1
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Wang L, Behara PK, Thompson MW, Gokey T, Wang Y, Wagner JR, Cole DJ, Gilson MK, Shirts MR, Mobley DL. The Open Force Field Initiative: Open Software and Open Science for Molecular Modeling. J Phys Chem B 2024; 128:7043-7067. [PMID: 38989715 DOI: 10.1021/acs.jpcb.4c01558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Force fields are a key component of physics-based molecular modeling, describing the energies and forces in a molecular system as a function of the positions of the atoms and molecules involved. Here, we provide a review and scientific status report on the work of the Open Force Field (OpenFF) Initiative, which focuses on the science, infrastructure and data required to build the next generation of biomolecular force fields. We introduce the OpenFF Initiative and the related OpenFF Consortium, describe its approach to force field development and software, and discuss accomplishments to date as well as future plans. OpenFF releases both software and data under open and permissive licensing agreements to enable rapid application, validation, extension, and modification of its force fields and software tools. We discuss lessons learned to date in this new approach to force field development. We also highlight ways that other force field researchers can get involved, as well as some recent successes of outside researchers taking advantage of OpenFF tools and data.
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Affiliation(s)
- Lily Wang
- Open Force Field, Open Molecular Software Foundation, Davis, California 95616, United States
| | - Pavan Kumar Behara
- Center for Neurotherapeutics, University of California, Irvine, California 92697, United States
| | - Matthew W Thompson
- Open Force Field, Open Molecular Software Foundation, Davis, California 95616, United States
| | - Trevor Gokey
- Department of Chemistry, University of California, Irvine, California 92697, United States
| | - Yuanqing Wang
- Simons Center for Computational Physical Chemistry and Center for Data Science, New York, New York 10004, United States
| | - Jeffrey R Wagner
- Open Force Field, Open Molecular Software Foundation, Davis, California 95616, United States
| | - Daniel J Cole
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, The University of California at San Diego, La Jolla, California 92093, United States
| | - Michael R Shirts
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
| | - David L Mobley
- Department of Chemistry, University of California, Irvine, California 92697, United States
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
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2
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Wankowicz SA, Ravikumar A, Sharma S, Riley BT, Raju A, Flowers J, Hogan D, van den Bedem H, Keedy DA, Fraser JS. Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.28.546963. [PMID: 37425870 PMCID: PMC10327213 DOI: 10.1101/2023.06.28.546963] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift towards modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R f r e e and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g. Coot) and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.
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Affiliation(s)
- Stephanie A. Wankowicz
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Ashraya Ravikumar
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Shivani Sharma
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
- Ph.D. Program in Biology, The Graduate Center – City University of New York, New York, NY 10016
| | - Blake T. Riley
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
| | - Akshay Raju
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
| | - Jessica Flowers
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Hogan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Henry van den Bedem
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
- Atomwise, Inc., San Francisco, CA, United States
| | - Daniel A. Keedy
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
- Department of Chemistry and Biochemistry, City College of New York, New York, NY 10031
- Ph.D. Programs in Biochemistry, Biology, and Chemistry, The Graduate Center – City University of New York, New York, NY 10016
| | - James S. Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
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3
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Carpentier M, Chomilier J. Analyses of Mutation Displacements from Homology Models. Methods Mol Biol 2023; 2627:195-210. [PMID: 36959449 DOI: 10.1007/978-1-0716-2974-1_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
Evaluation of the structural perturbations introduced by a single amino acid mutation is the main issue for protein structural biology. We propose here to present some recent advances in methods, allowing the splitting of distortion between the actual substitution effect and the contribution of the local flexibility of the position where the mutation occurs. Its main drawback is the need of many structures with a single mutation in each of them. To bypass this difficulty, we propose to use molecular modeling tools, with several software enabling us to build a model from a template, given the sequence. As a proof of concept, we rely on a gold standard, the human lysozyme. Both wild-type and three mutant structures are available in the PDB. Two of these mutations result in amyloid fibril formation, and the last one is neutral. As a conclusion, irrespective of the algorithm used for modeling, side chain conformations at the site of mutation are reliable, although long-range effects are out of reach of these tools.
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Affiliation(s)
- Mathilde Carpentier
- Institut Systématique Evolution Biodiversité (ISYEB), Sorbonne Université, MNHN, CNRS, EPHE, Paris, France.
| | - Jacques Chomilier
- Sorbonne Université, BiBiP, IMPMC, UMR 7590, CNRS, MNHN, Paris, France
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4
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Ashworth MA, Bombino E, de Jong RM, Wijma HJ, Janssen DB, McLean KJ, Munro AW. Computation-Aided Engineering of Cytochrome P450 for the Production of Pravastatin. ACS Catal 2022; 12:15028-15044. [PMID: 36570080 PMCID: PMC9764288 DOI: 10.1021/acscatal.2c03974] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/22/2022] [Indexed: 11/29/2022]
Abstract
CYP105AS1 is a cytochrome P450 from Amycolatopsis orientalis that catalyzes monooxygenation of compactin to 6-epi-pravastatin. For fermentative production of the cholesterol-lowering drug pravastatin, the stereoselectivity of the enzyme needs to be inverted, which has been partially achieved by error-prone PCR mutagenesis and screening. In the current study, we report further optimization of the stereoselectivity by a computationally aided approach. Using the CoupledMoves protocol of Rosetta, a virtual library of mutants was designed to bind compactin in a pro-pravastatin orientation. By examining the frequency of occurrence of beneficial substitutions and rational inspection of their interactions, a small set of eight mutants was predicted to show the desired selectivity and these variants were tested experimentally. The best CYP105AS1 variant gave >99% stereoselective hydroxylation of compactin to pravastatin, with complete elimination of the unwanted 6-epi-pravastatin diastereomer. The enzyme-substrate complexes were also examined by ultrashort molecular dynamics simulations of 50 × 100 ps and 5 × 22 ns, which revealed that the frequency of occurrence of near-attack conformations agreed with the experimentally observed stereoselectivity. These results show that a combination of computational methods and rational inspection could improve CYP105AS1 stereoselectivity beyond what was obtained by directed evolution. Moreover, the work lays out a general in silico framework for specificity engineering of enzymes of known structure.
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Affiliation(s)
- Mark A. Ashworth
- Manchester
Institute of Biotechnology, School of Chemistry, The University of Manchester, Manchester M1 7DN, United Kingdom
| | - Elvira Bombino
- Department
of Biochemistry, Groningen Biomolecular Sciences and Biotechnology
Institute, University of Groningen, Nijenborgh 4, Groningen 9747 AG, Netherlands
| | - René M. de Jong
- DSM
Food & Beverage, Alexander Fleminglaan 1, 2613 AX Delft, the Netherlands
| | - Hein J. Wijma
- Department
of Biochemistry, Groningen Biomolecular Sciences and Biotechnology
Institute, University of Groningen, Nijenborgh 4, Groningen 9747 AG, Netherlands
| | - Dick B. Janssen
- Department
of Biochemistry, Groningen Biomolecular Sciences and Biotechnology
Institute, University of Groningen, Nijenborgh 4, Groningen 9747 AG, Netherlands,
| | - Kirsty J. McLean
- Manchester
Institute of Biotechnology, School of Chemistry, The University of Manchester, Manchester M1 7DN, United Kingdom,Department
of Biological and Geographical Sciences, School of Applied Sciences, University of Huddersfield, Huddersfield HD1 3DH, United Kingdom
| | - Andrew W. Munro
- Manchester
Institute of Biotechnology, School of Chemistry, The University of Manchester, Manchester M1 7DN, United Kingdom
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5
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Ferraz MVF, Viana IFT, Coêlho DF, da Cruz CHB, de Arruda Lima M, de Luna Aragão MA, Lins RD. Association strength of E6 to E6AP/p53 complex correlates with HPV‐mediated oncogenesis risk. Biopolymers 2022; 113:e23524. [DOI: 10.1002/bip.23524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/26/2022] [Accepted: 08/01/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Matheus Vitor Ferreira Ferraz
- Aggeu Magalhães Institute Oswaldo Cruz Foundation Recife Brazil
- Department of Fundamental Chemistry Federal University of Pernambuco Recife Brazil
| | | | - Danilo Fernandes Coêlho
- Aggeu Magalhães Institute Oswaldo Cruz Foundation Recife Brazil
- Department of Fundamental Chemistry Federal University of Pernambuco Recife Brazil
| | | | | | | | - Roberto Dias Lins
- Aggeu Magalhães Institute Oswaldo Cruz Foundation Recife Brazil
- Department of Fundamental Chemistry Federal University of Pernambuco Recife Brazil
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6
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Gumpper RH, Fay JF, Roth BL. Molecular insights into the regulation of constitutive activity by RNA editing of 5HT 2C serotonin receptors. Cell Rep 2022; 40:111211. [PMID: 35977511 PMCID: PMC9422347 DOI: 10.1016/j.celrep.2022.111211] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 06/01/2022] [Accepted: 07/21/2022] [Indexed: 11/25/2022] Open
Abstract
RNA editing is a process by which post-transcriptional changes of mRNA nucleotides alter protein function through modification of the amino acid content. The 5HT2C serotonin receptor, which undergoes 32 distinct RNA-editing events leading to 24 protein isoforms, is a notable example of this process. These 5HT2C isoforms display differences in constitutive activity, agonist/inverse agonist potencies, and efficacies. To elucidate the molecular mechanisms responsible for these effects of RNA editing, we present four active-state 5HT2C-transducer-coupled structures of three representative isoforms (INI, VGV, and VSV) with the selective drug lorcaserin (Belviq) and the classic psychedelic psilocin. We also provide a comprehensive analysis of agonist activation and constitutive activity across all 24 protein isoforms. Collectively, these findings reveal a unique hydrogen-bonding network located on intracellular loop 2 that is subject to RNA editing, which differentially affects GPCR constitutive and agonist signaling activities. It is established that the 5HT2C receptor undergoes RNA editing leading to 24 isoforms. Several isoforms exhibit changes in basal activity and are linked to pathologies. Gumpper et al. have done a systematic structure-function characterization of all the isoforms revealing the underlying mechanisms that govern basal activity of the 5HT2C receptor.
