolutionary trajectory, and develop an experimentally guided, ensemble-based computational enzyme design procedure.
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5
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Glasgow AA, Huang YM, Mandell DJ, Thompson M, Ritterson R, Loshbaugh AL, Pellegrino J, Krivacic C, Pache RA, Barlow KA, Ollikainen N, Jeon D, Kelly MJS, Fraser JS, Kortemme T. Computational design of a modular protein sense-response system. Science 2020; 366:1024-1028. [PMID: 31754004 DOI: 10.1126/science.aax8780] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 10/07/2019] [Indexed: 12/28/2022]
Abstract
Sensing and responding to signals is a fundamental ability of living systems, but despite substantial progress in the computational design of new protein structures, there is no general approach for engineering arbitrary new protein sensors. Here, we describe a generalizable computational strategy for designing sensor-actuator proteins by building binding sites de novo into heterodimeric protein-protein interfaces and coupling ligand sensing to modular actuation through split reporters. Using this approach, we designed protein sensors that respond to farnesyl pyrophosphate, a metabolic intermediate in the production of valuable compounds. The sensors are functional in vitro and in cells, and the crystal structure of the engineered binding site closely matches the design model. Our computational design strategy opens broad avenues to link biological outputs to new signals.
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Affiliation(s)
- Anum A Glasgow
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Yao-Ming Huang
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel J Mandell
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Bioinformatics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Michael Thompson
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Ryan Ritterson
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Amanda L Loshbaugh
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Jenna Pellegrino
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Cody Krivacic
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,UC Berkeley-UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, CA, USA
| | - Roland A Pache
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Kyle A Barlow
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Bioinformatics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Noah Ollikainen
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Bioinformatics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Deborah Jeon
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Mark J S Kelly
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA. .,Bioinformatics Graduate Program, University of California San Francisco, San Francisco, CA, USA.,Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, USA.,UC Berkeley-UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
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6
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Chiu ML, Goulet DR, Teplyakov A, Gilliland GL. Antibody Structure and Function: The Basis for Engineering Therapeutics. Antibodies (Basel) 2019; 8:antib8040055. [PMID: 31816964 PMCID: PMC6963682 DOI: 10.3390/antib8040055] [Citation(s) in RCA: 229] [Impact Index Per Article: 45.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 11/25/2019] [Accepted: 11/28/2019] [Indexed: 12/11/2022] Open
Abstract
Antibodies and antibody-derived macromolecules have established themselves as the mainstay in protein-based therapeutic molecules (biologics). Our knowledge of the structure–function relationships of antibodies provides a platform for protein engineering that has been exploited to generate a wide range of biologics for a host of therapeutic indications. In this review, our basic understanding of the antibody structure is described along with how that knowledge has leveraged the engineering of antibody and antibody-related therapeutics having the appropriate antigen affinity, effector function, and biophysical properties. The platforms examined include the development of antibodies, antibody fragments, bispecific antibody, and antibody fusion products, whose efficacy and manufacturability can be improved via humanization, affinity modulation, and stability enhancement. We also review the design and selection of binding arms, and avidity modulation. Different strategies of preparing bispecific and multispecific molecules for an array of therapeutic applications are included.
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Affiliation(s)
- Mark L. Chiu
- Drug Product Development Science, Janssen Research & Development, LLC, Malvern, PA 19355, USA
- Correspondence:
| | - Dennis R. Goulet
- Department of Medicinal Chemistry, University of Washington, P.O. Box 357610, Seattle, WA 98195-7610, USA;
| | - Alexey Teplyakov
- Biologics Research, Janssen Research & Development, LLC, Spring House, PA 19477, USA; (A.T.); (G.L.G.)
| | - Gary L. Gilliland
- Biologics Research, Janssen Research & Development, LLC, Spring House, PA 19477, USA; (A.T.); (G.L.G.)
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7
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Loshbaugh AL, Kortemme T. Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions. Proteins 2019; 88:206-226. [PMID: 31344278 DOI: 10.1002/prot.25790] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 07/15/2019] [Accepted: 07/19/2019] [Indexed: 01/03/2023]
Abstract
Computational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible-backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible-backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.
