1
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Alford RF, Leaver-Fay A, Jeliazkov JR, O'Meara MJ, DiMaio FP, Park H, Shapovalov MV, Renfrew PD, Mulligan VK, Kappel K, Labonte JW, Pacella MS, Bonneau R, Bradley P, Dunbrack RL, Das R, Baker D, Kuhlman B, Kortemme T, Gray JJ. Correction to "The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design". J Chem Theory Comput 2022; 18:4594. [PMID: 35667008 DOI: 10.1021/acs.jctc.2c00500] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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2
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Albanese KI, Leaver-Fay A, Treacy JW, Park R, Houk KN, Kuhlman B, Waters ML. Comparative Analysis of Sulfonium-π, Ammonium-π, and Sulfur-π Interactions and Relevance to SAM-Dependent Methyltransferases. J Am Chem Soc 2022; 144:2535-2545. [PMID: 35108000 PMCID: PMC8923077 DOI: 10.1021/jacs.1c09902] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We report the measurement and analysis of sulfonium-π, thioether-π, and ammonium-π interactions in a β-hairpin peptide model system, coupled with computational investigation and PDB analysis. These studies indicated that the sulfonium-π interaction is the strongest and that polarizability contributes to the stronger interaction with sulfonium relative to ammonium. Computational studies demonstrate that differences in solvation of the trimethylsulfonium versus the trimethylammonium group also contribute to the stronger sulfonium-π interaction. In comparing sulfonium-π versus sulfur-π interactions in proteins, analysis of SAM- and SAH-bound enzymes in the PDB suggests that aromatic residues are enriched in close proximity to the sulfur of both SAM and SAH, but the populations of aromatic interactions of the two cofactors are not significantly different, with the exception of the Me-π interactions in SAM, which are the most prevalent interaction in SAM but are not possible for SAH. This suggests that the weaker interaction energies due to loss of the cation-π interaction in going from SAM to SAH may contribute to turnover of the cofactor.
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Affiliation(s)
- Katherine I. Albanese
- Department of Chemistry, CB 3290, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
| | - Joseph W. Treacy
- Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, 90095-1569
| | - Rodney Park
- Department of Biochemistry and Biophysics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
| | - K. N. Houk
- Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, 90095-1569
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
| | - Marcey L. Waters
- Department of Chemistry, CB 3290, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
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3
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Koehler Leman J, Lyskov S, Lewis SM, Adolf-Bryfogle J, Alford RF, Barlow K, Ben-Aharon Z, Farrell D, Fell J, Hansen WA, Harmalkar A, Jeliazkov J, Kuenze G, Krys JD, Ljubetič A, Loshbaugh AL, Maguire J, Moretti R, Mulligan VK, Nance ML, Nguyen PT, Ó Conchúir S, Roy Burman SS, Samanta R, Smith ST, Teets F, Tiemann JKS, Watkins A, Woods H, Yachnin BJ, Bahl CD, Bailey-Kellogg C, Baker D, Das R, DiMaio F, Khare SD, Kortemme T, Labonte JW, Lindorff-Larsen K, Meiler J, Schief W, Schueler-Furman O, Siegel JB, Stein A, Yarov-Yarovoy V, Kuhlman B, Leaver-Fay A, Gront D, Gray JJ, Bonneau R. Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks. Nat Commun 2021; 12:6947. [PMID: 34845212 PMCID: PMC8630030 DOI: 10.1038/s41467-021-27222-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 11/02/2021] [Indexed: 01/14/2023] Open
Abstract
Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Steven M Lewis
- Cyrus Biotechnology, 1201 Second Ave, Suite 900, Seattle, WA, 98101, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kyle Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Ziv Ben-Aharon
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Daniel Farrell
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Jason Fell
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - William A Hansen
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jeliazko Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - Justyna D Krys
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Ajasja Ljubetič
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Amanda L Loshbaugh
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA
| | - Morgan L Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Phuong T Nguyen
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Shane Ó Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Shannon T Smith
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Frank Teets
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Johanna K S Tiemann
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Hope Woods
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Brahm J Yachnin
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Boston, MA, 02115, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | | | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Sagar D Khare
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - William Schief
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Justin B Siegel
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Brian Kuhlman
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Andrew Leaver-Fay
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
- Department of Computer Science, New York University, New York, NY, 10003, USA.
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4
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Le KH, Adolf-Bryfogle J, Klima JC, Lyskov S, Labonte J, Bertolani S, Burman SSR, Leaver-Fay A, Weitzner B, Maguire J, Rangan R, Adrianowycz MA, Alford RF, Adal A, Nance ML, Wu Y, Willis J, Kulp DW, Das R, Dunbrack RL, Schief W, Kuhlman B, Siegel JB, Gray JJ. PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design. Biophysicist (Rockv) 2021; 2:108-122. [PMID: 35128343 DOI: 10.35459/tbp.2019.000147] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of sixteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.