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Affiliation(s)
- Ryan H Gumpper
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA.
| | - Jonathan F Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA.
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7
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Magi Meconi G, Sasselli IR, Bianco V, Onuchic JN, Coluzza I. Key aspects of the past 30 years of protein design. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2022; 85:086601. [PMID: 35704983 DOI: 10.1088/1361-6633/ac78ef] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.
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Affiliation(s)
- Giulia Magi Meconi
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | - Ivan R Sasselli
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | | | - Jose N Onuchic
- Center for Theoretical Biological Physics, Department of Physics & Astronomy, Department of Chemistry, Department of Biosciences, Rice University, Houston, TX 77251, United States of America
| | - Ivan Coluzza
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, Bld. Martina Casiano, UPV/EHU Science Park, Barrio Sarriena s/n, 48940 Leioa, Spain
- Basque Foundation for Science, Ikerbasque, 48009, Bilbao, Spain
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8
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Gisdon FJ, Kynast JP, Ayyildiz M, Hine AV, Plückthun A, Höcker B. Modular peptide binders - development of a predictive technology as alternative for reagent antibodies. Biol Chem 2022; 403:535-543. [PMID: 35089661 DOI: 10.1515/hsz-2021-0384] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/11/2022] [Indexed: 11/15/2022]
Abstract
Current biomedical research and diagnostics critically depend on detection agents for specific recognition and quantification of protein molecules. Monoclonal antibodies have been used for this purpose over decades and facilitated numerous biological and biomedical investigations. Recently, however, it has become apparent that many commercial reagent antibodies lack specificity or do not recognize their target at all. Thus, synthetic alternatives are needed whose complex designs are facilitated by multidisciplinary approaches incorporating experimental protein engineering with computational modeling. Here, we review the status of such an engineering endeavor based on the modular armadillo repeat protein scaffold and discuss challenges in its implementation.
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Affiliation(s)
- Florian J Gisdon
- Department of Biochemistry, University of Bayreuth, D-95447 Bayreuth, Germany
| | - Josef P Kynast
- Department of Biochemistry, University of Bayreuth, D-95447 Bayreuth, Germany
| | - Merve Ayyildiz
- Department of Biochemistry, University of Bayreuth, D-95447 Bayreuth, Germany
| | - Anna V Hine
- College of Health and Life Sciences, Aston University, Birmingham B4 7ET, UK
| | - Andreas Plückthun
- Department of Biochemistry, University of Zurich, CH-8057 Zürich, Switzerland
| | - Birte Höcker
- Department of Biochemistry, University of Bayreuth, D-95447 Bayreuth, Germany
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9
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A Collection of Designed Peptides to Target SARS-CoV-2 Spike RBD-ACE2 Interaction. Int J Mol Sci 2021; 22:ijms222111627. [PMID: 34769056 PMCID: PMC8584250 DOI: 10.3390/ijms222111627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 01/18/2023] Open
Abstract
The angiotensin-converting enzyme 2 (ACE2) is the receptor used by SARS-CoV and SARS-CoV-2 coronaviruses to attach to cells via the receptor-binding domain (RBD) of their viral spike protein. Since the start of the COVID-19 pandemic, several structures of protein complexes involving ACE2 and RBD as well as monoclonal antibodies and nanobodies have become available. We have leveraged the structural data to design peptides to target the interaction between the RBD of SARS-CoV-2 and ACE2 and SARS-CoV and ACE2, as contrasting exemplar, as well as the dimerization surface of ACE2 monomers. The peptides were modelled using our original method: PiPreD that uses native elements of the interaction between the targeted protein and cognate partner(s) that are subsequently included in the designed peptides. These peptides recapitulate stretches of residues present in the native interface plus novel and highly diverse conformations surrogating key interactions at the interface. To facilitate the access to this information we have created a freely available and dedicated web-based repository, PepI-Covid19 database, providing convenient access to this wealth of information to the scientific community with the view of maximizing its potential impact in the development of novel therapeutic and diagnostic agents.
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10
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Pan X, Kortemme T. Recent advances in de novo protein design: Principles, methods, and applications. J Biol Chem 2021; 296:100558. [PMID: 33744284 PMCID: PMC8065224 DOI: 10.1016/j.jbc.2021.100558] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.
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Affiliation(s)
- Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA.
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA; Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California, USA.
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11
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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: 65] [Impact Index Per Article: 16.3] [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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12
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Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RYR, Watkins A, Zimmerman L, Bonneau R. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 2020; 17:665-680. [PMID: 32483333 PMCID: PMC7603796 DOI: 10.1038/s41592-020-0848-2] [Citation(s) in RCA: 433] [Impact Index Per Article: 108.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
| | - Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Steven M Lewis
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biochemistry, Duke University, Durham, NC, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Aprahamian
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kyle A Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, USA
| | - Patrick Barth
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Benjamin Basanta
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Biological Physics Structure and Design PhD Program, University of Washington, Seattle, WA, USA
| | - Brian J Bender
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Kristin Blacklock
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Scott E Boyken
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Phil Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Bystroff
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Patrick Conway
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lorna Dsilva
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Alexander S Ford
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Brandon Frenz
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Darwin Y Fu
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Sharon Guffy
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott Horowitz
- Department of Chemistry & Biochemistry, University of Denver, Denver, CO, USA
- The Knoebel Institute for Healthy Aging, University of Denver, Denver, CO, USA
| | - Po-Ssu Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Tim M Jacobs
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - David K Johnson
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - John Karanicolas
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Hamed Khakzad
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
| | - Karen R Khar
- Cyrus Biotechnology, Seattle, WA, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Sagar D Khare
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Indigo C King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Robert Kleffner
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daisuke Kuroda
- Medical Device Development and Regulation Research Center, School of Engineering, University of Tokyo, Tokyo, Japan
- Department of Bioengineering, School of Engineering, University of Tokyo, Tokyo, Japan
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemistry, Franklin & Marshall College, Lancaster, PA, USA
| | - Jason K Lai
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Thomas Linsky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Nir London
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Joseph H Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lars Malmström
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Enrique Marcos
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Research in Biomedicine Barcelona, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Orly Marcu
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nicholas A Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Santrupti Nerli
- Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Christoffer Norn
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shane Ó'Conchúir
- Department of Bioengineering and Therapeutic Sciences, 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
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Michael S Pacella
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ryan E Pavlovicz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Manasi Pethe
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Kala Bharath Pilla
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Barak Raveh
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Aliza Rubenstein
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Marion F Sauer
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Andreas Scheck
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Sedan
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alexander M Sevy
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Lei Shi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | | | - Shannon Smith
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Yifan Song
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Amelie Stein
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Szegedy
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Frank D Teets
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Summer B Thyme
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ray Yu-Ruei Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Lior Zimmerman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
- Department of Computer Science, New York University, New York, NY, USA.