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Affiliation(s)
- Amanda L Loshbaugh
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California.,Biophysics Graduate Program, University of California San Francisco, San Francisco, California
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California.,Biophysics Graduate Program, University of California San Francisco, San Francisco, California.,Quantitative Biosciences Institute, University of California San Francisco, San Francisco, California.,Chan Zuckerberg Biohub, San Francisco, California
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8
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Ludwiczak J, Jarmula A, Dunin-Horkawicz S. Combining Rosetta with molecular dynamics (MD): A benchmark of the MD-based ensemble protein design. J Struct Biol 2018; 203:54-61. [PMID: 29454111 DOI: 10.1016/j.jsb.2018.02.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2017] [Revised: 01/25/2018] [Accepted: 02/13/2018] [Indexed: 01/15/2023]
Abstract
Computational protein design is a set of procedures for computing amino acid sequences that will fold into a specified structure. Rosetta Design, a commonly used software for protein design, allows for the effective identification of sequences compatible with a given backbone structure, while molecular dynamics (MD) simulations can thoroughly sample near-native conformations. We benchmarked a procedure in which Rosetta design is started on MD-derived structural ensembles and showed that such a combined approach generates 20-30% more diverse sequences than currently available methods with only a slight increase in computation time. Importantly, the increase in diversity is achieved without a loss in the quality of the designed sequences assessed by their resemblance to natural sequences. We demonstrate that the MD-based procedure is also applicable to de novo design tasks started from backbone structures without any sequence information. In addition, we implemented a protocol that can be used to assess the stability of designed models and to select the best candidates for experimental validation. In sum our results demonstrate that the MD ensemble-based flexible backbone design can be a viable method for protein design, especially for tasks that require a large pool of diverse sequences.
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Affiliation(s)
- Jan Ludwiczak
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland; Laboratory of Bioinformatics, Nencki Institute of Experimental Biology, Pasteura 3, 02-093 Warsaw, Poland
| | - Adam Jarmula
- Laboratory of Bioinformatics, Nencki Institute of Experimental Biology, Pasteura 3, 02-093 Warsaw, Poland
| | - Stanislaw Dunin-Horkawicz
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland.
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9
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Abstract
The immune systems protect our bodies from foreign molecules or antigens, where antibodies play important roles. Antibodies evolve over time upon antigen encounter by somatically mutating their genome sequences. The end result is a series of antibodies that display higher affinities and specificities to specific antigens. This process is called affinity maturation. Recent improvements in computer hardware and modeling algorithms now enable the rational design of protein structures and functions, and several works on computer-aided antibody design have been published. In this chapter, we briefly describe computational methods for antibody affinity maturation, focusing on methods for sampling antibody conformations and for scoring designed antibody variants. We also discuss lessons learned from the successful computer-aided design of antibodies.
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Affiliation(s)
- Daisuke Kuroda
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
- Medical Proteomics Laboratory, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
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10
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Dekel Y, Machluf Y, Gefen T, Eidelshtein G, Kotlyar A, Bram Y, Shahar E, Reslane F, Aizenshtein E, Pitcovski J. Formation of multimeric antibodies for self-delivery of active monomers. Drug Deliv 2017; 24:199-208. [PMID: 28156181 PMCID: PMC8241139 DOI: 10.1080/10717544.2016.1242179] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 09/25/2016] [Indexed: 12/13/2022] Open
Abstract
Proteins and peptides have been used as drugs for almost a century. Technological advances in the past 30 years have enabled the production of pure, stable proteins in vast amounts. In contrast, administration of proteins based on their native active conformation (and thus necessitating the use of subcutaneous injections) has remained solely unchanged. The therapeutic anti-HER2 humanized monoclonal immunoglobulin (IgG) Trastuzumab (Herceptin) is a first line of the treatment for breast cancer. Chicken IgY is a commercially important polyclonal antibody (Ab). These Abs were examined for their ability to self-assemble and form ordered aggregates, by several biophysical methods. Atomic force microscopy analyses revealed the formation of multimeric nanostructures. The biological activity of multimeric IgG or IgY particles was retained and restored, in a dilution/time-dependent manner. IgG activity was confirmed by a binding assay using HER2 + human breast cancer cell line, SKBR3, while IgY activity was confirmed by ELISA assay using the VP2 antigen. Competition assay with native Herceptin antibodies demonstrated that the binding availability of the multimer formulation remained unaffected. Under long incubation periods, IgG multimers retained five times more activity than native IgG. In conclusion, the multimeric antibody formulations can serve as a storage depositories and sustained-release particles. These two important characteristics make this formulation promising for future novel administration protocols and altogether bring to light a different conceptual approach for the future use of therapeutic proteins as self-delivery entities rather than conjugated/encapsulated to other bio-compounds.