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Affiliation(s)
- Kathy H Le
- T. C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, United States
| | - Jason C Klima
- Institute for Protein Design, University of Washington, Seattle, Washington, United States.,Department of Biochemistry, University of Washington, Seattle, Washington, United States.,Lyell Immunopharma, Inc., Seattle, Washington, United States
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jason Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States.,Department of Chemistry, Franklin & Marshall College, Lancaster, Pennsylvania, United States
| | - Steven Bertolani
- Department of Chemistry, Department of Biochemistry and Molecular Medicine, Genome Center, University of California, Davis, Davis, California, United States
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Brian Weitzner
- Institute for Protein Design, University of Washington, Seattle, Washington, United States.,Department of Biochemistry, University of Washington, Seattle, Washington, United States.,Lyell Immunopharma, Inc., Seattle, Washington, United States
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Ramya Rangan
- Program in Biophysics, Stanford University, Stanford, California, United States
| | - Matt A Adrianowycz
- Program in Biophysics, Stanford University, Stanford, California, United States
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aleexsan Adal
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Morgan L Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
| | - Yuanhan Wu
- Vaccine and Immunotherapy Center, Wistar Institute, Philadelphia, Pennsylvania, United States
| | - Jordan Willis
- RubrYc Therapeutics, San Ramon, California, United States
| | - Daniel W Kulp
- Vaccine and Immunotherapy Center, Wistar Institute, Philadelphia, Pennsylvania, United States
| | - Rhiju Das
- Program in Biophysics, Stanford University, Stanford, California, United States
| | | | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, United States
| | - Brian Kuhlman
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States.,Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Justin B Siegel
- Department of Chemistry, Department of Biochemistry and Molecular Medicine, Genome Center, University of California, Davis, Davis, California, United States
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States.,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
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5
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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: 373] [Impact Index Per Article: 93.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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6
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Koehler Leman J, Weitzner BD, Renfrew PD, Lewis SM, Moretti R, Watkins AM, Mulligan VK, Lyskov S, Adolf-Bryfogle J, Labonte JW, Krys J, Bystroff C, Schief W, Gront D, Schueler-Furman O, Baker D, Bradley P, Dunbrack R, Kortemme T, Leaver-Fay A, Strauss CEM, Meiler J, Kuhlman B, Gray JJ, Bonneau R. Better together: Elements of successful scientific software development in a distributed collaborative community. PLoS Comput Biol 2020; 16:e1007507. [PMID: 32365137 PMCID: PMC7197760 DOI: 10.1371/journal.pcbi.1007507] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, United States of America
- Dept of Biology, New York University, New York, NY, United States of America
| | - Brian D. Weitzner
- Dept of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- Dept of Biochemistry, University of Washington, Seattle, WA, United States of America
- Institute for Protein Design, University of Washington, Seattle, WA, United States of America
- Lyell Immunopharma, Seattle, WA, United States of America
| | - P. Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, United States of America
| | - Steven M. Lewis
- Dept of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
- Dept of Biochemistry, Duke University, Durham, NC, United States of America
- Cyrus Biotechnology, Seattle, WA United States of America
| | - Rocco Moretti
- Dept of Chemistry, Vanderbilt University, Nashville, TN, United States of America
| | - Andrew M. Watkins
- Dept of Biochemistry, Stanford University School of Medicine, Stanford CA, United States of America
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, United States of America
- Dept of Biochemistry, University of Washington, Seattle, WA, United States of America
- Institute for Protein Design, University of Washington, Seattle, WA, United States of America
| | - Sergey Lyskov
- Dept of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Jared Adolf-Bryfogle
- Dept of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States of America
| | - Jason W. Labonte
- Dept of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- Dept of Chemistry, Franklin & Marshall College, Lancaster, PA, United States of America
| | - Justyna Krys
- Dept of Chemistry, University of Warsaw, Warsaw, Poland
| | | | - Christopher Bystroff
- Dept of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States of America
| | - William Schief
- Dept of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States of America
| | - Dominik Gront
- Dept of Chemistry, University of Warsaw, Warsaw, Poland
| | - Ora Schueler-Furman
- Dept of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - David Baker
- Dept of Biochemistry, University of Washington, Seattle, WA, United States of America
- Institute for Protein Design, University of Washington, Seattle, WA, United States of America
| | - Philip Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia PA, United States of America
| | - Tanja Kortemme
- Dept of Bioengineering and Therapeutic Sciences, University of California San Francisco, CA, United States of America
| | - Andrew Leaver-Fay
- Dept of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Charlie E. M. Strauss
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States of America
| | - Jens Meiler
- Depts of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States of America
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, United States of America
- Institute for Drug Discovery, Leipzig University, Leipzig, Germany
| | - Brian Kuhlman
- Dept of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Jeffrey J. Gray
- Dept of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, United States of America
- Dept of Biology, New York University, New York, NY, United States of America
- Dept of Computer Science, New York University, New York, NY, United States of America
- Center for Data Science, New York University, New York, NY, United States of America
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7
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Kleffner R, Flatten J, Leaver-Fay A, Baker D, Siegel JB, Khatib F, Cooper S. Foldit Standalone: a video game-derived protein structure manipulation interface using Rosetta. Bioinformatics 2018; 33:2765-2767. [PMID: 28481970 PMCID: PMC5860063 DOI: 10.1093/bioinformatics/btx283] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2016] [Accepted: 05/04/2017] [Indexed: 11/24/2022] Open
Abstract
Summary Foldit Standalone is an interactive graphical interface to the Rosetta molecular modeling package. In contrast to most command-line or batch interactions with Rosetta, Foldit Standalone is designed to allow easy, real-time, direct manipulation of protein structures, while also giving access to the extensive power of Rosetta computations. Derived from the user interface of the scientific discovery game Foldit (itself based on Rosetta), Foldit Standalone has added more advanced features and removed the competitive game elements. Foldit Standalone was built from the ground up with a custom rendering and event engine, configurable visualizations and interactions driven by Rosetta. Foldit Standalone contains, among other features: electron density and contact map visualizations, multiple sequence alignment tools for template-based modeling, rigid body transformation controls, RosettaScripts support and an embedded Lua interpreter. Availability and Implementation Foldit Standalone is available for download at https://fold.it/standalone, under the Rosetta license, which is free for academic and non-profit users. It is implemented in cross-platform C ++ and binary executables are available for Windows, macOS and Linux.
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Affiliation(s)
- Robert Kleffner
- College of Computer and Information Science, Northeastern University, Boston, MA 02115, USA
| | - Jeff Flatten
- Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - David Baker
- Department of Biochemistry.,Institute for Protein Design.,Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - Justin B Siegel
- Genome Center.,Department of Chemistry.,Department of Biochemistry and Molecular Medicine, University of California Davis, Davis, CA 95616, USA
| | - Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA
| | - Seth Cooper
- College of Computer and Information Science, Northeastern University, Boston, MA 02115, USA
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8
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Alford RF, Leaver-Fay A, Gonzales L, Dolan EL, Gray JJ. A cyber-linked undergraduate research experience in computational biomolecular structure prediction and design. PLoS Comput Biol 2017; 13:e1005837. [PMID: 29216185 PMCID: PMC5720517 DOI: 10.1371/journal.pcbi.1005837] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Computational biology is an interdisciplinary field, and many computational biology research projects involve distributed teams of scientists. To accomplish their work, these teams must overcome both disciplinary and geographic barriers. Introducing new training paradigms is one way to facilitate research progress in computational biology. Here, we describe a new undergraduate program in biomolecular structure prediction and design in which students conduct research at labs located at geographically-distributed institutions while remaining connected through an online community. This 10-week summer program begins with one week of training on computational biology methods development, transitions to eight weeks of research, and culminates in one week at the Rosetta annual conference. To date, two cohorts of students have participated, tackling research topics including vaccine design, enzyme design, protein-based materials, glycoprotein modeling, crowd-sourced science, RNA processing, hydrogen bond networks, and amyloid formation. Students in the program report outcomes comparable to students who participate in similar in-person programs. These outcomes include the development of a sense of community and increases in their scientific self-efficacy, scientific identity, and science values, all predictors of continuing in a science research career. Furthermore, the program attracted students from diverse backgrounds, which demonstrates the potential of this approach to broaden the participation of young scientists from backgrounds traditionally underrepresented in computational biology.
