1
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Adams JL, Kangarloo T, Tracey B, O'Donnell P, Volfson D, Latzman RD, Zach N, Alexander R, Bergethon P, Cosman J, Anderson D, Best A, Severson J, Kostrzebski MA, Auinger P, Wilmot P, Pohlson Y, Waddell E, Jensen-Roberts S, Gong Y, Kilambi KP, Herrero TR, Ray Dorsey E. Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study. NPJ Parkinsons Dis 2023; 9:64. [PMID: 37069193 PMCID: PMC10108794 DOI: 10.1038/s41531-023-00497-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 02/27/2023] [Indexed: 04/19/2023] Open
Abstract
Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson's disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials.
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Affiliation(s)
- Jamie L Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
| | | | | | - Patricio O'Donnell
- Takeda Pharmaceuticals, Cambridge, MA, USA
- Sage Therapeutics, Seattle, WA, USA
| | | | | | - Neta Zach
- Takeda Pharmaceuticals, Cambridge, MA, USA
| | - Robert Alexander
- Takeda Pharmaceuticals, Cambridge, MA, USA
- Banner Health, Phoenix, AZ, USA
| | | | | | | | | | | | - Melissa A Kostrzebski
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Peggy Auinger
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Peter Wilmot
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Yvonne Pohlson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Emma Waddell
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Stella Jensen-Roberts
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Yishu Gong
- Takeda Pharmaceuticals, Cambridge, MA, USA
| | - Krishna Praneeth Kilambi
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA, USA
| | | | - E Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
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2
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Roussos G, Herrero TR, Hill DL, Dowling AV, L T M Müller M, Evers LJW, Burton J, Derungs A, Fisher K, Kilambi KP, Mehrotra N, Bhatnagar R, Sardar S, Stephenson D, Adams JL, Ray Dorsey E, Cosman J. Identifying and characterising sources of variability in digital outcome measures in Parkinson's disease. NPJ Digit Med 2022; 5:93. [PMID: 35840653 PMCID: PMC9284971 DOI: 10.1038/s41746-022-00643-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 06/29/2022] [Indexed: 11/23/2022] Open
Abstract
Smartphones and wearables are widely recognised as the foundation for novel Digital Health Technologies (DHTs) for the clinical assessment of Parkinson's disease. Yet, only limited progress has been made towards their regulatory acceptability as effective drug development tools. A key barrier in achieving this goal relates to the influence of a wide range of sources of variability (SoVs) introduced by measurement processes incorporating DHTs, on their ability to detect relevant changes to PD. This paper introduces a conceptual framework to assist clinical research teams investigating a specific Concept of Interest within a particular Context of Use, to identify, characterise, and when possible, mitigate the influence of SoVs. We illustrate how this conceptual framework can be applied in practice through specific examples, including two data-driven case studies.
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Affiliation(s)
| | | | | | | | | | - Luc J W Evers
- Radboud University Medical Center and Radboud University, Nijmegen, The Netherlands
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3
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Arndt JW, Quan C, Qian F, Kilambi KP, Bussière T, Weinreb P. P1‐167: Structural Basis of Unique Selectivity of Aducanumab for Oligomeric Forms of Amyloid‐β. Alzheimers Dement 2016. [DOI: 10.1016/j.jalz.2016.06.915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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4
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Lensink MF, Moal IH, Bates PA, Kastritis PL, Melquiond ASJ, Karaca E, Schmitz C, van Dijk M, Bonvin AMJJ, Eisenstein M, Jiménez-García B, Grosdidier S, Solernou A, Pérez-Cano L, Pallara C, Fernández-Recio J, Xu J, Muthu P, Praneeth Kilambi K, Gray JJ, Grudinin S, Derevyanko G, Mitchell JC, Wieting J, Kanamori E, Tsuchiya Y, Murakami Y, Sarmiento J, Standley DM, Shirota M, Kinoshita K, Nakamura H, Chavent M, Ritchie DW, Park H, Ko J, Lee H, Seok C, Shen Y, Kozakov D, Vajda S, Kundrotas PJ, Vakser IA, Pierce BG, Hwang H, Vreven T, Weng Z, Buch I, Farkash E, Wolfson HJ, Zacharias M, Qin S, Zhou HX, Huang SY, Zou X, Wojdyla JA, Kleanthous C, Wodak SJ. Blind prediction of interfacial water positions in CAPRI. Proteins 2014; 82:620-32. [PMID: 24155158 PMCID: PMC4582081 DOI: 10.1002/prot.24439] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Revised: 09/16/2013] [Accepted: 09/26/2013] [Indexed: 12/30/2022]
Abstract
We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.