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Affiliation(s)
- Yaron Dekel
- Shamir Research Institute, University of Haifa, Kazrin, Israel
- Department of Clinical Laboratory, Zefat Academic College, Zefat, Israel
- Department of Life Sciences, Tel Hai College, Upper Galilee, Israel
| | - Yossy Machluf
- Consultant, specialist in the fields of biochemistry, molecular biology and genetics
| | - Tal Gefen
- Department of Life Sciences, Tel Hai College, Upper Galilee, Israel
- MIGAL – Galilee Technology Center, Kiryat Shmona, Israel
| | - Gennady Eidelshtein
- Department of Biochemistry and Molecular Biology, Tel Aviv University, Tel Aviv, Israel, and
| | - Alexander Kotlyar
- Department of Biochemistry and Molecular Biology, Tel Aviv University, Tel Aviv, Israel, and
| | - Yaron Bram
- Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
| | - Ehud Shahar
- Department of Life Sciences, Tel Hai College, Upper Galilee, Israel
- MIGAL – Galilee Technology Center, Kiryat Shmona, Israel
| | - Farah Reslane
- Department of Life Sciences, Tel Hai College, Upper Galilee, Israel
- MIGAL – Galilee Technology Center, Kiryat Shmona, Israel
| | - Elina Aizenshtein
- Department of Life Sciences, Tel Hai College, Upper Galilee, Israel
- MIGAL – Galilee Technology Center, Kiryat Shmona, Israel
| | - Jacob Pitcovski
- Department of Life Sciences, Tel Hai College, Upper Galilee, Israel
- MIGAL – Galilee Technology Center, Kiryat Shmona, Israel
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11
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Abstract
The ability of computational protein design (CPD) to identify protein sequences possessing desired characteristics in vast sequence spaces makes it a highly valuable tool in the protein engineering toolbox. CPD calculations are typically performed using a single-state design (SSD) approach in which amino-acid sequences are optimized on a single protein structure. Although SSD has been successfully applied to the design of numerous protein functions and folds, the approach can lead to the incorrect rejection of desirable sequences because of the combined use of a fixed protein backbone template and a set of rigid rotamers. This fixed backbone approximation can be addressed by using multistate design (MSD) with backbone ensembles. MSD improves the quality of predicted sequences by using ensembles approximating conformational flexibility as input templates instead of a single fixed protein structure. In this chapter, we present a step-by-step guide to the implementation and analysis of MSD calculations with backbone ensembles. Specifically, we describe ensemble generation with the PertMin protocol, execution of MSD calculations for recapitulation of Streptococcal protein G domain β1 mutant stability, and analysis of computational predictions by sequence binning. Furthermore, we provide a comparison between MSD and SSD calculation results and discuss the benefits of multistate approaches to CPD.
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12
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Druart K, Bigot J, Audit E, Simonson T. A Hybrid Monte Carlo Scheme for Multibackbone Protein Design. J Chem Theory Comput 2016; 12:6035-6048. [DOI: 10.1021/acs.jctc.6b00421] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Karen Druart
- Laboratoire
de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
- Maison
de la Simulation, CEA, CNRS, Univ. Paris-Sud, UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Julien Bigot
- Maison
de la Simulation, CEA, CNRS, Univ. Paris-Sud, UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Edouard Audit
- Maison
de la Simulation, CEA, CNRS, Univ. Paris-Sud, UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Thomas Simonson
- Laboratoire
de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
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13
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Kuroda D, Gray JJ. Pushing the Backbone in Protein-Protein Docking. Structure 2016; 24:1821-1829. [PMID: 27568930 DOI: 10.1016/j.str.2016.06.025] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Revised: 05/17/2016] [Accepted: 06/19/2016] [Indexed: 02/01/2023]
Abstract
Conformational changes of proteins that occur upon binding typically confound computational docking algorithms. In this study, we test computational methods to capture protein backbone conformational change related to binding. To address how well existing algorithms can sample bound-like backbones, we query seven techniques including Monte Carlo-based sampling, molecular dynamics, and normal mode analysis. All methods tested rarely sample near-bound states from the unbound conformation. Nevertheless, the direction of the predicted motions overlap with the actual conformational change. We next forced the backbone from the unbound toward the bound conformation to create a family of docking energy landscapes. Seventy percent of docking targets succeed when the unbound backbones is pushed to within 0.6 Å of the bound. Current methods can capture an average of 22% of unbound-bound transitions through conformer selection methods and another 57% through induced-fit methodologies, delineating a stubborn gap (21%) in backbone motion not covered by any current approach.