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Affiliation(s)
- Rebecca F. Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Lynda Gonzales
- Texas Institute for Discovery Education in Science, University of Texas, Austin, Texas, United States of America
| | - Erin L. Dolan
- Texas Institute for Discovery Education in Science, University of Texas, Austin, Texas, United States of America
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, Georgia, United States of America
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland, United States of America
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9
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Froning KJ, Leaver-Fay A, Wu X, Phan S, Gao L, Huang F, Pustilnik A, Bacica M, Houlihan K, Chai Q, Fitchett JR, Hendle J, Kuhlman B, Demarest SJ. Computational design of a specific heavy chain/κ light chain interface for expressing fully IgG bispecific antibodies. Protein Sci 2017; 26:2021-2038. [PMID: 28726352 DOI: 10.1002/pro.3240] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 07/12/2017] [Accepted: 07/13/2017] [Indexed: 12/31/2022]
Abstract
The use of bispecific antibodies (BsAbs) to treat human diseases is on the rise. Increasingly complex and powerful therapeutic mechanisms made possible by BsAbs are spurring innovation of novel BsAb formats and methods for their production. The long-lived in vivo pharmacokinetics, optimal biophysical properties and potential effector functions of natural IgG monoclonal (and monospecific) antibodies has resulted in a push to generate fully IgG BsAb formats with the same quaternary structure as monoclonal IgGs. The production of fully IgG BsAbs is challenging because of the highly heterogeneous pairing of heavy chains (HCs) and light chains (LCs) when produced in mammalian cells with two IgG HCs and two LCs. A solution to the HC heterodimerization aspect of IgG BsAb production was first discovered two decades ago; however, addressing the LC mispairing issue has remained intractable until recently. Here, we use computational and rational engineering to develop novel designs to the HC/LC pairing issue, and particularly for κ LCs. Crystal structures of these designs highlight the interactions that provide HC/LC specificity. We produce and characterize multiple fully IgG BsAbs using these novel designs. We demonstrate the importance of specificity engineering in both the variable and constant domains to achieve robust HC/LC specificity within all the BsAbs. These solutions facilitate the production of fully IgG BsAbs for clinical use.
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Affiliation(s)
- K J Froning
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, California, 92121
| | - A Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - X Wu
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, California, 92121
| | - S Phan
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, California, 92121
| | - L Gao
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, California, 92121
| | - F Huang
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, California, 92121
| | - A Pustilnik
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, California, 92121
| | - M Bacica
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, California, 92121
| | - K Houlihan
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Q Chai
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, California, 92121
| | - J R Fitchett
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, California, 92121
| | - J Hendle
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, California, 92121
| | - B Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - S J Demarest
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, California, 92121
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10
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Alford RF, Leaver-Fay A, Jeliazkov JR, O'Meara MJ, DiMaio FP, Park H, Shapovalov MV, Renfrew PD, Mulligan VK, Kappel K, Labonte JW, Pacella MS, Bonneau R, Bradley P, Dunbrack RL, Das R, Baker D, Kuhlman B, Kortemme T, Gray JJ. The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. J Chem Theory Comput 2017; 13:3031-3048. [PMID: 28430426 DOI: 10.1021/acs.jctc.7b00125] [Citation(s) in RCA: 750] [Impact Index Per Article: 107.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta's success is the energy function: a model parametrized from small-molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, called the Rosetta Energy Function 2015 (REF15). Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend its capabilities from soluble proteins to also include membrane proteins, peptides containing noncanonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.