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Affiliation(s)
- Marc F Lensink
- Interdisciplinary Research Institute USR3078 CNRS, University Lille North of France, Villeneuve d'Ascq, France
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5
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Kilambi KP, Pacella MS, Xu J, Labonte JW, Porter JR, Muthu P, Drew K, Kuroda D, Schueler-Furman O, Bonneau R, Gray JJ. Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27. Proteins 2013; 81:2201-9. [PMID: 24123494 PMCID: PMC4037910 DOI: 10.1002/prot.24425] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 09/12/2013] [Accepted: 09/13/2013] [Indexed: 11/09/2022]
Abstract
Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems.
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Affiliation(s)
- Krishna Praneeth Kilambi
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Michael S. Pacella
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jianqing Xu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jason W. Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Justin R. Porter
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Pravin Muthu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Kevin Drew
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York
| | - Daisuke Kuroda
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
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6
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Moretti R, Fleishman SJ, Agius R, Torchala M, Bates PA, Kastritis PL, Rodrigues JPGLM, Trellet M, Bonvin AMJJ, Cui M, Rooman M, Gillis D, Dehouck Y, Moal I, Romero-Durana M, Perez-Cano L, Pallara C, Jimenez B, Fernandez-Recio J, Flores S, Pacella M, Kilambi KP, Gray JJ, Popov P, Grudinin S, Esquivel-Rodríguez J, Kihara D, Zhao N, Korkin D, Zhu X, Demerdash ONA, Mitchell JC, Kanamori E, Tsuchiya Y, Nakamura H, Lee H, Park H, Seok C, Sarmiento J, Liang S, Teraguchi S, Standley DM, Shimoyama H, Terashi G, Takeda-Shitaka M, Iwadate M, Umeyama H, Beglov D, Hall DR, Kozakov D, Vajda S, Pierce BG, Hwang H, Vreven T, Weng Z, Huang Y, Li H, Yang X, Ji X, Liu S, Xiao Y, Zacharias M, Qin S, Zhou HX, Huang SY, Zou X, Velankar S, Janin J, Wodak SJ, Baker D. Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions. Proteins 2013; 81:1980-7. [PMID: 23843247 PMCID: PMC4143140 DOI: 10.1002/prot.24356] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2013] [Revised: 06/13/2013] [Accepted: 06/18/2013] [Indexed: 12/25/2022]
Abstract
Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.
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Affiliation(s)
- Rocco Moretti
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
| | - Sarel J. Fleishman
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Rudi Agius
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, WC2A 3LY, UK
| | - Mieczyslaw Torchala
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, WC2A 3LY, UK
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, WC2A 3LY, UK
| | - Panagiotis L. Kastritis
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CG, Utrecht, the Netherlands
| | - João P. G. L. M. Rodrigues
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CG, Utrecht, the Netherlands
| | - Mikaël Trellet
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CG, Utrecht, the Netherlands
| | - Alexandre M. J. J. Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CG, Utrecht, the Netherlands
| | - Meng Cui
- Department of Physiology and Biophysics, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Marianne Rooman
- Department of BioModelling, BioInformatics and BioProcesses, Université Libre de Bruxelles (ULB), 1050 Brussels, Belgium
| | - Dimitri Gillis
- Department of BioModelling, BioInformatics and BioProcesses, Université Libre de Bruxelles (ULB), 1050 Brussels, Belgium
| | - Yves Dehouck
- Department of BioModelling, BioInformatics and BioProcesses, Université Libre de Bruxelles (ULB), 1050 Brussels, Belgium
| | - Iain Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Miguel Romero-Durana
- Joint BSC-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Laura Perez-Cano
- Joint BSC-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Chiara Pallara
- Joint BSC-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Brian Jimenez
- Joint BSC-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Juan Fernandez-Recio
- Joint BSC-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Samuel Flores
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, 75124, Sweden
| | - Michael Pacella
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Krishna Praneeth Kilambi
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Petr Popov
- NANO-D, INRIA Grenoble-Rhone-Alpes Research Center, 38334 Saint Ismier Cedex, Montbonnot, France; CNRS, Laboratoire Jean Kuntzmann, BP 53, Grenoble Cedex 9, France
| | - Sergei Grudinin
- NANO-D, INRIA Grenoble-Rhone-Alpes Research Center, 38334 Saint Ismier Cedex, Montbonnot, France; CNRS, Laboratoire Jean Kuntzmann, BP 53, Grenoble Cedex 9, France
| | | | - Daisuke Kihara
- Department of Computer Science, Purdue University ,West Lafayette, IN 47907, USA
- Department of Biological Sciences, Purdue University ,West Lafayette, IN 47907, USA
| | - Nan Zhao
- Informatics Institute and Department of Computer Science, University of Missouri-Columbia, MO 65211, USA