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Affiliation(s)
- Daisuke Kuroda
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Analytical and Physical Chemistry, Showa University School of Pharmacy, Tokyo 142-8555, Japan
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.
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14
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Gainza P, Nisonoff HM, Donald BR. Algorithms for protein design. Curr Opin Struct Biol 2016; 39:16-26. [PMID: 27086078 PMCID: PMC5065368 DOI: 10.1016/j.sbi.2016.03.006] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 03/15/2016] [Accepted: 03/22/2016] [Indexed: 02/05/2023]
Abstract
Computational structure-based protein design programs are becoming an increasingly important tool in molecular biology. These programs compute protein sequences that are predicted to fold to a target structure and perform a desired function. The success of a program's predictions largely relies on two components: first, the input biophysical model, and second, the algorithm that computes the best sequence(s) and structure(s) according to the biophysical model. Improving both the model and the algorithm in tandem is essential to improving the success rate of current programs, and here we review recent developments in algorithms for protein design, emphasizing how novel algorithms enable the use of more accurate biophysical models. We conclude with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins and protein assemblies.
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Affiliation(s)
- Pablo Gainza
- Department of Computer Science, Duke University, Durham, NC, United States
| | - Hunter M Nisonoff
- Department of Computer Science, Duke University, Durham, NC, United States
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC, United States; Department of Biochemistry, Duke University Medical Center, Durham, NC, United States; Department of Chemistry, Duke University, Durham, NC, United States.
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15
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Leaver-Fay A, Froning KJ, Atwell S, Aldaz H, Pustilnik A, Lu F, Huang F, Yuan R, Hassanali S, Chamberlain AK, Fitchett JR, Demarest SJ, Kuhlman B. Computationally Designed Bispecific Antibodies using Negative State Repertoires. Structure 2016; 24:641-651. [PMID: 26996964 DOI: 10.1016/j.str.2016.02.013] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Revised: 02/04/2016] [Accepted: 02/17/2016] [Indexed: 12/27/2022]
Abstract
A challenge in the structure-based design of specificity is modeling the negative states, i.e., the complexes that you do not want to form. This is a difficult problem because mutations predicted to destabilize the negative state might be accommodated by small conformational rearrangements. To overcome this challenge, we employ an iterative strategy that cycles between sequence design and protein docking in order to build up an ensemble of alternative negative state conformations for use in specificity prediction. We have applied our technique to the design of heterodimeric CH3 interfaces in the Fc region of antibodies. Combining computationally and rationally designed mutations produced unique designs with heterodimer purities greater than 90%. Asymmetric Fc crystallization was able to resolve the interface mutations; the heterodimer structures confirmed that the interfaces formed as designed. With these CH3 mutations, and those made at the heavy-/light-chain interface, we demonstrate one-step synthesis of four fully IgG-bispecific antibodies.
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Affiliation(s)
- Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Campus Box 7260, Chapel Hill, NC 27599, USA
| | - Karen J Froning
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Shane Atwell
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Hector Aldaz
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Anna Pustilnik
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Frances Lu
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Flora Huang
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Richard Yuan
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Saleema Hassanali
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Aaron K Chamberlain
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Jonathan R Fitchett
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Stephen J Demarest
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA.
| | - Brian Kuhlman
- Department of Biochemistry, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Campus Box 7260, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, USA.
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16
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Sirin S, Apgar JR, Bennett EM, Keating AE. AB-Bind: Antibody binding mutational database for computational affinity predictions. Protein Sci 2016; 25:393-409. [PMID: 26473627 PMCID: PMC4815335 DOI: 10.1002/pro.2829] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2015] [Revised: 10/09/2015] [Accepted: 10/12/2015] [Indexed: 12/26/2022]
Abstract
Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. The need for high-affinity and high-specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. Our Antibody-Bind (AB-Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔG) across 32 complexes. Using the AB-Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Numerical correlations between computed and observed ΔΔG values were low (r = 0.16-0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves; the highest AUC values for 527 mutants with |ΔΔG| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Some methods could also enrich for variants with improved binding affinity; FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with ΔΔG < -1.0 kcal/mol) in the top 5% of the database. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.
