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Kryś JD, Gront D. Coarse-grained potential for hydrogen bond interactions. J Mol Graph Model 2023; 124:108507. [PMID: 37295157 DOI: 10.1016/j.jmgm.2023.108507] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/12/2023] [Accepted: 04/18/2023] [Indexed: 06/12/2023]
Abstract
Understanding protein structure and dynamics is crucial for investigating numerous biological processes. This however requires proper description of molecular interactions, most notably hydrogen bonds, which are the driving force behind the folding of protein sequences into working molecules. Due to the multi-body character of this interaction, proper mathematical formulation has been a matter of long debate in the literature. This description becomes even more complex in reduced protein models. In this contribution, we propose a novel hydrogen bond energy function definition that is based only on Cα positions and used for coarse-grained simulations. We show that this new method has the capability to recognize hydrogen bonds with over 80% accuracy and can successfully identify β-sheet in β-amyloid peptide simulations.
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Affiliation(s)
- Justyna D Kryś
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland.
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
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2
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Macnar JM, Brzezinski D, Chruszcz M, Gront D. Analysis of protein structures containing
HEPES
and
MES
molecules. Protein Sci 2022. [PMCID: PMC9601878 DOI: 10.1002/pro.4415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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] [Indexed: 11/08/2022]
Abstract
X‐ray crystallography is the main experimental method behind ligand–macromolecule complexes found in the Protein Data Bank (PDB). Applying bioinformatics methods to such structural data can fuel drug discovery, albeit under the condition that the information is correct. Regrettably, a small number of structures in the PDB are of suboptimal quality due to incorrectly identified and modeled ligands in protein–ligand complexes. In this paper, we combine a theoretical‐graph approach, nuclear density estimates, bioinformatics methods, and prior chemical knowledge to analyze two non‐physiological ligands, HEPES and MES, that are frequent components of crystallization and purifications buffers. Our analysis includes quantum mechanics calculations and Cambridge Structure Database (CSD) queries to define the ideal conformation of these ligands, geometry analysis of PDB deposits regarding several quality factors, and a search for homologous structures to identify other small molecules that could bind in place of the parasitic ligand. Our results highlight the need for careful refinement of macromolecule–ligand complexes and better validation tools that integrate results from all relevant resources. PDB Code(s): 3K4L, 3PYI, 5T6L, 6BB0, 1PJX, 3O4P, 6WCF, 3DKE, 3E10, 6G38, 4E8R, 4Z91, 3E9F, 1MOS, 1MOQ, 2ESB, 1VHR, 4P66 and 6NNI;
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Affiliation(s)
- Joanna Magdalena Macnar
- Department of Molecular Physiology and Biological Physics University of Virginia Charlottesville Virginia USA
- College of Inter‐Faculty Individual Studies in Mathematics and Natural Sciences University of Warsaw Warsaw Poland
- Faculty of Chemistry, Biological and Chemical Research Center University of Warsaw Warsaw Poland
| | - Dariusz Brzezinski
- Department of Molecular Physiology and Biological Physics University of Virginia Charlottesville Virginia USA
- Institute of Computing Science Poznan University of Technology Poznan Poland
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry Polish Academy of Sciences Poznan Poland
| | - Maksymilian Chruszcz
- Department of Chemistry and Biochemistry University of South Carolina Columbia South Carolina USA
| | - Dominik Gront
- Department of Molecular Physiology and Biological Physics University of Virginia Charlottesville Virginia USA
- Faculty of Chemistry, Biological and Chemical Research Center University of Warsaw Warsaw Poland
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3
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Macnar J, Brzezinski D, Gront D. Analysis of common buffer molecules' conformations in ligand–protein complexes. Acta Cryst Sect A 2022. [DOI: 10.1107/s2053273322094256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
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4
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Malinga NA, Nzuza N, Padayachee T, Syed PR, Karpoormath R, Gront D, Nelson DR, Syed K. An Unprecedented Number of Cytochrome P450s Are Involved in Secondary Metabolism in Salinispora Species. Microorganisms 2022; 10:microorganisms10050871. [PMID: 35630316 PMCID: PMC9143469 DOI: 10.3390/microorganisms10050871] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [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: 04/04/2022] [Revised: 04/15/2022] [Accepted: 04/19/2022] [Indexed: 01/04/2023] Open