| | - Dmitry Korkin
- Informatics Institute and Department of Computer Science, University of Missouri-Columbia, MO 65211, USA
| | - Xiaolei Zhu
- Departments of Mathematics and Biochemistry, University of Wisconsin, Madison, WI 53706, USA
| | - Omar N. A. Demerdash
- Departments of Mathematics and Biochemistry, University of Wisconsin, Madison, WI 53706, USA
| | - Julie C. Mitchell
- Departments of Mathematics and Biochemistry, University of Wisconsin, Madison, WI 53706, USA
| | - Eiji Kanamori
- Japan Biological Informatics Consortium, Tokyo, Japan
| | - Yuko Tsuchiya
- Division of Life Sciences, Graduate School of Humanities and Sciences, Ochanomizu University, Tokyo, Japan
| | - Haruki Nakamura
- Institute for Protein Research, Osaka University, Osaka, Japan
| | - Hasup Lee
- Department of Chemistry, Seoul National University, Seoul 151-747, Korea
| | - Hahnbeom Park
- Department of Chemistry, Seoul National University, Seoul 151-747, Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 151-747, Korea
| | - Jamica Sarmiento
- Systems Immunology Lab, WPI Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Shide Liang
- Systems Immunology Lab, WPI Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Shusuke Teraguchi
- Systems Immunology Lab, WPI Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Daron M. Standley
- Systems Immunology Lab, WPI Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | | | | | | | - Mitsuo Iwadate
- Department of Biological Sciences, Faculty of Science and Engineering, Chuo University
| | - Hideaki Umeyama
- Department of Biological Sciences, Faculty of Science and Engineering, Chuo University
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - David R. Hall
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Dima Kozakov
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Brian G. Pierce
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Howook Hwang
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Yangyu Huang
- Huazhong University of Science and Technology, China
| | - Haotian Li
- Huazhong University of Science and Technology, China
| | - Xiufeng Yang
- Huazhong University of Science and Technology, China
| | - Xiaofeng Ji
- Huazhong University of Science and Technology, China
| | - Shiyong Liu
- Huazhong University of Science and Technology, China
| | - Yi Xiao
- Huazhong University of Science and Technology, China
| | - Martin Zacharias
- Physics Department, Technical University Munich, 85748 Garching, Germany
| | - Sanbo Qin
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
| | - Huan-Xiang Zhou
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
| | - Sheng-You Huang
- Department of Physics and Astronomy, Department of Biochemistry, Dalton Cardiovascular Research Center, Informatics Institute; University of Missouri-Columbia; Columbia, MO 65211, USA
| | - Xiaoqin Zou
- Department of Physics and Astronomy, Department of Biochemistry, Dalton Cardiovascular Research Center, Informatics Institute; University of Missouri-Columbia; Columbia, MO 65211, USA
| | - Sameer Velankar
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Joël Janin
- IBBMC, Université Paris-Sud, 91405-Orsay, France
| | - Shoshana J. Wodak
- Department of Biochemistry, University of Toronto, Ontario, Canada M5S 1A8
- Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5K 1X8, Canada
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, United States
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7
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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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Kilambi KP, Gray JJ. Rapid calculation of protein pKa values using Rosetta. Biophys J 2013; 103:587-595. [PMID: 22947875 DOI: 10.1016/j.bpj.2012.06.044] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2012] [Revised: 06/08/2012] [Accepted: 06/11/2012] [Indexed: 12/21/2022] Open
Abstract
We developed a Rosetta-based Monte Carlo method to calculate the pK(a) values of protein residues that commonly exhibit variable protonation states (Asp, Glu, Lys, His, and Tyr). We tested the technique by calculating pK(a) values for 264 residues from 34 proteins. The standard Rosetta score function, which is independent of any environmental conditions, failed to capture pK(a) shifts. After incorporating a Coulomb electrostatic potential and optimizing the solvation reference energies for pK(a) calculations, we employed a method that allowed side-chain flexibility and achieved a root mean-square deviation (RMSD) of 0.83 from experimental values (0.68 after discounting 11 predictions with an error over 2 pH units). Additional degrees of side-chain conformational freedom for the proximal residues facilitated the capture of charge-charge interactions in a few cases, resulting in an overall RMSD of 0.85 pH units. The addition of backbone flexibility increased the overall RMSD to 0.93 pH units but improved relative pK(a) predictions for proximal catalytic residues. The method also captures large pK(a) shifts of lysine and some glutamate point mutations in staphylococcal nuclease. Thus, a simple and fast method based on the Rosetta score function and limited conformational sampling produces pK(a) values that will be useful when rapid estimation is essential, such as in docking, design, and folding.