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Affiliation(s)
- Sarah Sirin
- Department of BiologyMassachusetts Institute of TechnologyCambridgeMassachusetts02139
| | - James R. Apgar
- Global Biotherapeutics Technologies, Pfizer Inc610 Main StreetCambridgeMassachusetts02139
| | - Eric M. Bennett
- Global Biotherapeutics Technologies, Pfizer Inc610 Main StreetCambridgeMassachusetts02139
| | - Amy E. Keating
- Department of BiologyMassachusetts Institute of TechnologyCambridgeMassachusetts02139
- Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeMassachusetts02139
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17
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Schenkelberg CD, Bystroff C. Protein backbone ensemble generation explores the local structural space of unseen natural homologs. Bioinformatics 2016; 32:1454-61. [PMID: 26787668 DOI: 10.1093/bioinformatics/btw001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Accepted: 01/03/2016] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Mutations in homologous proteins affect changes in the backbone conformation that involve a complex interplay of forces which are difficult to predict. Protein design algorithms need to anticipate these backbone changes in order to accurately calculate the energy of the structure given an amino acid sequence, without knowledge of the final, designed sequence. This is related to the problem of predicting small changes in the backbone between highly similar sequences. RESULTS We explored the ability of the Rosetta suite of protein design tools to move the backbone from its position in one structure (template) to its position in a close homologous structure (target) as a function of the diversity of a backbone ensemble constructed using the template structure, the percent sequence identity between the template and target, and the size of local zone being considered in the ensemble. We describe a pareto front in the likelihood of moving the backbone toward the target as a function of ensemble diversity and zone size. The equations and protocols presented here will be useful for protein design. AVAILABILITY AND IMPLEMENTATION PyRosetta scripts available at www.bioinfo.rpi.edu/bystrc/downloads.html#ensemble CONTACT bystrc@rpi.edu.
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Affiliation(s)
| | - Christopher Bystroff
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
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18
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Roberts KE, Gainza P, Hallen MA, Donald BR. Fast gap-free enumeration of conformations and sequences for protein design. Proteins 2015; 83:1859-1877. [PMID: 26235965 DOI: 10.1002/prot.24870] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Revised: 07/14/2015] [Accepted: 07/21/2015] [Indexed: 12/12/2022]
Abstract
Despite significant successes in structure-based computational protein design in recent years, protein design algorithms must be improved to increase the biological accuracy of new designs. Protein design algorithms search through an exponential number of protein conformations, protein ensembles, and amino acid sequences in an attempt to find globally optimal structures with a desired biological function. To improve the biological accuracy of protein designs, it is necessary to increase both the amount of protein flexibility allowed during the search and the overall size of the design, while guaranteeing that the lowest-energy structures and sequences are found. DEE/A*-based algorithms are the most prevalent provable algorithms in the field of protein design and can provably enumerate a gap-free list of low-energy protein conformations, which is necessary for ensemble-based algorithms that predict protein binding. We present two classes of algorithmic improvements to the A* algorithm that greatly increase the efficiency of A*. First, we analyze the effect of ordering the expansion of mutable residue positions within the A* tree and present a dynamic residue ordering that reduces the number of A* nodes that must be visited during the search. Second, we propose new methods to improve the conformational bounds used to estimate the energies of partial conformations during the A* search. The residue ordering techniques and improved bounds can be combined for additional increases in A* efficiency. Our enhancements enable all A*-based methods to more fully search protein conformation space, which will ultimately improve the accuracy of complex biomedically relevant designs.