- Center for Data Science, New York University, New York, NY, USA.
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13
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Affinity-matured variants derived from nimotuzumab keep the original fine specificity and exhibit superior biological activity. Sci Rep 2020; 10:1194. [PMID: 31988343 PMCID: PMC6985160 DOI: 10.1038/s41598-019-57279-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 12/26/2019] [Indexed: 12/11/2022] Open
Abstract
Nimotuzumab is a humanized monoclonal antibody against the Epidermal Growth Factor Receptor with a long history of therapeutic use, recognizing an epitope different from the ones targeted by other antibodies against the same antigen. It is also distinguished by much less toxicity resulting in a better safety profile, which has been attributed to its lower affinity compared to these other antibodies. Nevertheless, the ideal affinity window for optimizing the balance between anti-tumor activity and toxic effects has not been determined. In the current work, the paratope of the phage-displayed nimotuzumab Fab fragment was evolved in vitro to obtain affinity-matured variants. Soft-randomization of heavy chain variable region CDRs and phage selection resulted in mutated variants with improved binding ability. Two recombinant antibodies were constructed using these variable regions, which kept the original fine epitope specificity and showed moderate affinity increases against the target (3-4-fold). Such differences were translated into a greatly enhanced inhibitory capacity upon ligand-induced receptor phosphorylation on tumor cells. The new antibodies, named K4 and K5, are valuable tools to explore the role of affinity in nimotuzumab biological properties, and could be used for applications requiring a fine-tuning of the balance between binding to tumor cells and healthy tissues.
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14
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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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15
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Welborn VV, Head-Gordon T. Fluctuations of Electric Fields in the Active Site of the Enzyme Ketosteroid Isomerase. J Am Chem Soc 2019; 141:12487-12492. [DOI: 10.1021/jacs.9b05323] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Valerie Vaissier Welborn
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Teresa Head-Gordon
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
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16
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Luo L, Wang Y, Li B, Xu L, Kamau PM, Zheng J, Yang F, Yang S, Lai R. Molecular basis for heat desensitization of TRPV1 ion channels. Nat Commun 2019; 10:2134. [PMID: 31086183 PMCID: PMC6513986 DOI: 10.1038/s41467-019-09965-6] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Accepted: 04/09/2019] [Indexed: 12/26/2022] Open
Abstract
The transient receptor potential vanilloid 1 (TRPV1) ion channel is a prototypical molecular sensor for noxious heat in mammals. Its role in sustained heat response remains poorly understood, because rapid heat-induced desensitization (Dh) follows tightly heat-induced activation (Ah). To understand the physiological role and structural basis of Dh, we carried out a comparative study of TRPV1 channels in mouse (mV1) and those in platypus (pV1), which naturally lacks Dh. Here we show that a temperature-sensitive interaction between the N- and C-terminal domains of mV1 but not pV1 drives a conformational rearrangement in the pore leading to Dh. We further show that knock-in mice expressing pV1 sensed heat normally but suffered scald damages in a hot environment. Our findings suggest that Dh evolved late during evolution as a protective mechanism and a delicate balance between Ah and Dh is crucial for mammals to sense and respond to noxious heat.
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Affiliation(s)
- Lei Luo
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, 650223, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yunfei Wang
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, 650223, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bowen Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, 650223, China
| | - Lizhen Xu
- Key Laboratory of Medical Neurobiology, Department of Biophysics and Kidney Disease Center, First Affiliated Hospital, Institute of Neuroscience, National Health Commission and Chinese Academy of Medical Sciences, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China
| | - Peter Muiruri Kamau
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, 650223, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jie Zheng
- Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA.
| | - Fan Yang
- Key Laboratory of Medical Neurobiology, Department of Biophysics and Kidney Disease Center, First Affiliated Hospital, Institute of Neuroscience, National Health Commission and Chinese Academy of Medical Sciences, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.
| | - Shilong Yang
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, 650223, China.
| | - Ren Lai
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, 650223, China.
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17
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Yang F, Xiao X, Lee BH, Vu S, Yang W, Yarov-Yarovoy V, Zheng J. The conformational wave in capsaicin activation of transient receptor potential vanilloid 1 ion channel. Nat Commun 2018; 9:2879. [PMID: 30038260 PMCID: PMC6056546 DOI: 10.1038/s41467-018-05339-6] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 06/19/2018] [Indexed: 01/17/2023] Open
Abstract
The capsaicin receptor TRPV1 has been intensively studied by cryo-electron microscopy and functional tests. However, though the apo and capsaicin-bound structural models are available, the dynamic process of capsaicin activation remains intangible, largely due to the lack of a capsaicin-induced open structural model and the low occupancy of the transition states. Here we report that reducing temperature toward the freezing point substantially increased channel closure events even in the presence of saturating capsaicin. We further used a combination of fluorescent unnatural amino acid (fUAA) incorporation, computational modeling, and rate-equilibrium linear free-energy relationships analysis (Φ-analysis) to derive the fully open capsaicin-bound state model, and reveal how the channel transits from the apo to the open state. We observed that capsaicin initiates a conformational wave that propagates through the S4–S5 linker towards the S6 bundle and finally reaching the selectivity filter. Our study provides a temporal mechanism for capsaicin activation of TRPV1. The capsaicin receptor TRPV1 has been structurally characterized, but the capsaicin activation dynamics remain elusive. Here authors use fluorescent unnatural amino acid incorporation, computational modeling and Φ-analysis to derive the capsaicin-bound open state model and reveal the capsaicin induced conformational changes.
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Affiliation(s)
- Fan Yang
- Department of Biophysics and Kidney Disease Center, First Affiliated Hospital, Institute of Neuroscience, National Health Commission and Chinese Academy of Medical Sciences Key Laboratory of Medical Neurobiology, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang Province, China. .,Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA.
| | - Xian Xiao
- Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA.,Institute for Basic Medical Sciences, Westlake Institute for Advanced Study, Westlake University, Shilongshan Road No. 18, Xihu District, Hangzhou, 310024, Zhejiang Province, China
| | - Bo Hyun Lee
- Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA.,University of Washington, Department of Physiology and Biophysics, Seattle, WA, 98195, USA
| | - Simon Vu
- Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA
| | - Wei Yang
- Department of Biophysics and Kidney Disease Center, First Affiliated Hospital, Institute of Neuroscience, National Health Commission and Chinese Academy of Medical Sciences Key Laboratory of Medical Neurobiology, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang Province, China
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA
| | - Jie Zheng
- Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA.
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18
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Chimeric Fatty Acyl-Acyl Carrier Protein Thioesterases Provide Mechanistic Insight into Enzyme Specificity and Expression. Appl Environ Microbiol 2018; 84:AEM.02868-17. [PMID: 29549102 DOI: 10.1128/aem.02868-17] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2017] [Accepted: 03/12/2018] [Indexed: 12/27/2022] Open
Abstract
Medium-chain fatty acids are commodity chemicals. Increasing and modifying the activity of thioesterases (TEs) on medium-chain fatty acyl-acyl carrier protein (acyl-ACP) esters may enable a high-yield microbial production of these molecules. The plant Cuphea palustris harbors two distinct TEs: C. palustris FatB1 (CpFatB1) (C8 specificity, lower activity) and CpFatB2 (C14 specificity, higher activity) with 78% sequence identity. We combined structural features from these two enzymes to create several chimeric TEs, some of which showed nonnatural fatty acid production as measured by an enzymatic assay and gas chromatography-mass spectrometry (GC-MS). Notably, chimera 4 exhibited an increased C8 fatty acid production in correlation with improved microbial expression. This chimera led us to identify CpFatB2-specific amino acids between positions 219 and 272 that lead to higher protein levels. Chimera 7 produced a broad range of fatty acids and appeared to combine a fatty acid binding pocket with long-chain specificity and an ACP interaction site that may activate fatty acid extrusion. Using homology modeling and in silico docking with ACP, we identified a "positive patch" within amino acids 162 to 218, which may direct the ACP interaction and regulate access to short-chain fatty acids. On the basis of this modeling, we transplanted putative ACP interaction sequences from CpFatB1 into CpFatB2 and created a chimeric thioesterase that produced medium-chain as well as long-chain fatty acids. Thus, the engineering of chimeric enzymes and characterizing their microbial activity and chain-length specificity suggested mechanistic insights into TE functions and also generated thioesterases with potentially useful properties. These observations may inform a rational engineering of TEs to allow alkyl chain length control.IMPORTANCE Medium-chain fatty acids are important commodity chemicals. These molecules are used as plastic precursors and in shampoos and other detergents and could be used as biofuel precursors if production economics were favorable. Hydrocarbon-based liquid fuels must be optimized to have a desired boiling point, low freezing point, low viscosity, and other physical characteristics. Similarly, the solubility and harshness of detergents and the flexibility of plastic polymers can be modulated. The length and distribution of the carbon chains in the hydrophobic tails determine these properties. The biological synthesis of cell membranes and fatty acids produces chains of primarily 16 to 18 carbons, which give rise to current biofuels. The ultimate goal of the work presented here is to engineer metabolic pathways to produce designer molecules with the correct number of carbons in a chain, so that such molecules could be used directly as specialty commodity chemicals or as fuels after minimal processing.