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Affiliation(s)
- Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University , 3400 North Charles Street, Baltimore, Maryland 21218, United States
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill , 120 Mason Farm Road, Chapel Hill, North Carolina 27599, United States
| | - Jeliazko R Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University , 3400 North Charles Street, Baltimore, Maryland 21218, United States
| | - Matthew J O'Meara
- Department of Pharmaceutical Chemistry, University of California at San Francisco , 1700 Fourth Street, San Francisco, California 94158, United States
| | - Frank P DiMaio
- Department of Biochemistry, University of Washington , J-Wing, Health Sciences Building, Box 357350, Seattle, Washington 98195, United States
| | - Hahnbeom Park
- Department of Biochemistry, University of Washington , Molecular Engineering and Sciences, Box 351655, 3946 West Stevens Way NE, Seattle, Washington 98195, United States
| | - Maxim V Shapovalov
- Institute for Cancer Research, Fox Chase Cancer Center , 333 Cottman Avenue, Philadelphia, Pennsylvania 19111, United States
| | - P Douglas Renfrew
- Department of Biology, Center for Genomics and Systems Biology, New York University , 100 Washington Square East, New York, New York 10003, United States.,Center for Computational Biology, Flatiron Institute, Simons Foundation , 162 Fifth Avenue, New York, New York 10010, United States
| | - Vikram K Mulligan
- Department of Biochemistry, University of Washington , Molecular Engineering and Sciences, Box 351655, 3946 West Stevens Way NE, Seattle, Washington 98195, United States
| | - Kalli Kappel
- Biophysics Program, Stanford University , 450 Serra Mall, Stanford, California 94305, United States
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University , 3400 North Charles Street, Baltimore, Maryland 21218, United States
| | - Michael S Pacella
- Department of Biomedical Engineering, Johns Hopkins University , 3400 North Charles Street, Baltimore, Maryland 21218, United States
| | - Richard Bonneau
- Department of Biology, Center for Genomics and Systems Biology, New York University , 100 Washington Square East, New York, New York 10003, United States.,Center for Computational Biology, Flatiron Institute, Simons Foundation , 162 Fifth Avenue, New York, New York 10010, United States
| | - Philip Bradley
- Computational Biology Program, Fred Hutchinson Cancer Research Center , 1100 Fairview Avenue North, Seattle, Washington 98109, United States
| | - Roland L Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center , 333 Cottman Avenue, Philadelphia, Pennsylvania 19111, United States
| | - Rhiju Das
- Biophysics Program, Stanford University , 450 Serra Mall, Stanford, California 94305, United States
| | - David Baker
- Department of Biochemistry, University of Washington , Molecular Engineering and Sciences, Box 351655, 3946 West Stevens Way NE, Seattle, Washington 98195, United States.,Howard Hughes Medical Institute, University of Washington , Seattle, Washington 98195, United States
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill , 120 Mason Farm Road, Chapel Hill, North Carolina 27599, United States
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco , San Francisco, California 94158, United States
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University , 3400 North Charles Street, Baltimore, Maryland 21218, United States.,Program in Molecular Biophysics, Johns Hopkins University , 3400 North Charles Street, Baltimore, Maryland 21218, United States
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11
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Alford RF, Leaver-Fay A, Jeliazkov JR, Pacella MS, Kuhlman B, Kortemme T, Gray JJ. A Deep-Dive into the Rosetta Energy Function for Biological Macromolecules. Biophys J 2017. [DOI: 10.1016/j.bpj.2016.11.1078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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12
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Leaver-Fay A, Froning KJ, Atwell S, Aldaz H, Pustilnik A, Lu F, Huang F, Yuan R, Hassanali S, Chamberlain AK, Fitchett JR, Demarest SJ, Kuhlman B. Computationally Designed Bispecific Antibodies using Negative State Repertoires. Structure 2016; 24:641-651. [PMID: 26996964 DOI: 10.1016/j.str.2016.02.013] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Revised: 02/04/2016] [Accepted: 02/17/2016] [Indexed: 12/27/2022]
Abstract
A challenge in the structure-based design of specificity is modeling the negative states, i.e., the complexes that you do not want to form. This is a difficult problem because mutations predicted to destabilize the negative state might be accommodated by small conformational rearrangements. To overcome this challenge, we employ an iterative strategy that cycles between sequence design and protein docking in order to build up an ensemble of alternative negative state conformations for use in specificity prediction. We have applied our technique to the design of heterodimeric CH3 interfaces in the Fc region of antibodies. Combining computationally and rationally designed mutations produced unique designs with heterodimer purities greater than 90%. Asymmetric Fc crystallization was able to resolve the interface mutations; the heterodimer structures confirmed that the interfaces formed as designed. With these CH3 mutations, and those made at the heavy-/light-chain interface, we demonstrate one-step synthesis of four fully IgG-bispecific antibodies.
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Affiliation(s)
- Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Campus Box 7260, Chapel Hill, NC 27599, USA
| | - Karen J Froning
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Shane Atwell
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Hector Aldaz
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Anna Pustilnik
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Frances Lu
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Flora Huang
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Richard Yuan
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Saleema Hassanali
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Aaron K Chamberlain
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Jonathan R Fitchett
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Stephen J Demarest
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA.
| | - Brian Kuhlman
- Department of Biochemistry, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Campus Box 7260, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, USA.
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13
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O’Meara MJ, Leaver-Fay A, Tyka M, Stein A, Houlihan K, DiMaio F, Bradley P, Kortemme T, Baker D, Snoeyink J, Kuhlman B. Combined covalent-electrostatic model of hydrogen bonding improves structure prediction with Rosetta. J Chem Theory Comput 2015; 11:609-22. [PMID: 25866491 PMCID: PMC4390092 DOI: 10.1021/ct500864r] [Citation(s) in RCA: 169] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Interactions between polar atoms are challenging to model because at very short ranges they form hydrogen bonds (H-bonds) that are partially covalent in character and exhibit strong orientation preferences; at longer ranges the orientation preferences are lost, but significant electrostatic interactions between charged and partially charged atoms remain. To simultaneously model these two types of behavior, we refined an orientation dependent model of hydrogen bonds [Kortemme et al. J. Mol. Biol. 2003, 326, 1239] used by the molecular modeling program Rosetta and then combined it with a distance-dependent Coulomb model of electrostatics. The functional form of the H-bond potential is physically motivated and parameters are fit so that H-bond geometries that Rosetta generates closely resemble H-bond geometries in high-resolution crystal structures. The combined potentials improve performance in a variety of scientific benchmarks including decoy discrimination, side chain prediction, and native sequence recovery in protein design simulations and establishes a new standard energy function for Rosetta.