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Affiliation(s)
- Kyle E Roberts
- Department of Computer Science, Duke University, Durham, NC
| | - Pablo Gainza
- Department of Computer Science, Duke University, Durham, NC
| | - Mark A Hallen
- Department of Computer Science, Duke University, Durham, NC
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC.,Department of Biochemistry, Duke University Medical Center, Durham, NC.,Department of Chemistry, Duke University, Durham, NC
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19
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Davey JA, Chica RA. Optimization of rotamers prior to template minimization improves stability predictions made by computational protein design. Protein Sci 2015; 24:545-60. [PMID: 25492709 DOI: 10.1002/pro.2618] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Accepted: 12/04/2014] [Indexed: 11/07/2022]
Abstract
Computational protein design (CPD) predictions are highly dependent on the structure of the input template used. However, it is unclear how small differences in template geometry translate to large differences in stability prediction accuracy. Herein, we explored how structural changes to the input template affect the outcome of stability predictions by CPD. To do this, we prepared alternate templates by Rotamer Optimization followed by energy Minimization (ROM) and used them to recapitulate the stability of 84 protein G domain β1 mutant sequences. In the ROM process, side-chain rotamers for wild-type (WT) or mutant sequences are optimized on crystal or nuclear magnetic resonance (NMR) structures prior to template minimization, resulting in alternate structures termed ROM templates. We show that use of ROM templates prepared from sequences known to be stable results predominantly in improved prediction accuracy compared to using the minimized crystal or NMR structures. Conversely, ROM templates prepared from sequences that are less stable than the WT reduce prediction accuracy by increasing the number of false positives. These observed changes in prediction outcomes are attributed to differences in side-chain contacts made by rotamers in ROM templates. Finally, we show that ROM templates prepared from sequences that are unfolded or that adopt a nonnative fold result in the selective enrichment of sequences that are also unfolded or that adopt a nonnative fold, respectively. Our results demonstrate the existence of a rotamer bias caused by the input template that can be harnessed to skew predictions toward sequences displaying desired characteristics.
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Affiliation(s)
- James A Davey
- Department of Chemistry, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5
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20
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Lanouette S, Davey JA, Elisma F, Ning Z, Figeys D, Chica RA, Couture JF. Discovery of substrates for a SET domain lysine methyltransferase predicted by multistate computational protein design. Structure 2014; 23:206-215. [PMID: 25533488 DOI: 10.1016/j.str.2014.11.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Revised: 11/04/2014] [Accepted: 11/05/2014] [Indexed: 01/01/2023]
Abstract
Characterization of lysine methylation has proven challenging despite its importance in biological processes such as gene transcription, protein turnover, and cytoskeletal organization. In contrast to other key posttranslational modifications, current proteomics techniques have thus far shown limited success at characterizing methyl-lysine residues across the cellular landscape. To complement current biochemical characterization methods, we developed a multistate computational protein design procedure to probe the substrate specificity of the protein lysine methyltransferase SMYD2. Modeling of substrate-bound SMYD2 identified residues important for substrate recognition and predicted amino acids necessary for methylation. Peptide- and protein- based substrate libraries confirmed that SMYD2 activity is dictated by the motif [LFM]-1-K(∗)-[AFYMSHRK]+1-[LYK]+2 around the target lysine K(∗). Comprehensive motif-based searches and mutational analysis further established four additional substrates of SMYD2. Our methodology paves the way to systematically predict and validate posttranslational modification sites while simultaneously pairing them with their associated enzymes.
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Affiliation(s)
- Sylvain Lanouette
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada
| | - James A Davey
- Department of Chemistry, University of Ottawa, Ottawa, ON, K1N 6N5, Canada; Centre for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
| | - Fred Elisma
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada
| | - Zhibin Ning
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada
| | - Daniel Figeys
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada; Department of Chemistry, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
| | - Roberto A Chica
- Department of Chemistry, University of Ottawa, Ottawa, ON, K1N 6N5, Canada; Centre for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, K1N 6N5, Canada.
| | - Jean-François Couture
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada; Centre for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, K1N 6N5, Canada.
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21
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Liu S, Liu S, Wang Y, Liao Z. The P2/P2′ sites affect the substrate cleavage of TNF-α converting enzyme (TACE). Mol Immunol 2014; 62:122-8. [DOI: 10.1016/j.molimm.2014.05.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2014] [Revised: 05/28/2014] [Accepted: 05/29/2014] [Indexed: 01/08/2023]
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22
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Pierce BG, Weng Z. A flexible docking approach for prediction of T cell receptor-peptide-MHC complexes. Protein Sci 2014; 22:35-46. [PMID: 23109003 DOI: 10.1002/pro.2181] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2012] [Accepted: 10/15/2012] [Indexed: 11/10/2022]
Abstract
T cell receptors (TCRs) are immune proteins that specifically bind to antigenic molecules, which are often foreign peptides presented by major histocompatibility complex proteins (pMHCs), playing a key role in the cellular immune response. To advance our understanding and modeling of this dynamic immunological event, we assembled a protein-protein docking benchmark consisting of 20 structures of crystallized TCR/pMHC complexes for which unbound structures exist for both TCR and pMHC. We used our benchmark to compare predictive performance using several flexible and rigid backbone TCR/pMHC docking protocols. Our flexible TCR docking algorithm, TCRFlexDock, improved predictive success over the fixed backbone protocol, leading to near-native predictions for 80% of the TCR/pMHC cases among the top 10 models, and 100% of the cases in the top 30 models. We then applied TCRFlexDock to predict the two distinct docking modes recently described for a single TCR bound to two different antigens, and tested several protein modeling scoring functions for prediction of TCR/pMHC binding affinities. This algorithm and benchmark should enable future efforts to predict, and design of uncharacterized TCR/pMHC complexes.