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19
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Barlow KA, Ó Conchúir S, Thompson S, Suresh P, Lucas JE, Heinonen M, Kortemme T. Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation. J Phys Chem B 2018; 122:5389-5399. [PMID: 29401388 DOI: 10.1021/acs.jpcb.7b11367] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Computationally modeling changes in binding free energies upon mutation (interface ΔΔ G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using "backrub" to generate an ensemble of models and then applies torsion minimization, side chain repacking, and averaging across this ensemble to estimate interface ΔΔ G values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting nonlinear reweighting model improved the agreement with experimentally determined interface ΔΔ G values but also highlighted the necessity of future energy function improvements.
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Affiliation(s)
- Kyle A Barlow
- Graduate Program in Bioinformatics , University of California San Francisco , San Francisco , California , United States of America
| | - Shane Ó Conchúir
- California Institute for Quantitative Biosciences , University of California San Francisco , San Francisco , California , United States of America.,Department of Bioengineering and Therapeutic Sciences , University of California San Francisco , San Francisco , California , United States of America
| | - Samuel Thompson
- Graduate Program in Biophysics , University of California San Francisco , San Francisco , California , United States of America
| | - Pooja Suresh
- Graduate Program in Biophysics , University of California San Francisco , San Francisco , California , United States of America
| | - James E Lucas
- Graduate Program in Bioengineering , University of California San Francisco , San Francisco , California , United States of America
| | - Markus Heinonen
- Department of Computer Science , Aalto University , Espoo , Finland.,Helsinki Institute for Information Technology (HIIT) , Helsinki , Finland
| | - Tanja Kortemme
- Graduate Program in Bioinformatics , University of California San Francisco , San Francisco , California , United States of America.,California Institute for Quantitative Biosciences , University of California San Francisco , San Francisco , California , United States of America.,Department of Bioengineering and Therapeutic Sciences , University of California San Francisco , San Francisco , California , United States of America.,Graduate Program in Biophysics , University of California San Francisco , San Francisco , California , United States of America.,Graduate Program in Bioengineering , University of California San Francisco , San Francisco , California , United States of America.,Chan Zuckerberg Biohub , San Francisco , California 94158 , United States
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20
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Vaissier V, Sharma SC, Schaettle K, Zhang T, Head-Gordon T. Computational Optimization of Electric Fields for Improving Catalysis of a Designed Kemp Eliminase. ACS Catal 2017. [DOI: 10.1021/acscatal.7b03151] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Valerie Vaissier
- Chemical
Sciences Division, Lawrence Berkeley National Laboratories, Berkeley, California 94720, United States
| | | | | | | | - Teresa Head-Gordon
- Chemical
Sciences Division, Lawrence Berkeley National Laboratories, Berkeley, California 94720, United States
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21
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Sun MGF, Kim PM. Data driven flexible backbone protein design. PLoS Comput Biol 2017; 13:e1005722. [PMID: 28837553 PMCID: PMC5587332 DOI: 10.1371/journal.pcbi.1005722] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Revised: 09/06/2017] [Accepted: 08/11/2017] [Indexed: 11/18/2022] Open
Abstract
Protein design remains an important problem in computational structural biology. Current computational protein design methods largely use physics-based methods, which make use of information from a single protein structure. This is despite the fact that multiple structures of many protein folds are now readily available in the PDB. While ensemble protein design methods can use multiple protein structures, they treat each structure independently. Here, we introduce a flexible backbone strategy, FlexiBaL-GP, which learns global protein backbone movements directly from multiple protein structures. FlexiBaL-GP uses the machine learning method of Gaussian Process Latent Variable Models to learn a lower dimensional representation of the protein coordinates that best represent backbone movements. These learned backbone movements are used to explore alternative protein backbones, while engineering a protein within a parallel tempered MCMC framework. Using the human ubiquitin-USP21 complex as a model we demonstrate that our design strategy outperforms current strategies for the interface design task of identifying tight binding ubiquitin variants for USP21.
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Affiliation(s)
- Mark G. F. Sun
- Department of Computer Science, University of Toronto, Toronto, Canada
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
| | - Philip M. Kim
- Department of Computer Science, University of Toronto, Toronto, Canada
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Canada
- * E-mail:
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22
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Löffler P, Schmitz S, Hupfeld E, Sterner R, Merkl R. Rosetta:MSF: a modular framework for multi-state computational protein design. PLoS Comput Biol 2017; 13:e1005600. [PMID: 28604768 PMCID: PMC5484525 DOI: 10.1371/journal.pcbi.1005600] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 06/26/2017] [Accepted: 05/27/2017] [Indexed: 12/20/2022] Open
Abstract
Computational protein design (CPD) is a powerful technique to engineer existing proteins or to design novel ones that display desired properties. Rosetta is a software suite including algorithms for computational modeling and analysis of protein structures and offers many elaborate protocols created to solve highly specific tasks of protein engineering. Most of Rosetta’s protocols optimize sequences based on a single conformation (i. e. design state). However, challenging CPD objectives like multi-specificity design or the concurrent consideration of positive and negative design goals demand the simultaneous assessment of multiple states. This is why we have developed the multi-state framework MSF that facilitates the implementation of Rosetta’s single-state protocols in a multi-state environment and made available two frequently used protocols. Utilizing MSF, we demonstrated for one of these protocols that multi-state design yields a 15% higher performance than single-state design on a ligand-binding benchmark consisting of structural conformations. With this protocol, we designed de novo nine retro-aldolases on a conformational ensemble deduced from a (βα)8-barrel protein. All variants displayed measurable catalytic activity, testifying to a high success rate for this concept of multi-state enzyme design. Protein engineering, i. e. the targeted modification or design of proteins has tremendous potential for medical and industrial applications. One generally applicable strategy for protein engineering is rational protein design: based on detailed knowledge of structure and function, computer programs like Rosetta propose the sequence of a protein possessing the desired properties. So far, most computer protocols have used rigid structures for design, which is a simplification because a protein’s structure is more accurately specified by a conformational ensemble. We have now implemented a framework for computational protein design that allows certain design protocols of Rosetta to make use of multiple design states like structural ensembles. An in silico assessment simulating ligand-binding design showed that this new approach generates more reliably native-like sequences than a single-state approach. As a proof-of-concept, we introduced de novo retro-aldolase activity into a scaffold protein and characterized nine variants experimentally, all of which were catalytically active.
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Affiliation(s)
- Patrick Löffler
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Samuel Schmitz
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Enrico Hupfeld
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Reinhard Sterner
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Rainer Merkl
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
- * E-mail:
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23
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Abstract
Protein-protein interactions play critical roles in essentially every cellular process. These interactions are often mediated by protein interaction domains that enable proteins to recognize their interaction partners, often by binding to short peptide motifs. For example, PDZ domains, which are among the most common protein interaction domains in the human proteome, recognize specific linear peptide sequences that are often at the C-terminus of other proteins. Determining the set of peptide sequences that a protein interaction domain binds, or it's "peptide specificity," is crucial for understanding its cellular function, and predicting how mutations impact peptide specificity is important for elucidating the mechanisms underlying human diseases. Moreover, engineering novel cellular functions for synthetic biology applications, such as the biosynthesis of biofuels or drugs, requires the design of protein interaction specificity to avoid crosstalk with native metabolic and signaling pathways. The ability to accurately predict and design protein-peptide interaction specificity is therefore critical for understanding and engineering biological function. One approach that has recently been employed toward accomplishing this goal is computational protein design. This chapter provides an overview of recent methodological advances in computational protein design and highlights examples of how these advances can enable increased accuracy in predicting and designing peptide specificity.