Abstract
Cytochrome P450 monooxygenases (CYPs/P450s) are heme thiolate proteins present in species across the biological kingdoms. By virtue of their broad substrate promiscuity and regio- and stereo-selectivity, these enzymes enhance or attribute diversity to secondary metabolites. Actinomycetes species are well-known producers of secondary metabolites, especially Salinispora species. Despite the importance of P450s, a comprehensive comparative analysis of P450s and their role in secondary metabolism in Salinispora species is not reported. We therefore analyzed P450s in 126 strains from three different species Salinispora arenicola, S. pacifica, and S. tropica. The study revealed the presence of 2643 P450s that can be grouped into 45 families and 103 subfamilies. CYP107 and CYP125 families are conserved, and CYP105 and CYP107 families are bloomed (a P450 family with many members) across Salinispora species. Analysis of P450s that are part of secondary metabolite biosynthetic gene clusters (smBGCs) revealed Salinispora species have an unprecedented number of P450s (1236 P450s-47%) part of smBGCs compared to other bacterial species belonging to the genera Streptomyces (23%) and Mycobacterium (11%), phyla Cyanobacteria (8%) and Firmicutes (18%) and the classes Alphaproteobacteria (2%) and Gammaproteobacteria (18%). A peculiar characteristic of up to six P450s in smBGCs was observed in Salinispora species. Future characterization Salinispora species P450s and their smBGCs have the potential for discovering novel secondary metabolites.
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Affiliation(s)
- Nsikelelo Allison Malinga
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa; (N.A.M.); (N.N.); (T.P.)
| | - Nomfundo Nzuza
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa; (N.A.M.); (N.N.); (T.P.)
| | - Tiara Padayachee
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa; (N.A.M.); (N.N.); (T.P.)
| | - Puleng Rosinah Syed
- Department of Pharmaceutical Chemistry, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa; (P.R.S.); (R.K.)
| | - Rajshekhar Karpoormath
- Department of Pharmaceutical Chemistry, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa; (P.R.S.); (R.K.)
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland;
| | - David R. Nelson
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Correspondence: (D.R.N.); (K.S.); Tel.: +19-014-488-303 (D.R.N.); +27-035-902-6857 (K.S.)
| | - Khajamohiddin Syed
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa; (N.A.M.); (N.N.); (T.P.)
- Correspondence: (D.R.N.); (K.S.); Tel.: +19-014-488-303 (D.R.N.); +27-035-902-6857 (K.S.)
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5
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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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6
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Akapo OO, Macnar JM, Kryś JD, Syed PR, Syed K, Gront D. In Silico Structural Modeling and Analysis of Interactions of Tremellomycetes Cytochrome P450 Monooxygenases CYP51s with Substrates and Azoles. Int J Mol Sci 2021; 22:7811. [PMID: 34360577 PMCID: PMC8346148 DOI: 10.3390/ijms22157811] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 02/25/2021] [Revised: 05/21/2021] [Accepted: 05/25/2021] [Indexed: 11/16/2022] Open
Abstract
Cytochrome P450 monooxygenase CYP51 (sterol 14α-demethylase) is a well-known target of the azole drug fluconazole for treating cryptococcosis, a life-threatening fungal infection in immune-compromised patients in poor countries. Studies indicate that mutations in CYP51 confer fluconazole resistance on cryptococcal species. Despite the importance of CYP51 in these species, few studies on the structural analysis of CYP51 and its interactions with different azole drugs have been reported. We therefore performed in silico structural analysis of 11 CYP51s from cryptococcal species and other Tremellomycetes. Interactions of 11 CYP51s with nine ligands (three substrates and six azoles) performed by Rosetta docking using 10,000 combinations for each of the CYP51-ligand complex (11 CYP51s × 9 ligands = 99 complexes) and hierarchical agglomerative clustering were used for selecting the complexes. A web application for visualization of CYP51s' interactions with ligands was developed (http://bioshell.pl/azoledocking/). The study results indicated that Tremellomycetes CYP51s have a high preference for itraconazole, corroborating the in vitro effectiveness of itraconazole compared to fluconazole. Amino acids interacting with different ligands were found to be conserved across CYP51s, indicating that the procedure employed in this study is accurate and can be automated for studying P450-ligand interactions to cater for the growing number of P450s.