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Affiliation(s)
- Brian G Pierce
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA
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23
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Wijma HJ, Floor RJ, Jekel PA, Baker D, Marrink SJ, Janssen DB. Computationally designed libraries for rapid enzyme stabilization. Protein Eng Des Sel 2014; 27:49-58. [PMID: 24402331 PMCID: PMC3893934 DOI: 10.1093/protein/gzt061] [Citation(s) in RCA: 166] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2013] [Revised: 11/28/2013] [Accepted: 11/30/2013] [Indexed: 11/24/2022] Open
Abstract
The ability to engineer enzymes and other proteins to any desired stability would have wide-ranging applications. Here, we demonstrate that computational design of a library with chemically diverse stabilizing mutations allows the engineering of drastically stabilized and fully functional variants of the mesostable enzyme limonene epoxide hydrolase. First, point mutations were selected if they significantly improved the predicted free energy of protein folding. Disulfide bonds were designed using sampling of backbone conformational space, which tripled the number of experimentally stabilizing disulfide bridges. Next, orthogonal in silico screening steps were used to remove chemically unreasonable mutations and mutations that are predicted to increase protein flexibility. The resulting library of 64 variants was experimentally screened, which revealed 21 (pairs of) stabilizing mutations located both in relatively rigid and in flexible areas of the enzyme. Finally, combining 10-12 of these confirmed mutations resulted in multi-site mutants with an increase in apparent melting temperature from 50 to 85°C, enhanced catalytic activity, preserved regioselectivity and a >250-fold longer half-life. The developed Framework for Rapid Enzyme Stabilization by Computational libraries (FRESCO) requires far less screening than conventional directed evolution.
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Affiliation(s)
- Hein J. Wijma
- Department of Biochemistry, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Robert J. Floor
- Department of Biochemistry, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Peter A. Jekel
- Department of Biochemistry, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
| | - Siewert J. Marrink
- Department of Biochemistry, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
- Department of Biophysical Chemistry, Zernike Institute for Advanced Materials, University of Groningen, Groningen, The Netherlands
| | - Dick B. Janssen
- Department of Biochemistry, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
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24
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Ollikainen N, Kortemme T. Computational protein design quantifies structural constraints on amino acid covariation. PLoS Comput Biol 2013; 9:e1003313. [PMID: 24244128 PMCID: PMC3828131 DOI: 10.1371/journal.pcbi.1003313] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Accepted: 09/20/2013] [Indexed: 02/02/2023] Open
Abstract
Amino acid covariation, where the identities of amino acids at different sequence positions are correlated, is a hallmark of naturally occurring proteins. This covariation can arise from multiple factors, including selective pressures for maintaining protein structure, requirements imposed by a specific function, or from phylogenetic sampling bias. Here we employed flexible backbone computational protein design to quantify the extent to which protein structure has constrained amino acid covariation for 40 diverse protein domains. We find significant similarities between the amino acid covariation in alignments of natural protein sequences and sequences optimized for their structures by computational protein design methods. These results indicate that the structural constraints imposed by protein architecture play a dominant role in shaping amino acid covariation and that computational protein design methods can capture these effects. We also find that the similarity between natural and designed covariation is sensitive to the magnitude and mechanism of backbone flexibility used in computational protein design. Our results thus highlight the necessity of including backbone flexibility to correctly model precise details of correlated amino acid changes and give insights into the pressures underlying these correlations. Proteins generally fold into specific three-dimensional structures to perform their cellular functions, and the presence of misfolded proteins is often deleterious for cellular and organismal fitness. For these reasons, maintenance of protein structure is thought to be one of the major fitness pressures acting on proteins. Consequently, the sequences of today's naturally occurring proteins contain signatures reflecting the constraints imposed by protein structure. Here we test the ability of computational protein design methods to recapitulate and explain these signatures. We focus on the physical basis of evolutionary pressures that act on interactions between amino acids in folded proteins, which are critical in determining protein structure and function. Such pressures can be observed from the appearance of amino acid covariation, where the amino acids at certain positions in protein sequences are correlated with each other. We find similar patterns of amino acid covariation in natural sequences and sequences optimized for their structures using computational protein design, demonstrating the importance of structural constraints in protein molecular evolution and providing insights into the structural mechanisms leading to covariation. In addition, these results characterize the ability of computational methods to model the precise details of correlated amino acid changes, which is critical for engineering new proteins with useful functions beyond those seen in nature.