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Affiliation(s)
- Noah Ollikainen
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Blvd., Pasadena, CA, 91125, USA
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24
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Bhowmick A, Brookes DH, Yost SR, Dyson HJ, Forman-Kay JD, Gunter D, Head-Gordon M, Hura GL, Pande VS, Wemmer DE, Wright PE, Head-Gordon T. Finding Our Way in the Dark Proteome. J Am Chem Soc 2016; 138:9730-42. [PMID: 27387657 PMCID: PMC5051545 DOI: 10.1021/jacs.6b06543] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The traditional structure-function paradigm has provided significant insights for well-folded proteins in which structures can be easily and rapidly revealed by X-ray crystallography beamlines. However, approximately one-third of the human proteome is comprised of intrinsically disordered proteins and regions (IDPs/IDRs) that do not adopt a dominant well-folded structure, and therefore remain "unseen" by traditional structural biology methods. This Perspective considers the challenges raised by the "Dark Proteome", in which determining the diverse conformational substates of IDPs in their free states, in encounter complexes of bound states, and in complexes retaining significant disorder requires an unprecedented level of integration of multiple and complementary solution-based experiments that are analyzed with state-of-the art molecular simulation, Bayesian probabilistic models, and high-throughput computation. We envision how these diverse experimental and computational tools can work together through formation of a "computational beamline" that will allow key functional features to be identified in IDP structural ensembles.
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Affiliation(s)
- Asmit Bhowmick
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720
| | - David H. Brookes
- Department of Chemistry, University of California, Berkeley, CA 94720
| | - Shane R. Yost
- Department of Chemistry, University of California, Berkeley, CA 94720
| | - H. Jane Dyson
- Department of Integrative Structural and Computational Biology, Scripps Research Institute, La Jolla, California 92037
| | - Julie D. Forman-Kay
- Molecular Structure and Function Program, Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Daniel Gunter
- Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720
| | | | - Gregory L. Hura
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720
| | - Vijay S. Pande
- Department of Chemistry, Stanford University, Stanford, CA 94305
| | - David E. Wemmer
- Department of Chemistry, University of California, Berkeley, CA 94720
| | - Peter E. Wright
- Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Teresa Head-Gordon
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720
- Department of Chemistry, University of California, Berkeley, CA 94720
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720
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25
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Regan L, Caballero D, Hinrichsen MR, Virrueta A, Williams DM, O'Hern CS. Protein design: Past, present, and future. Biopolymers 2016; 104:334-50. [PMID: 25784145 DOI: 10.1002/bip.22639] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Revised: 03/05/2015] [Accepted: 03/07/2015] [Indexed: 01/16/2023]
Abstract
Building on the pioneering work of Ho and DeGrado (J Am Chem Soc 1987, 109, 6751-6758) in the late 1980s, protein design approaches have revealed many fundamental features of protein structure and stability. We are now in the era that the early work presaged - the design of new proteins with practical applications and uses. Here we briefly survey some past milestones in protein design, in addition to highlighting recent progress and future aspirations.
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Affiliation(s)
- Lynne Regan
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT.,Department of Chemistry, Yale University, New Haven, CT.,Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT
| | - Diego Caballero
- Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT.,Department of Physics, Yale University, New Haven, CT
| | - Michael R Hinrichsen
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT
| | - Alejandro Virrueta
- Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT.,Department of Mechanical Engineering and Materials Science, Yale University, New Haven, CT
| | - Danielle M Williams
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT
| | - Corey S O'Hern
- Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT.,Department of Physics, Yale University, New Haven, CT.,Department of Mechanical Engineering and Materials Science, Yale University, New Haven, CT.,Department of Applied Physics, Yale University, New Haven, CT
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26
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Wahome N, Sully E, Singer C, Thomas JC, Hu L, Joshi SB, Volkin DB, Fang J, Karanicolas J, Jacobs DJ, Mantis NJ, Middaugh CR. Novel Ricin Subunit Antigens With Enhanced Capacity to Elicit Toxin-Neutralizing Antibody Responses in Mice. J Pharm Sci 2016; 105:1603-1613. [PMID: 26987947 PMCID: PMC4846473 DOI: 10.1016/j.xphs.2016.02.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Revised: 01/26/2016] [Accepted: 02/09/2016] [Indexed: 02/07/2023]
Abstract
RiVax is a candidate ricin toxin subunit vaccine antigen that has proven to be safe in human phase I clinical trials. In this study, we introduced double and triple cavity-filling point mutations into the RiVax antigen with the expectation that stability-enhancing modifications would have a beneficial effect on overall immunogenicity of the recombinant proteins. We demonstrate that 2 RiVax triple mutant derivatives, RB (V81L/C171L/V204I) and RC (V81I/C171L/V204I), when adsorbed to aluminum salts adjuvant and tested in a mouse prime-boost-boost regimen were 5- to 10-fold more effective than RiVax at eliciting toxin-neutralizing serum IgG antibody titers. Increased toxin neutralizing antibody values and seroconversion rates were evident at different antigen dosages and within 7 days after the first booster. Quantitative stability/flexibility relationships analysis revealed that the RB and RC mutations affect rigidification of regions spanning residues 98-103, which constitutes a known immunodominant neutralizing B-cell epitope. A more detailed understanding of the immunogenic nature of RB and RC may provide insight into the fundamental relationship between local protein stability and antibody reactivity.
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Affiliation(s)
- Newton Wahome
- Department of Pharmaceutical Chemistry, Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66047
| | - Erin Sully
- Division of Infectious Disease, Wadsworth Center, New York State Department of Health, Albany, New York 12208
| | - Christopher Singer
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, North Carolina 28223
| | - Justin C Thomas
- Department of Pharmaceutical Chemistry, Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66047
| | - Lei Hu
- Department of Pharmaceutical Chemistry, Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66047
| | - Sangeeta B Joshi
- Department of Pharmaceutical Chemistry, Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66047
| | - David B Volkin
- Department of Pharmaceutical Chemistry, Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66047
| | - Jianwen Fang
- Applied Bioinformatics Laboratory, Department of Medicinal Chemistry, University of Kansas, Lawrence, Kansas 66047
| | - John Karanicolas
- Department of Molecular Biosciences, Center for Computational Biology, University of Kansas, Lawrence, Kansas 66045
| | - Donald J Jacobs
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, North Carolina 28223.
| | - Nicholas J Mantis
- Division of Infectious Disease, Wadsworth Center, New York State Department of Health, Albany, New York 12208; Department of Biomedical Sciences, School of Public Health, University at Albany, Albany, New York 12201.
| | - C Russell Middaugh
- Department of Pharmaceutical Chemistry, Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66047.
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27
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Maximova T, Moffatt R, Ma B, Nussinov R, Shehu A. Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics. PLoS Comput Biol 2016; 12:e1004619. [PMID: 27124275 PMCID: PMC4849799 DOI: 10.1371/journal.pcbi.1004619] [Citation(s) in RCA: 132] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.
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Affiliation(s)
- Tatiana Maximova
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
| | - Ryan Moffatt
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
| | - Buyong Ma
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland, United States of America
| | - Ruth Nussinov
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland, United States of America
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
- Department of Biongineering, George Mason University, Fairfax, Virginia, United States of America
- School of Systems Biology, George Mason University, Manassas, Virginia, United States of America
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28
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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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29
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Stiebritz MT. MetREx: A protein design approach for the exploration of sequence-reactivity relationships in metalloenzymes. J Comput Chem 2015; 36:553-63. [DOI: 10.1002/jcc.23831] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Revised: 12/12/2014] [Accepted: 12/16/2014] [Indexed: 01/10/2023]
Affiliation(s)
- Martin T. Stiebritz
- Laboratorium für Physikalische Chemie, ETH Zürich; Vladimir-Prelog-Weg 2 CH-8093 Zürich Switzerland
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30
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Bhowmick A, Head-Gordon T. A Monte Carlo Method for Generating Side Chain Structural Ensembles. Structure 2015; 23:44-55. [DOI: 10.1016/j.str.2014.10.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2014] [Revised: 10/20/2014] [Accepted: 10/21/2014] [Indexed: 11/29/2022]
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31
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Stiel AC, Feldmeier K, Höcker B. Identification of protein scaffolds for enzyme design using scaffold selection. Methods Mol Biol 2014; 1216:183-196. [PMID: 25213416 DOI: 10.1007/978-1-4939-1486-9_9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The identification of suitable protein structures that can serve as scaffolds for the introduction of catalytic residues is crucial for the design of new enzymes. Here we describe how the automated and rapid scaffold search program ScaffoldSelection can be used to find the best starting points, namely protein structures that are most likely to tolerate the introduction and promote the proper formation of a specific catalytic motif.