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Affiliation(s)
- Olufunmilayo Olukemi Akapo
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa;
| | - Joanna M. Macnar
- College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Stefana Banacha 2C, 02-097 Warsaw, Poland;
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland;
| | - Justyna D. Kryś
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland;
| | - Puleng Rosinah Syed
- Department of Pharmaceutical Chemistry, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa;
| | - Khajamohiddin Syed
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa;
| | - Dominik Gront
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland;
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7
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Kryś JD, Gront D. VisuaLife: Library for interactive visualization in rich web applications. Bioinformatics 2021; 37:3662-3663. [PMID: 33961033 PMCID: PMC8136008 DOI: 10.1093/bioinformatics/btab251] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/24/2021] [Accepted: 04/19/2021] [Indexed: 11/13/2022] Open
Abstract
Motivation Visualization is a powerful tool to analyze, understand and present big data. Computational biology, bioinformatics and molecular modeling require dedicated tools, tailored to very complex, highly multidimensional data. Over the recent years, numerous tools have been developed for online presentation, but new challenges like the COVID-19 pandemic require new libraries which will guarantee fast development of online tools for a better understanding of biomedical data/results. Results VisuaLife is a Python library that provides a new approach to visualization in a web browser. It offers 2D and 3D plotting capabilities as well as widgets designed to display the most common biological data types: nucleotide or protein sequences, 3D biomolecular structures and multiple sequence alignments. Components provided by the VisuaLife library can be assembled into a web application to create an analysis tool tailored to provide multidimensional analysis of a specific research problem. VisuaLife, to our best knowledge, is the most modern solution that allows one to implement such a client-side interactivity in Python. Availability and implementation The git repository of the library is hosted at BitBucket: https://bitbucket.org/dgront/visualife/. PyPI distribution is also provided for MacOS and Linux. While basic examples are provided in the supporting materials, the full documentation is available at ReadTheDocs website: https://visualife.readthedocs.io/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Justyna D Kryś
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
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8
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Msomi NN, Padayachee T, Nzuza N, Syed PR, Kryś JD, Chen W, Gront D, Nelson DR, Syed K. In Silico Analysis of P450s and Their Role in Secondary Metabolism in the Bacterial Class Gammaproteobacteria. Molecules 2021; 26:1538. [PMID: 33799696 PMCID: PMC7998510 DOI: 10.3390/molecules26061538] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 02/12/2021] [Revised: 03/03/2021] [Accepted: 03/08/2021] [Indexed: 12/21/2022] Open
Abstract
The impact of lifestyle on shaping the genome content of an organism is a well-known phenomenon and cytochrome P450 enzymes (CYPs/P450s), heme-thiolate proteins that are ubiquitously present in organisms, are no exception. Recent studies focusing on a few bacterial species such as Streptomyces, Mycobacterium, Cyanobacteria and Firmicutes revealed that the impact of lifestyle affected the P450 repertoire in these species. However, this phenomenon needs to be understood in other bacterial species. We therefore performed genome data mining, annotation, phylogenetic analysis of P450s and their role in secondary metabolism in the bacterial class Gammaproteobacteria. Genome-wide data mining for P450s in 1261 Gammaproteobacterial species belonging to 161 genera revealed that only 169 species belonging to 41 genera have P450s. A total of 277 P450s found in 169 species grouped into 84 P450 families and 105 P450 subfamilies, where 38 new P450 families were found. Only 18% of P450s were found to be involved in secondary metabolism in Gammaproteobacterial species, as observed in Firmicutes as well. The pathogenic or commensal lifestyle of Gammaproteobacterial species influences them to such an extent that they have the lowest number of P450s compared to other bacterial species, indicating the impact of lifestyle on shaping the P450 repertoire. This study is the first report on comprehensive analysis of P450s in Gammaproteobacteria.