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Affiliation(s)
- Matthew J. O’Meara
- Department of Computer Science, University of North Carolina, 201 S Columbia St. Chapel Hill, North Carolina 27599, United States
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina, 120 Mason Farm Rd Chapel Hill, North Carolina 27599, United States
| | - Mike Tyka
- Google Inc., 1600 Amphitheatre Parkway Mountain View, California 94043, United States
| | - Amelie Stein
- Department of Bioengineering and Therapeutic Science, University of California San Francisco, 513 Parnassus Avenue San Francisco, California 94143, United States
| | - Kevin Houlihan
- Department of Biochemistry and Biophysics, University of North Carolina, 120 Mason Farm Rd Chapel Hill, North Carolina 27599, United States
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, 1705 North East Pacific Street Seattle Washington 98195, United States
| | - Philip Bradley
- Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle Washington 98109, United States
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Science, University of California San Francisco, 513 Parnassus Avenue San Francisco, California 94143, United States
| | - David Baker
- Department of Biochemistry, University of Washington, 1705 North East Pacific Street Seattle Washington 98195, United States
| | - Jack Snoeyink
- Department of Computer Science, University of North Carolina, 201 S Columbia St. Chapel Hill, North Carolina 27599, United States
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina, 120 Mason Farm Rd Chapel Hill, North Carolina 27599, United States
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14
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H. DeLuca S, L. DeLuca S, Leaver-Fay A, Meiler J. RosettaTMH: a method for membrane protein structure elucidation combining EPR distance restraints with assembly of transmembrane helices. AIMS Biophysics 2015. [DOI: 10.3934/biophy.2016.1.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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15
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Abstract
Degenerate codon (DC) libraries efficiently address the experimental library-size limitations of directed evolution by focusing diversity toward the positions and toward the amino acids (AAs) that are most likely to generate hits; however, manually constructing DC libraries is challenging, error prone and time consuming. This paper provides a dynamic programming solution to the task of finding the best DCs while keeping the size of the library beneath some given limit, improving on the existing integer-linear programming formulation. It then extends the algorithm to consider multiple DCs at each position, a heretofore unsolved problem, while adhering to a constraint on the number of primers needed to synthesize the library. In the two library-design problems examined here, the use of multiple DCs produces libraries that very nearly cover the set of desired AAs while still staying within the experimental size limits. Surprisingly, the algorithm is able to find near-perfect libraries where the ratio of amino-acid sequences to nucleic-acid sequences approaches 1; it effectively side-steps the degeneracy of the genetic code. Our algorithm is freely available through our web server and solves most design problems in about a second.
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Affiliation(s)
- Timothy M Jacobs
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hayretin Yumerefendi
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Brian Kuhlman
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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16
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Drew K, Renfrew PD, Craven TW, Butterfoss GL, Chou FC, Lyskov S, Bullock BN, Watkins A, Labonte JW, Pacella M, Kilambi KP, Leaver-Fay A, Kuhlman B, Gray JJ, Bradley P, Kirshenbaum K, Arora PS, Das R, Bonneau R. Adding diverse noncanonical backbones to rosetta: enabling peptidomimetic design. PLoS One 2013; 8:e67051. [PMID: 23869206 PMCID: PMC3712014 DOI: 10.1371/journal.pone.0067051] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Accepted: 05/13/2013] [Indexed: 11/19/2022] Open
Abstract
Peptidomimetics are classes of molecules that mimic structural and functional attributes of polypeptides. Peptidomimetic oligomers can frequently be synthesized using efficient solid phase synthesis procedures similar to peptide synthesis. Conformationally ordered peptidomimetic oligomers are finding broad applications for molecular recognition and for inhibiting protein-protein interactions. One critical limitation is the limited set of design tools for identifying oligomer sequences that can adopt desired conformations. Here, we present expansions to the ROSETTA platform that enable structure prediction and design of five non-peptidic oligomer scaffolds (noncanonical backbones), oligooxopiperazines, oligo-peptoids, -peptides, hydrogen bond surrogate helices and oligosaccharides. This work is complementary to prior additions to model noncanonical protein side chains in ROSETTA. The main purpose of our manuscript is to give a detailed description to current and future developers of how each of these noncanonical backbones was implemented. Furthermore, we provide a general outline for implementation of new backbone types not discussed here. To illustrate the utility of this approach, we describe the first tests of the ROSETTA molecular mechanics energy function in the context of oligooxopiperazines, using quantum mechanical calculations as comparison points, scanning through backbone and side chain torsion angles for a model peptidomimetic. Finally, as an example of a novel design application, we describe the automated design of an oligooxopiperazine that inhibits the p53-MDM2 protein-protein interaction. For the general biological and bioengineering community, several noncanonical backbones have been incorporated into web applications that allow users to freely and rapidly test the presented protocols (http://rosie.rosettacommons.org). This work helps address the peptidomimetic community's need for an automated and expandable modeling tool for noncanonical backbones.
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Affiliation(s)
- Kevin Drew
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - P. Douglas Renfrew
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Timothy W. Craven
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Glenn L. Butterfoss
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Fang-Chieh Chou
- Department of Biochemistry, Stanford University, Stanford, California, United States of America
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Brooke N. Bullock
- Department of Chemistry, New York University, New York, New York, United States of America
| | - Andrew Watkins
- Department of Chemistry, New York University, New York, New York, United States of America
| | - Jason W. Labonte
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Michael Pacella
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Krishna Praneeth Kilambi
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Brian Kuhlman
- Department of Biochemistry, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Philip Bradley
- Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Kent Kirshenbaum
- Department of Chemistry, New York University, New York, New York, United States of America
| | - Paramjit S. Arora
- Department of Chemistry, New York University, New York, New York, United States of America
| | - Rhiju Das
- Department of Biochemistry, Stanford University, Stanford, California, United States of America
| | - Richard Bonneau
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
- Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, United States of America
- * E-mail:
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17
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Leaver-Fay A, O'Meara MJ, Tyka M, Jacak R, Song Y, Kellogg EH, Thompson J, Davis IW, Pache RA, Lyskov S, Gray JJ, Kortemme T, Richardson JS, Havranek JJ, Snoeyink J, Baker D, Kuhlman B. Scientific benchmarks for guiding macromolecular energy function improvement. Methods Enzymol 2013; 523:109-43. [PMID: 23422428 DOI: 10.1016/b978-0-12-394292-0.00006-0] [Citation(s) in RCA: 159] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Accurate energy functions are critical to macromolecular modeling and design. We describe new tools for identifying inaccuracies in energy functions and guiding their improvement, and illustrate the application of these tools to the improvement of the Rosetta energy function. The feature analysis tool identifies discrepancies between structures deposited in the PDB and low-energy structures generated by Rosetta; these likely arise from inaccuracies in the energy function. The optE tool optimizes the weights on the different components of the energy function by maximizing the recapitulation of a wide range of experimental observations. We use the tools to examine three proposed modifications to the Rosetta energy function: improving the unfolded state energy model (reference energies), using bicubic spline interpolation to generate knowledge-based torisonal potentials, and incorporating the recently developed Dunbrack 2010 rotamer library (Shapovalov & Dunbrack, 2011).
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Affiliation(s)
- Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina, Chapel Hill, North Carolina, USA.