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Affiliation(s)
- André C Stiel
- Max Planck Institute for Developmental Biology, Spemannstr. 35, 72076, Tübingen, Germany
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32
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Abaturov LV, Nosova NG. Protein conformational dynamics of crambin in crystal, solution and in the trajectories of molecular dynamics simulations. Biophysics (Nagoya-shi) 2013. [DOI: 10.1134/s0006350913040027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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33
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Mahajan S, de Brevern AG, Offmann B, Srinivasan N. Correlation between local structural dynamics of proteins inferred from NMR ensembles and evolutionary dynamics of homologues of known structure. J Biomol Struct Dyn 2013; 32:751-8. [PMID: 23730714 DOI: 10.1080/07391102.2013.789989] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Conformational changes in proteins are extremely important for their biochemical functions. Correlation between inherent conformational variations in a protein and conformational differences in its homologues of known structure is still unclear. In this study, we have used a structural alphabet called Protein Blocks (PBs). PBs are used to perform abstraction of protein 3-D structures into a 1-D strings of 16 alphabets (a-p) based on dihedral angles of overlapping pentapeptides. We have analyzed the variations in local conformations in terms of PBs represented in the ensembles of 801 protein structures determined using NMR spectroscopy. In the analysis of concatenated data over all the residues in all the NMR ensembles, we observe that the overall nature of inherent local structural variations in NMR ensembles is similar to the nature of local structural differences in homologous proteins with a high correlation coefficient of .94. High correlation at the alignment positions corresponding to helical and β-sheet regions is only expected. However, the correlation coefficient by considering only the loop regions is also quite high (.91). Surprisingly, segregated position-wise analysis shows that this high correlation does not hold true to loop regions at the structurally equivalent positions in NMR ensembles and their homologues of known structure. This suggests that the general nature of local structural changes is unique; however most of the local structural variations in loop regions of NMR ensembles do not correlate to their local structural differences at structurally equivalent positions in homologues.
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Affiliation(s)
- Swapnil Mahajan
- a Faculté des Sciences et Technologies, Université de La Réunion , F-97715 Saint Denis Messag Cedex 09, La Réunion , France
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34
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Abaturov LV, Nosova NG. Structure of crambin in solution, crystal and in the trajectories of molecular dynamics simulations. Biophysics (Nagoya-shi) 2013. [DOI: 10.1134/s0006350913030020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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35
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Thomas JC, O'Hara JM, Hu L, Gao FP, Joshi SB, Volkin DB, Brey RN, Fang J, Karanicolas J, Mantis NJ, Middaugh CR. Effect of single-point mutations on the stability and immunogenicity of a recombinant ricin A chain subunit vaccine antigen. Hum Vaccin Immunother 2013; 9:744-52. [PMID: 23563512 DOI: 10.4161/hv.22998] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
There is great interest in the design and development of highly thermostable and immunogenic protein subunit vaccines for biodefense. In this study, we used two orthogonal and complementary computational protein design approaches to generate a series of single-point mutants of RiVax, an attenuated recombinant ricin A chain (RTA) protein subunit vaccine antigen. As assessed by differential scanning calorimetry, the conformational stabilities of the designed mutants ranged from 4°C less stable to 4.5°C more stable than RiVax, depending on solution pH. Two more thermostable (V18P, C171L) and two less thermostable (T13V, S89T) mutants that displayed native-like secondary and tertiary structures (as determined by circular dichroism and fluorescence spectral analysis, respectively) were tested for their capacity to elicit RTA-specific antibodies and toxin-neutralizing activity. Following a prime-boost regimen, we found qualitative differences with respect to specific antibody titers and toxin neutralizing antibody levels induced by the different mutants. Upon a second boost with the more thermostable mutant C171L, a statistically significant increase in RTA-specific antibody titers was observed when compared with RiVax-immunized mice. Notably, the results indicate that single residue changes can be made to the RiVax antigen that increase its thermal stability without adversely impacting the efficacy of the vaccine.
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Affiliation(s)
- Justin C Thomas
- Macromolecule and Vaccine Stabilization Center; Department of Pharmaceutical Chemistry; University of Kansas; Lawrence, KS USA
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36
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Ollikainen N, Smith CA, Fraser JS, Kortemme T. Flexible backbone sampling methods to model and design protein alternative conformations. Methods Enzymol 2013; 523:61-85. [PMID: 23422426 DOI: 10.1016/b978-0-12-394292-0.00004-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Sampling alternative conformations is key to understanding how proteins work and engineering them for new functions. However, accurately characterizing and modeling protein conformational ensembles remain experimentally and computationally challenging. These challenges must be met before protein conformational heterogeneity can be exploited in protein engineering and design. Here, as a stepping stone, we describe methods to detect alternative conformations in proteins and strategies to model these near-native conformational changes based on backrub-type Monte Carlo moves in Rosetta. We illustrate how Rosetta simulations that apply backrub moves improve modeling of point mutant side-chain conformations, native side-chain conformational heterogeneity, functional conformational changes, tolerated sequence space, protein interaction specificity, and amino acid covariation across protein-protein interfaces. We include relevant Rosetta command lines and RosettaScripts to encourage the application of these types of simulations to other systems. Our work highlights that critical scoring and sampling improvements will be necessary to approximate conformational landscapes. Challenges for the future development of these methods include modeling conformational changes that propagate away from designed mutation sites and modulating backbone flexibility to predictively design functionally important conformational heterogeneity.
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Affiliation(s)
- Noah Ollikainen
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, California, USA
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37
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Chitsaz M, Mayo SL. GRID: a high-resolution protein structure refinement algorithm. J Comput Chem 2012; 34:445-50. [PMID: 23065773 DOI: 10.1002/jcc.23151] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2012] [Revised: 07/31/2012] [Accepted: 08/27/2012] [Indexed: 12/27/2022]
Abstract
The energy-based refinement of protein structures generated by fold prediction algorithms to atomic-level accuracy remains a major challenge in structural biology. Energy-based refinement is mainly dependent on two components: (1) sufficiently accurate force fields, and (2) efficient conformational space search algorithms. Focusing on the latter, we developed a high-resolution refinement algorithm called GRID. It takes a three-dimensional protein structure as input and, using an all-atom force field, attempts to improve the energy of the structure by systematically perturbing backbone dihedrals and side-chain rotamer conformations. We compare GRID to Backrub, a stochastic algorithm that has been shown to predict a significant fraction of the conformational changes that occur with point mutations. We applied GRID and Backrub to 10 high-resolution (≤ 2.8 Å) crystal structures from the Protein Data Bank and measured the energy improvements obtained and the computation times required to achieve them. GRID resulted in energy improvements that were significantly better than those attained by Backrub while expending about the same amount of computational resources. GRID resulted in relaxed structures that had slightly higher backbone RMSDs compared to Backrub relative to the starting crystal structures. The average RMSD was 0.25 ± 0.02 Å for GRID versus 0.14 ± 0.04 Å for Backrub. These relatively minor deviations indicate that both algorithms generate structures that retain their original topologies, as expected given the nature of the algorithms.