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Affiliation(s)
- Krishna Praneeth Kilambi
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland; Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, Maryland.
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Chaikind B, Kilambi KP, Gray JJ, Ostermeier M. Targeted DNA methylation using an artificially bisected M.HhaI fused to zinc fingers. PLoS One 2012; 7:e44852. [PMID: 22984575 PMCID: PMC3439449 DOI: 10.1371/journal.pone.0044852] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [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: 06/13/2012] [Accepted: 08/08/2012] [Indexed: 11/18/2022] Open
Abstract
Little is known about the effects of single DNA methylation events on gene transcription. The ability to direct the methylation toward a single unique site within a genome would have broad use as a tool to study the effects of specific epigenetic changes on transcription. A targeted enzyme might also be useful in a therapy for diseases with an epigenetic component or as a means to site-specifically label DNA. Previous studies have sought to target methyltransferase activity by fusing DNA binding proteins to methyltransferases. However, the methyltransferase domain remains active even when the DNA binding protein is unbound, resulting in significant off-target methylation. A better strategy would make methyltransferase activity contingent upon the DNA binding protein’s association with its DNA binding site. We have designed targeted methyltransferases by fusing zinc fingers to the fragments of artificially-bisected, assembly-compromised methyltransferases. The zinc fingers’ binding sites flank the desired target site for methylation. Zinc finger binding localizes the two fragments near each other encouraging their assembly only over the desired site. Through a combination of molecular modeling and experimental optimization in E. coli, we created an engineered methyltransferase derived from M.HhaI with 50–60% methylation at a target site and nearly undetectable levels of methylation at a non-target M.HhaI site (1.4±2.4%). Using a restriction digestion assay, we demonstrate that localization of both fragments synergistically increases methylation at the target site, illustrating the promise of our approach.
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Affiliation(s)
- Brian Chaikind
- Chemistry-Biology Interface Graduate Program, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Krishna Praneeth Kilambi
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Marc Ostermeier
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- * E-mail:
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Sircar A, Chaudhury S, Kilambi KP, Berrondo M, Gray JJ. A generalized approach to sampling backbone conformations with RosettaDock for CAPRI rounds 13-19. Proteins 2011; 78:3115-23. [PMID: 20535822 DOI: 10.1002/prot.22765] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In CAPRI rounds 13-19, the most native-like structure predicted by RosettaDock resulted in two high, one medium, and one acceptable accuracy model out of 13 targets. The current rounds of CAPRI were especially challenging with many unbound and homology modeled starting structures. Novel docking methods, including EnsembleDock and SnugDock, allowed backbone conformational sampling during docking and enabled the creation of more accurate models. For Target 32, α-amylase/subtilisin inhibitor-subtilisin savinase, we sampled different backbone conformations at an interfacial loop to produce five high-quality models including the most accurate structure submitted in the challenge (2.1 Å ligand rmsd, 0.52 Å interface rmsd). For Target 41, colicin-immunity protein, we used EnsembleDock to sample the ensemble of nuclear magnetic resonance (NMR) models of the immunity protein to generate a medium accuracy structure. Experimental data identifying the catalytic residues at the binding interface for Target 40 (trypsin-inhibitor) were used to filter RosettaDock global rigid body docking decoys to determine high accuracy predictions for the two distinct binding sites in which the inhibitor interacts with trypsin. We discuss our generalized approach to selecting appropriate methods for different types of docking problems. The current toolset provides some robustness to errors in homology models, but significant challenges remain in accommodating larger backbone uncertainties and in sampling adequately for global searches.
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Affiliation(s)
- Aroop Sircar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
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