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18
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Jacak R, Leaver-Fay A, Kuhlman B. Computational protein design with explicit consideration of surface hydrophobic patches. Proteins 2011; 80:825-38. [PMID: 22223219 DOI: 10.1002/prot.23241] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2011] [Revised: 10/16/2011] [Accepted: 10/29/2011] [Indexed: 11/09/2022]
Abstract
De novo protein design requires the identification of amino-acid sequences that favor the target-folded conformation and are soluble in water. One strategy for promoting solubility is to disallow hydrophobic residues on the protein surface during design. However, naturally occurring proteins often have hydrophobic amino acids on their surface that contribute to protein stability via the partial burial of hydrophobic surface area or play a key role in the formation of protein-protein interactions. A less restrictive approach for surface design that is used by the modeling program Rosetta is to parameterize the energy function so that the number of hydrophobic amino acids designed on the protein surface is similar to what is observed in naturally occurring monomeric proteins. Previous studies with Rosetta have shown that this limits surface hydrophobics to the naturally occurring frequency (∼28%), but that it does not prevent the formation of hydrophobic patches that are considerably larger than those observed in naturally occurring proteins. Here, we describe a new score term that explicitly detects and penalizes the formation of hydrophobic patches during computational protein design. With the new term, we are able to design protein surfaces that include hydrophobic amino acids at naturally occurring frequencies, but do not have large hydrophobic patches. By adjusting the strength of the new score term, the emphasis of surface redesigns can be switched between maintaining solubility and maximizing folding free energy.
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Affiliation(s)
- Ron Jacak
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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19
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Abstract
Some protein design tasks cannot be modeled by the traditional single state design strategy of finding a sequence that is optimal for a single fixed backbone. Such cases require multistate design, where a single sequence is threaded onto multiple backbones (states) and evaluated for its strengths and weaknesses on each backbone. For example, to design a protein that can switch between two specific conformations, it is necessary to to find a sequence that is compatible with both backbone conformations. We present in this paper a generic implementation of multistate design that is suited for a wide range of protein design tasks and demonstrate in silico its capabilities at two design tasks: one of redesigning an obligate homodimer into an obligate heterodimer such that the new monomers would not homodimerize, and one of redesigning a promiscuous interface to bind to only a single partner and to no longer bind the rest of its partners. Both tasks contained negative design in that multistate design was asked to find sequences that would produce high energies for several of the states being modeled. Success at negative design was assessed by computationally redocking the undesired protein-pair interactions; we found that multistate design's accuracy improved as the diversity of conformations for the undesired protein-pair interactions increased. The paper concludes with a discussion of the pitfalls of negative design, which has proven considerably more challenging than positive design.
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Affiliation(s)
- Andrew Leaver-Fay
- Deptartment of Biochemistry, University of North Carolina, Chapel Hill, North Carolina, United States of America.
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20
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Abstract
Symmetric protein assemblies play important roles in many biochemical processes. However, the large size of such systems is challenging for traditional structure modeling methods. This paper describes the implementation of a general framework for modeling arbitrary symmetric systems in Rosetta3. We describe the various types of symmetries relevant to the study of protein structure that may be modeled using Rosetta's symmetric framework. We then describe how this symmetric framework is efficiently implemented within Rosetta, which restricts the conformational search space by sampling only symmetric degrees of freedom, and explicitly simulates only a subset of the interacting monomers. Finally, we describe structure prediction and design applications that utilize the Rosetta3 symmetric modeling capabilities, and provide a guide to running simulations on symmetric systems.
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Affiliation(s)
- Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America.
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21
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Abstract
The Rosetta de novo enzyme design protocol has been used to design enzyme
catalysts for a variety of chemical reactions, and in principle can be applied
to any arbitrary chemical reaction of interest, The process has four stages: 1)
choice of a catalytic mechanism and corresponding minimal model active site, 2)
identification of sites in a set of scaffold proteins where this minimal active
site can be realized, 3) optimization of the identities of the surrounding
residues for stabilizing interactions with the transition state and primary
catalytic residues, and 4) evaluation and ranking the resulting designed
sequences. Stages two through four of this process can be carried out with the
Rosetta package, while stage one needs to be done externally. Here, we
demonstrate how to carry out the Rosetta enzyme design protocol from start to
end in detail using for illustration the triosephosphate isomerase reaction.
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Affiliation(s)
- Florian Richter
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America.
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22
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Song Y, Tyka M, Leaver-Fay A, Thompson J, Baker D. Structure-guided forcefield optimization. Proteins 2011; 79:1898-909. [PMID: 21488100 PMCID: PMC3457920 DOI: 10.1002/prot.23013] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2010] [Revised: 01/06/2011] [Accepted: 01/20/2011] [Indexed: 11/06/2022]
Abstract
Accurate modeling of biomolecular systems requires accurate forcefields. Widely used molecular mechanics (MM) forcefields obtain parameters from experimental data and quantum chemistry calculations on small molecules but do not have a clear way to take advantage of the information in high-resolution macromolecular structures. In contrast, knowledge-based methods largely ignore the physical chemistry of interatomic interactions, and instead derive parameters almost exclusively from macromolecular structures. This can involve considerable double counting of the same physical interactions. Here, we describe a method for forcefield improvement that combines the strengths of the two approaches. We use this method to improve the Rosetta all-atom forcefield, in which the total energy is expressed as the sum of terms representing different physical interactions as in MM forcefields and the parameters are tuned to reproduce the properties of macromolecular structures. To resolve inaccuracies resulting from possible double counting of interactions, we compare distribution functions from low-energy modeled structures to those from crystal structures. The structural and physical bases of the deviations between the modeled and reference structures are identified and used to guide forcefield improvements. We describe improvements resolving double counting between backbone hydrogen bond interactions and Lennard-Jones interactions in helices; between sidechain-backbone hydrogen bonds and the backbone torsion potential; and between the sidechain torsion potential and Lennard-Jones interactions. Discrepancies between computed and observed distributions are also used to guide the incorporation of an explicit Cα-hydrogen bond in β sheets. The method can be used generally to integrate different sources of information for forcefield improvement.