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Affiliation(s)
- Mohsen Chitsaz
- Biochemistry and Molecular Biophysics Option, California Institute of Technology, Pasadena, California 91125, USA
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38
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Prediction of mutational tolerance in HIV-1 protease and reverse transcriptase using flexible backbone protein design. PLoS Comput Biol 2012; 8:e1002639. [PMID: 22927804 PMCID: PMC3426558 DOI: 10.1371/journal.pcbi.1002639] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2010] [Accepted: 06/27/2012] [Indexed: 01/21/2023] Open
Abstract
Predicting which mutations proteins tolerate while maintaining their structure and function has important applications for modeling fundamental properties of proteins and their evolution; it also drives progress in protein design. Here we develop a computational model to predict the tolerated sequence space of HIV-1 protease reachable by single mutations. We assess the model by comparison to the observed variability in more than 50,000 HIV-1 protease sequences, one of the most comprehensive datasets on tolerated sequence space. We then extend the model to a second protein, reverse transcriptase. The model integrates multiple structural and functional constraints acting on a protein and uses ensembles of protein conformations. We find the model correctly captures a considerable fraction of protease and reverse-transcriptase mutational tolerance and shows comparable accuracy using either experimentally determined or computationally generated structural ensembles. Predictions of tolerated sequence space afforded by the model provide insights into stability-function tradeoffs in the emergence of resistance mutations and into strengths and limitations of the computational model. Many related protein sequences can be consistent with the structure and function of a given protein, suggesting that proteins may be quite robust to mutations. This tolerance to mutations is frequently exploited by pathogens. In particular, pathogens can rapidly evolve mutated proteins that have a new function - resistance against a therapeutic inhibitor - without abandoning other functions essential for the pathogen. This principle may also hold more generally: Proteins tolerant to mutational changes can more easily acquire new functions while maintaining their existing properties. The ability to predict the tolerance of proteins to mutation could thus help both to analyze the emergence of resistance mutations in pathogens and to engineer proteins with new functions. Here we develop a computational model to predict protein mutational tolerance towards point mutations accessible by single nucleotide changes, and validate it using two important pathogenic proteins and therapeutic targets: the protease and reverse transcriptase from HIV-1. The model provides insights into how resistance emerges and makes testable predictions on mutations that have not been seen yet. Similar models of mutational tolerance should be useful for characterizing and reengineering the functions of other proteins for which a three-dimensional structure is available.
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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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Keedy DA, Georgiev I, Triplett EB, Donald BR, Richardson DC, Richardson JS. The role of local backrub motions in evolved and designed mutations. PLoS Comput Biol 2012; 8:e1002629. [PMID: 22876172 PMCID: PMC3410847 DOI: 10.1371/journal.pcbi.1002629] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2011] [Accepted: 06/18/2012] [Indexed: 11/23/2022] Open
Abstract
Amino acid substitutions in protein structures often require subtle backbone adjustments that are difficult to model in atomic detail. An improved ability to predict realistic backbone changes in response to engineered mutations would be of great utility for the blossoming field of rational protein design. One model that has recently grown in acceptance is the backrub motion, a low-energy dipeptide rotation with single-peptide counter-rotations, that is coupled to dynamic two-state sidechain rotamer jumps, as evidenced by alternate conformations in very high-resolution crystal structures. It has been speculated that backrubs may facilitate sequence changes equally well as rotamer changes. However, backrub-induced shifts and experimental uncertainty are of similar magnitude for backbone atoms in even high-resolution structures, so comparison of wildtype-vs.-mutant crystal structure pairs is not sufficient to directly link backrubs to mutations. In this study, we use two alternative approaches that bypass this limitation. First, we use a quality-filtered structure database to aggregate many examples for precisely defined motifs with single amino acid differences, and find that the effectively amplified backbone differences closely resemble backrubs. Second, we directly apply a provably-accurate, backrub-enabled protein design algorithm to idealized versions of these motifs, and discover that the lowest-energy computed models match the average-coordinate experimental structures. These results support the hypothesis that backrubs participate in natural protein evolution and validate their continued use for design of synthetic proteins. Protein design has the potential to generate useful molecules for medicine and chemistry, including sensors, drugs, and catalysts for arbitrary reactions. When protein design is carried out starting from an experimentally determined structure, as is often the case, one important aspect to consider is backbone flexibility, because in response to a mutation the backbone often must shift slightly to reconcile the new sidechain with its environment. In principle, one may model the backbone in many ways, but not all are physically realistic or experimentally validated. Here we study the "backrub" motion, which has been previously documented in atomic detail, but only for sidechain movements within single structures. By a twopronged approach involving both structural bioinformatics and computation with a principled design algorithm, we demonstrate that backrubs are sufficient to explain the backbone differences between mutation-related sets of very precisely defined motifs from the protein structure database. Our findings illustrate that backrubs are useful for describing evolutionary sequence change and, by extension, suggest that they are also appropriate for rational protein design calculations.
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Affiliation(s)
- Daniel A Keedy
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, United States of America.
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41
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Murphy GS, Mills JL, Miley MJ, Machius M, Szyperski T, Kuhlman B. Increasing sequence diversity with flexible backbone protein design: the complete redesign of a protein hydrophobic core. Structure 2012; 20:1086-96. [PMID: 22632833 PMCID: PMC3372604 DOI: 10.1016/j.str.2012.03.026] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2011] [Revised: 02/15/2012] [Accepted: 03/30/2012] [Indexed: 01/07/2023]
Abstract
Protein design tests our understanding of protein stability and structure. Successful design methods should allow the exploration of sequence space not found in nature. However, when redesigning naturally occurring protein structures, most fixed backbone design algorithms return amino acid sequences that share strong sequence identity with wild-type sequences, especially in the protein core. This behavior places a restriction on functional space that can be explored and is not consistent with observations from nature, where sequences of low identity have similar structures. Here, we allow backbone flexibility during design to mutate every position in the core (38 residues) of a four-helix bundle protein. Only small perturbations to the backbone, 1-2 Å, were needed to entirely mutate the core. The redesigned protein, DRNN, is exceptionally stable (melting point >140°C). An NMR and X-ray crystal structure show that the side chains and backbone were accurately modeled (all-atom RMSD = 1.3 Å).
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Affiliation(s)
- Grant S. Murphy
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-3290, USA
| | - Jeffrey L. Mills
- Department of Chemistry, State University of New York at Buffalo, Buffalo, NY, 14260, USA
,Northeast Structural Genomics Consortium
| | - Michael J. Miley
- Center for Structural Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Mischa Machius
- Center for Structural Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Thomas Szyperski
- Department of Chemistry, State University of New York at Buffalo, Buffalo, NY, 14260, USA
,Northeast Structural Genomics Consortium
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7260, USA
,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
,corresponding author. , Phone: 919-843-0188, Fax: 919-966-2852
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Shapovalov MV, Dunbrack RL. A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. Structure 2011; 19:844-58. [PMID: 21645855 DOI: 10.1016/j.str.2011.03.019] [Citation(s) in RCA: 636] [Impact Index Per Article: 48.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2010] [Revised: 03/19/2011] [Accepted: 03/22/2011] [Indexed: 11/15/2022]
Abstract
Rotamer libraries are used in protein structure determination, prediction, and design. The backbone-dependent rotamer library consists of rotamer frequencies, mean dihedral angles, and variances as a function of the backbone dihedral angles. Structure prediction and design methods that employ backbone flexibility would strongly benefit from smoothly varying probabilities and angles. A new version of the backbone-dependent rotamer library has been developed using adaptive kernel density estimates for the rotamer frequencies and adaptive kernel regression for the mean dihedral angles and variances. This formulation allows for evaluation of the rotamer probabilities, mean angles, and variances as a smooth and continuous function of phi and psi. Continuous probability density estimates for the nonrotameric degrees of freedom of amides, carboxylates, and aromatic side chains have been modeled as a function of the backbone dihedrals and rotamers of the remaining degrees of freedom. New backbone-dependent rotamer libraries at varying levels of smoothing are available from http://dunbrack.fccc.edu.