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Affiliation(s)
- Noah Ollikainen
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, California, United States of America
| | - Tanja Kortemme
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, California, United States of America
- California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, California, United States of America
- Department of Bioengineering and Therapeutic Science, University of California San Francisco, San Francisco, California, United States of America
- * E-mail:
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25
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Wijma HJ, Janssen DB. Computational design gains momentum in enzyme catalysis engineering. FEBS J 2013; 280:2948-60. [DOI: 10.1111/febs.12324] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2013] [Revised: 04/19/2013] [Accepted: 04/24/2013] [Indexed: 01/19/2023]
Affiliation(s)
- Hein J. Wijma
- Department of Biochemistry; Groningen Biomolecular Sciences and Biotechnology Institute; University of Groningen; The Netherlands
| | - Dick B. Janssen
- Department of Biochemistry; Groningen Biomolecular Sciences and Biotechnology Institute; University of Groningen; The Netherlands
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26
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Kuroda D, Shirai H, Jacobson MP, Nakamura H. Computer-aided antibody design. Protein Eng Des Sel 2012; 25:507-21. [PMID: 22661385 PMCID: PMC3449398 DOI: 10.1093/protein/gzs024] [Citation(s) in RCA: 169] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2012] [Revised: 04/14/2012] [Accepted: 04/19/2012] [Indexed: 11/12/2022] Open
Abstract
Recent clinical trials using antibodies with low toxicity and high efficiency have raised expectations for the development of next-generation protein therapeutics. However, the process of obtaining therapeutic antibodies remains time consuming and empirical. This review summarizes recent progresses in the field of computer-aided antibody development mainly focusing on antibody modeling, which is divided essentially into two parts: (i) modeling the antigen-binding site, also called the complementarity determining regions (CDRs), and (ii) predicting the relative orientations of the variable heavy (V(H)) and light (V(L)) chains. Among the six CDR loops, the greatest challenge is predicting the conformation of CDR-H3, which is the most important in antigen recognition. Further computational methods could be used in drug development based on crystal structures or homology models, including antibody-antigen dockings and energy calculations with approximate potential functions. These methods should guide experimental studies to improve the affinities and physicochemical properties of antibodies. Finally, several successful examples of in silico structure-based antibody designs are reviewed. We also briefly review structure-based antigen or immunogen design, with application to rational vaccine development.
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Affiliation(s)
- Daisuke Kuroda
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, Japan.
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27
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Davey JA, Chica RA. Multistate approaches in computational protein design. Protein Sci 2012; 21:1241-52. [PMID: 22811394 DOI: 10.1002/pro.2128] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2012] [Revised: 07/04/2012] [Accepted: 07/12/2012] [Indexed: 11/10/2022]
Abstract
Computational protein design (CPD) is a useful tool for protein engineers. It has been successfully applied towards the creation of proteins with increased thermostability, improved binding affinity, novel enzymatic activity, and altered ligand specificity. Traditionally, CPD calculations search and rank sequences using a single fixed protein backbone template in an approach referred to as single-state design (SSD). While SSD has enjoyed considerable success, certain design objectives require the explicit consideration of multiple conformational and/or chemical states. Cases where a "multistate" approach may be advantageous over the SSD approach include designing conformational changes into proteins, using native ensembles to mimic backbone flexibility, and designing ligand or oligomeric association specificities. These design objectives can be efficiently tackled using multistate design (MSD), an emerging methodology in CPD that considers any number of protein conformational or chemical states as inputs instead of a single protein backbone template, as in SSD. In this review article, recent examples of the successful design of a desired property into proteins using MSD are described. These studies employing MSD are divided into two categories--those that utilized multiple conformational states, and those that utilized multiple chemical states. In addition, the scoring of competing states during negative design is discussed as a current challenge for MSD.
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Affiliation(s)
- James A Davey
- Department of Chemistry, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
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