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Affiliation(s)
- Yifan Song
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
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23
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Leaver-Fay A, Tyka M, Lewis SM, Lange OF, Thompson J, Jacak R, Kaufman K, Renfrew PD, Smith CA, Sheffler W, Davis IW, Cooper S, Treuille A, Mandell DJ, Richter F, Ban YEA, Fleishman SJ, Corn JE, Kim DE, Lyskov S, Berrondo M, Mentzer S, Popović Z, Havranek JJ, Karanicolas J, Das R, Meiler J, Kortemme T, Gray JJ, Kuhlman B, Baker D, Bradley P. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol 2011. [PMID: 21187238 DOI: 10.1016/b978-0-12-381270-4.00019-6.r] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
We have recently completed a full re-architecturing of the ROSETTA molecular modeling program, generalizing and expanding its existing functionality. The new architecture enables the rapid prototyping of novel protocols by providing easy-to-use interfaces to powerful tools for molecular modeling. The source code of this rearchitecturing has been released as ROSETTA3 and is freely available for academic use. At the time of its release, it contained 470,000 lines of code. Counting currently unpublished protocols at the time of this writing, the source includes 1,285,000 lines. Its rapid growth is a testament to its ease of use. This chapter describes the requirements for our new architecture, justifies the design decisions, sketches out central classes, and highlights a few of the common tasks that the new software can perform.
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Affiliation(s)
- Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina, Chapel Hill, North Carolina, USA
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24
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Leaver-Fay A, Tyka M, Lewis SM, Lange OF, Thompson J, Jacak R, Kaufman K, Renfrew PD, Smith CA, Sheffler W, Davis IW, Cooper S, Treuille A, Mandell DJ, Richter F, Ban YEA, Fleishman SJ, Corn JE, Kim DE, Lyskov S, Berrondo M, Mentzer S, Popović Z, Havranek JJ, Karanicolas J, Das R, Meiler J, Kortemme T, Gray JJ, Kuhlman B, Baker D, Bradley P. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol 2011; 487:545-74. [PMID: 21187238 PMCID: PMC4083816 DOI: 10.1016/b978-0-12-381270-4.00019-6] [Citation(s) in RCA: 1296] [Impact Index Per Article: 99.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We have recently completed a full re-architecturing of the ROSETTA molecular modeling program, generalizing and expanding its existing functionality. The new architecture enables the rapid prototyping of novel protocols by providing easy-to-use interfaces to powerful tools for molecular modeling. The source code of this rearchitecturing has been released as ROSETTA3 and is freely available for academic use. At the time of its release, it contained 470,000 lines of code. Counting currently unpublished protocols at the time of this writing, the source includes 1,285,000 lines. Its rapid growth is a testament to its ease of use. This chapter describes the requirements for our new architecture, justifies the design decisions, sketches out central classes, and highlights a few of the common tasks that the new software can perform.
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Affiliation(s)
- Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina, Chapel Hill, North Carolina, USA
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25
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Leaver-Fay A, Tyka M, Lewis SM, Lange OF, Thompson J, Jacak R, Kaufman K, Renfrew PD, Smith CA, Sheffler W, Davis IW, Cooper S, Treuille A, Mandell DJ, Richter F, Ban YEA, Fleishman SJ, Corn JE, Kim DE, Lyskov S, Berrondo M, Mentzer S, Popović Z, Havranek JJ, Karanicolas J, Das R, Meiler J, Kortemme T, Gray JJ, Kuhlman B, Baker D, Bradley P. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol 2011. [PMID: 21187238 DOI: 10.1016/s0076-6879(11)87019-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
We have recently completed a full re-architecturing of the ROSETTA molecular modeling program, generalizing and expanding its existing functionality. The new architecture enables the rapid prototyping of novel protocols by providing easy-to-use interfaces to powerful tools for molecular modeling. The source code of this rearchitecturing has been released as ROSETTA3 and is freely available for academic use. At the time of its release, it contained 470,000 lines of code. Counting currently unpublished protocols at the time of this writing, the source includes 1,285,000 lines. Its rapid growth is a testament to its ease of use. This chapter describes the requirements for our new architecture, justifies the design decisions, sketches out central classes, and highlights a few of the common tasks that the new software can perform.
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Affiliation(s)
- Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina, Chapel Hill, North Carolina, USA
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26
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Kellogg EH, Leaver-Fay A, Baker D. Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins 2010; 79:830-8. [PMID: 21287615 DOI: 10.1002/prot.22921] [Citation(s) in RCA: 433] [Impact Index Per Article: 30.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2010] [Revised: 09/08/2010] [Accepted: 10/13/2010] [Indexed: 11/06/2022]
Abstract
The prediction of changes in protein stability and structure resulting from single amino acid substitutions is both a fundamental test of macromolecular modeling methodology and an important current problem as high throughput sequencing reveals sequence polymorphisms at an increasing rate. In principle, given the structure of a wild-type protein and a point mutation whose effects are to be predicted, an accurate method should recapitulate both the structural changes and the change in the folding-free energy. Here, we explore the performance of protocols which sample an increasing diversity of conformations. We find that surprisingly similar performances in predicting changes in stability are achieved using protocols that involve very different amounts of conformational sampling, provided that the resolution of the force field is matched to the resolution of the sampling method. Methods involving backbone sampling can in some cases closely recapitulate the structural changes accompanying mutations but not surprisingly tend to do more harm than good in cases where structural changes are negligible. Analysis of the outliers in the stability change calculations suggests areas needing particular improvement; these include the balance between desolvation and the formation of favorable buried polar interactions, and unfolded state modeling.