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Affiliation(s)
- Maxim V Shapovalov
- Institute for Cancer Research, Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111, USA
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43
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Luo X, Lü Q, Wu H, Yang L, Huang X, Qian P, Fu G. Automatic prediction of flexible regions improves the accuracy of protein-protein docking models. J Mol Model 2011; 18:2199-208. [DOI: 10.1007/s00894-011-1231-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2011] [Accepted: 08/22/2011] [Indexed: 11/28/2022]
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Smith CA, Kortemme T. Predicting the tolerated sequences for proteins and protein interfaces using RosettaBackrub flexible backbone design. PLoS One 2011; 6:e20451. [PMID: 21789164 PMCID: PMC3138746 DOI: 10.1371/journal.pone.0020451] [Citation(s) in RCA: 77] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2011] [Accepted: 04/20/2011] [Indexed: 11/18/2022] Open
Abstract
Predicting the set of sequences that are tolerated by a protein or protein interface, while maintaining a desired function, is useful for characterizing protein interaction specificity and for computationally designing sequence libraries to engineer proteins with new functions. Here we provide a general method, a detailed set of protocols, and several benchmarks and analyses for estimating tolerated sequences using flexible backbone protein design implemented in the Rosetta molecular modeling software suite. The input to the method is at least one experimentally determined three-dimensional protein structure or high-quality model. The starting structure(s) are expanded or refined into a conformational ensemble using Monte Carlo simulations consisting of backrub backbone and side chain moves in Rosetta. The method then uses a combination of simulated annealing and genetic algorithm optimization methods to enrich for low-energy sequences for the individual members of the ensemble. To emphasize certain functional requirements (e.g. forming a binding interface), interactions between and within parts of the structure (e.g. domains) can be reweighted in the scoring function. Results from each backbone structure are merged together to create a single estimate for the tolerated sequence space. We provide an extensive description of the protocol and its parameters, all source code, example analysis scripts and three tests applying this method to finding sequences predicted to stabilize proteins or protein interfaces. The generality of this method makes many other applications possible, for example stabilizing interactions with small molecules, DNA, or RNA. Through the use of within-domain reweighting and/or multistate design, it may also be possible to use this method to find sequences that stabilize particular protein conformations or binding interactions over others.
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Affiliation(s)
- Colin A. Smith
- Graduate Program in Biological and Medical Informatics, University of California San Francisco, San Francisco, California, United States of America
- California Institute for Quantitative Biosciences, San Francisco, California, United States of America
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
| | - Tanja Kortemme
- Graduate Program in Biological and Medical Informatics, University of California San Francisco, San Francisco, California, United States of America
- California Institute for Quantitative Biosciences, San Francisco, California, United States of America
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- * E-mail:
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45
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Babor M, Mandell DJ, Kortemme T. Assessment of flexible backbone protein design methods for sequence library prediction in the therapeutic antibody Herceptin-HER2 interface. Protein Sci 2011; 20:1082-9. [PMID: 21465611 DOI: 10.1002/pro.632] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2010] [Revised: 03/15/2011] [Accepted: 03/16/2011] [Indexed: 01/28/2023]
Abstract
Computational protein design methods can complement experimental screening and selection techniques by predicting libraries of low-energy sequences compatible with a desired structure and function. Incorporating backbone flexibility in computational design allows conformational adjustments that should broaden the range of predicted low-energy sequences. Here, we evaluate computational predictions of sequence libraries from different protocols for modeling backbone flexibility using the complex between the therapeutic antibody Herceptin and its target human epidermal growth factor receptor 2 (HER2) as a model system. Within the program RosettaDesign, three methods are compared: The first two use ensembles of structures generated by Monte Carlo protocols for near-native conformational sampling: kinematic closure (KIC) and backrub, and the third method uses snapshots from molecular dynamics (MD) simulations. KIC or backrub methods were better able to identify the amino acid residues experimentally observed by phage display in the Herceptin-HER2 interface than MD snapshots, which generated much larger conformational and sequence diversity. KIC and backrub, as well as fixed backbone simulations, captured the key mutation Asp98Trp in Herceptin, which leads to a further threefold affinity improvement of the already subnanomolar parental Herceptin-HER2 interface. Modeling subtle backbone conformational changes may assist in the design of sequence libraries for improving the affinity of antibody-antigen interfaces and could be suitable for other protein complexes for which structural information is available.
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Affiliation(s)
- Mariana Babor
- California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, California 94158-2330, USA
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46
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Morin A, Meiler J, Mizoue LS. Computational design of protein-ligand interfaces: potential in therapeutic development. Trends Biotechnol 2011; 29:159-66. [PMID: 21295366 DOI: 10.1016/j.tibtech.2011.01.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2010] [Revised: 12/22/2010] [Accepted: 01/05/2011] [Indexed: 01/16/2023]
Abstract
Computational design of protein-ligand interfaces finds optimal amino acid sequences within a small-molecule binding site of a protein for tight binding of a specific small molecule. It requires a search algorithm that can rapidly sample the vast sequence and conformational space, and a scoring function that can identify low energy designs. This review focuses on recent advances in computational design methods and their application to protein-small molecule binding sites. Strategies for increasing affinity, altering specificity, creating broad-spectrum binding, and building novel enzymes from scratch are described. Future prospects for applications in drug development are discussed, including limitations that will need to be overcome to achieve computational design of protein therapeutics with novel modes of action.
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Affiliation(s)
- Andrew Morin
- Departments of Chemistry, Pharmacology, and Biomedical Informatics, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, TN 37235, USA
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Ding F, Yin S, Dokholyan NV. Rapid flexible docking using a stochastic rotamer library of ligands. J Chem Inf Model 2011; 50:1623-32. [PMID: 20712341 DOI: 10.1021/ci100218t] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Existing flexible docking approaches model the ligand and receptor flexibility either separately or in a loosely coupled manner, which captures the conformational changes inefficiently. Here, we propose a flexible docking approach, MedusaDock, which models both ligand and receptor flexibility simultaneously with sets of discrete rotamers. We developed an algorithm to build the ligand rotamer library "on-the-fly" during docking simulations. MedusaDock benchmarks demonstrate a rapid sampling efficiency and high prediction accuracy in both self- (to the cocrystallized state) and cross-docking (to a state cocrystallized with a different ligand), the latter of which mimics the virtual screening procedure in computational drug discovery. We also perform a virtual screening test of four flexible kinase targets, including cyclin-dependent kinase 2, vascular endothelial growth factor receptor 2, HIV reverse transcriptase, and HIV protease. We find significant improvements of virtual screening enrichments when compared to rigid-receptor methods. The predictive power of MedusaDock in cross-docking and preliminary virtual-screening benchmarks highlights the importance to model both ligand and receptor flexibility simultaneously in computational docking.
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Affiliation(s)
- Feng Ding
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, North Carolina 27599, USA
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48
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Accounting for conformational entropy in predicting binding free energies of protein-protein interactions. Proteins 2010; 79:444-62. [DOI: 10.1002/prot.22894] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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49
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Hernández G, Anderson JS, LeMaster DM. Assessing the native state conformational distribution of ubiquitin by peptide acidity. Biophys Chem 2010; 153:70-82. [PMID: 21055867 DOI: 10.1016/j.bpc.2010.10.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2010] [Revised: 10/08/2010] [Accepted: 10/10/2010] [Indexed: 11/18/2022]
Abstract
At equilibrium, every energetically feasible conformation of a protein occurs with a non-zero probability. Quantitative analysis of protein flexibility is thus synonymous with determining the proper Boltzmann-weighting of this conformational distribution. The exchange reactivity of solvent-exposed amide hydrogens greatly varies with conformation, while the short-lived peptide anion intermediate implies an insensitivity to the dynamics of conformational motion. Amides that are well-exposed in model conformational ensembles of ubiquitin vary a million-fold in exchange rates which continuum dielectric methods can predict with an rmsd of 3. However, the exchange rates for many of the more rarely exposed amides are markedly overestimated in the PDB-deposited 2K39 and 2KN5 ubiquitin ensembles, while the 2NR2 ensemble predictions are largely consistent with those of the Boltzmann-weighted conformational distribution sampled at the level of 1%. The correlation between the fraction of solvent-accessible conformations for a given amide hydrogen and the exchange rate constant for that residue provides a useful monitor of the degree of completeness with which a given ensemble has sampled the energetically accessible conformational space. These exchange predictions correlate with the degree to which each ensemble deviates from a set of 46 ubiquitin X-ray structures. Kolmogorov-Smirnov analysis for the distribution of intra- and inter-ensemble pairwise structural rmsd values assisted the identification of a subensemble of 2K39 that eliminates the overestimations of hydrogen exchange rates observed for the full ensemble. The relative merits of incorporating experimental restraints into the conformational sampling process are compared to using these restraints as filters to select subpopulations consistent with the experimental data.
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Affiliation(s)
- Griselda Hernández
- Wadsworth Center, New York State Department of Health, University at Albany-SUNY, 12201, USA
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50
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Friedland GD, Kortemme T. Designing ensembles in conformational and sequence space to characterize and engineer proteins. Curr Opin Struct Biol 2010; 20:377-84. [DOI: 10.1016/j.sbi.2010.02.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2010] [Accepted: 02/19/2010] [Indexed: 11/16/2022]
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