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Affiliation(s)
- Elizabeth H Kellogg
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
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Jha RK, Leaver-Fay A, Yin S, Wu Y, Butterfoss GL, Szyperski T, Dokholyan NV, Kuhlman B. Computational design of a PAK1 binding protein. J Mol Biol 2010; 400:257-70. [PMID: 20460129 DOI: 10.1016/j.jmb.2010.05.006] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2010] [Revised: 04/28/2010] [Accepted: 05/03/2010] [Indexed: 11/17/2022]
Abstract
We describe a computational protocol, called DDMI, for redesigning scaffold proteins to bind to a specified region on a target protein. The DDMI protocol is implemented within the Rosetta molecular modeling program and uses rigid-body docking, sequence design, and gradient-based minimization of backbone and side-chain torsion angles to design low-energy interfaces between the scaffold and target protein. Iterative rounds of sequence design and conformational optimization were needed to produce models that have calculated binding energies that are similar to binding energies calculated for native complexes. We also show that additional conformation sampling with molecular dynamics can be iterated with sequence design to further lower the computed energy of the designed complexes. To experimentally test the DDMI protocol, we redesigned the human hyperplastic discs protein to bind to the kinase domain of p21-activated kinase 1 (PAK1). Six designs were experimentally characterized. Two of the designs aggregated and were not characterized further. Of the remaining four designs, three bound to the PAK1 with affinities tighter than 350 muM. The tightest binding design, named Spider Roll, bound with an affinity of 100 muM. NMR-based structure prediction of Spider Roll based on backbone and (13)C(beta) chemical shifts using the program CS-ROSETTA indicated that the architecture of human hyperplastic discs protein is preserved. Mutagenesis studies confirmed that Spider Roll binds the target patch on PAK1. Additionally, Spider Roll binds to full-length PAK1 in its activated state but does not bind PAK1 when it forms an auto-inhibited conformation that blocks the Spider Roll target site. Subsequent NMR characterization of the binding of Spider Roll to PAK1 revealed a comparably small binding 'on-rate' constant (<<10(5) M(-1) s(-1)). The ability to rationally design the site of novel protein-protein interactions is an important step towards creating new proteins that are useful as therapeutics or molecular probes.
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Affiliation(s)
- Ramesh K Jha
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7260, USA
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Leaver-Fay A, Butterfoss GL, Snoeyink J, Kuhlman B. Maintaining solvent accessible surface area under rotamer substitution for protein design. J Comput Chem 2007; 28:1336-41. [PMID: 17285560 DOI: 10.1002/jcc.20626] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Although quantities derived from solvent accessible surface areas (SASA) are useful in many applications in protein design and structural biology, the computational cost of accurate SASA calculation makes SASA-based scores difficult to integrate into commonly used protein design methodologies. We demonstrate a method for maintaining accurate SASA during a Monte Carlo search of sequence and rotamer space for a fixed protein backbone. We extend the fast Le Grand and Merz algorithm (Le Grand and Merz, J Comput Chem, 14, 349), which discretizes the solvent accessible surface for each atom by placing dots on a sphere and combines Boolean masks to determine which dots are exposed. By replacing semigroup operations with group operations (from Boolean logic to counting dot coverage) we support SASA updates. Our algorithm takes time proportional to the number of atoms affected by rotamer substitution, rather than the number of atoms in the protein. For design simulations with a one hundred residue protein our approach is approximately 145 times faster than performing a Le Grand and Merz SASA calculation from scratch following each rotamer substitution. To demonstrate practical effectiveness, we optimize a SASA-based measure of protein packing in the complete redesign of a large set of proteins and protein-protein interfaces.
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Affiliation(s)
- Andrew Leaver-Fay
- Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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Davis IW, Leaver-Fay A, Chen VB, Block JN, Kapral GJ, Wang X, Murray LW, Arendall WB, Snoeyink J, Richardson JS, Richardson DC. MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res 2007; 35:W375-83. [PMID: 17452350 PMCID: PMC1933162 DOI: 10.1093/nar/gkm216] [Citation(s) in RCA: 3154] [Impact Index Per Article: 185.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MolProbity is a general-purpose web server offering quality validation for 3D structures of proteins, nucleic acids and complexes. It provides detailed all-atom contact analysis of any steric problems within the molecules as well as updated dihedral-angle diagnostics, and it can calculate and display the H-bond and van der Waals contacts in the interfaces between components. An integral step in the process is the addition and full optimization of all hydrogen atoms, both polar and nonpolar. New analysis functions have been added for RNA, for interfaces, and for NMR ensembles. Additionally, both the web site and major component programs have been rewritten to improve speed, convenience, clarity and integration with other resources. MolProbity results are reported in multiple forms: as overall numeric scores, as lists or charts of local problems, as downloadable PDB and graphics files, and most notably as informative, manipulable 3D kinemage graphics shown online in the KiNG viewer. This service is available free to all users at http://molprobity.biochem.duke.edu.
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Affiliation(s)
- Ian W. Davis
- Department of Biochemistry, Duke University, Durham, NC, USA and Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Andrew Leaver-Fay
- Department of Biochemistry, Duke University, Durham, NC, USA and Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Vincent B. Chen
- Department of Biochemistry, Duke University, Durham, NC, USA and Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Jeremy N. Block
- Department of Biochemistry, Duke University, Durham, NC, USA and Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Gary J. Kapral
- Department of Biochemistry, Duke University, Durham, NC, USA and Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Xueyi Wang
- Department of Biochemistry, Duke University, Durham, NC, USA and Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Laura W. Murray
- Department of Biochemistry, Duke University, Durham, NC, USA and Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA
| | - W. Bryan Arendall
- Department of Biochemistry, Duke University, Durham, NC, USA and Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Jack Snoeyink
- Department of Biochemistry, Duke University, Durham, NC, USA and Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Jane S. Richardson
- Department of Biochemistry, Duke University, Durham, NC, USA and Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA
| | - David C. Richardson
- Department of Biochemistry, Duke University, Durham, NC, USA and Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA
- *To whom correspondence should be addressed. +1-919-684-6010
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Leaver-Fay A, Kuhlman B, Snoeyink J. An adaptive dynamic programming algorithm for the side chain placement problem. Pac Symp Biocomput 2005:16-27. [PMID: 15759610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Larger rotamer libraries, which provide a fine grained discretization of side chain conformation space by sampling near the canonical rotamers, allow protein designers to find better conformations, but slow down the algorithms that search for them. We present a dynamic programming solution to the side chain placement problem which treats rotamers at high or low resolution only as necessary. Dynamic programming is an exact technique; we turn it into an approximation, but can still analyze the error that can be introduced. We have used our algorithm to redesign the surface residues of ubiquitin's beta sheet.
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