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Affiliation(s)
- Ntombizethu Nokuphiwa Msomi
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa; (N.N.M.); (T.P.); (N.N.)
| | - Tiara Padayachee
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa; (N.N.M.); (T.P.); (N.N.)
| | - Nomfundo Nzuza
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa; (N.N.M.); (T.P.); (N.N.)
| | - Puleng Rosinah Syed
- Department of Pharmaceutical Chemistry, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa;
| | - Justyna Dorota Kryś
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland;
| | - Wanping Chen
- Department of Molecular Microbiology and Genetics, University of Göttingen, 37077 Göttingen, Germany;
| | - Dominik Gront
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland;
| | - David R. Nelson
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Khajamohiddin Syed
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa; (N.N.M.); (T.P.); (N.N.)
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9
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Mnguni FC, Padayachee T, Chen W, Gront D, Yu JH, Nelson DR, Syed K. More P450s Are Involved in Secondary Metabolite Biosynthesis in Streptomyces Compared to Bacillus, Cyanobacteria, and Mycobacterium. Int J Mol Sci 2020; 21:ijms21134814. [PMID: 32646068 PMCID: PMC7369989 DOI: 10.3390/ijms21134814] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 02/11/2020] [Accepted: 02/13/2020] [Indexed: 12/18/2022] Open
Abstract
Unraveling the role of cytochrome P450 monooxygenases (CYPs/P450s), heme-thiolate proteins present in living and non-living entities, in secondary metabolite synthesis is gaining momentum. In this direction, in this study, we analyzed the genomes of 203 Streptomyces species for P450s and unraveled their association with secondary metabolism. Our analyses revealed the presence of 5460 P450s, grouped into 253 families and 698 subfamilies. The CYP107 family was found to be conserved and highly populated in Streptomyces and Bacillus species, indicating its key role in the synthesis of secondary metabolites. Streptomyces species had a higher number of P450s than Bacillus and cyanobacterial species. The average number of secondary metabolite biosynthetic gene clusters (BGCs) and the number of P450s located in BGCs were higher in Streptomyces species than in Bacillus, mycobacterial, and cyanobacterial species, corroborating the superior capacity of Streptomyces species for generating diverse secondary metabolites. Functional analysis via data mining confirmed that many Streptomyces P450s are involved in the biosynthesis of secondary metabolites. This study was the first of its kind to conduct a comparative analysis of P450s in such a large number (203) of Streptomyces species, revealing the P450s’ association with secondary metabolite synthesis in Streptomyces species. Future studies should include the selection of Streptomyces species with a higher number of P450s and BGCs and explore the biotechnological value of secondary metabolites they produce.
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Affiliation(s)
- Fanele Cabangile Mnguni
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa; (F.C.M.); (T.P.)
| | - Tiara Padayachee
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa; (F.C.M.); (T.P.)
| | - Wanping Chen
- Department of Molecular Microbiology and Genetics, University of Göttingen, 37077 Göttingen, Germany;
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland;
| | - Jae-Hyuk Yu
- Department of Bacteriology, University of Wisconsin-Madison, 3155 MSB, 1550 Linden Drive, Madison, WI 53706, USA;
- Department of Systems Biotechnology, Konkuk University, Seoul 05029, Korea
| | - David R. Nelson
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Correspondence: (D.R.N.); (K.S.)
| | - Khajamohiddin Syed
- Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Zululand, KwaDlangezwa 3886, South Africa; (F.C.M.); (T.P.)
- Correspondence: (D.R.N.); (K.S.)
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10
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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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11
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Abstract
We describe common approaches to atomistic structure modeling with single particle analysis derived cryo-EM maps. Several strategies for atomistic model building and atomistic model fitting methods are discussed, including selection criteria and implementation procedures. In covering basic concepts and caveats, this short perspective aims to help facilitate active discussion between scientists at different levels with diverse backgrounds.
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Affiliation(s)
- Doo Nam Kim
- Computational Biology Team, Biological Science Division, Pacific Northwest National Laboratory, Richland, Washington, 99354, United States
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Karissa Y. Sanbonmatsu
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, 87545, United States
- New Mexico Consortium, Los Alamos, New Mexico, 87544, United States
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12
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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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13
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Macnar JM, Szulc NA, Kryś JD, Badaczewska-Dawid AE, Gront D. BioShell 3.0: Library for Processing Structural Biology Data. Biomolecules 2020; 10:biom10030461. [PMID: 32188163 PMCID: PMC7175226 DOI: 10.3390/biom10030461] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [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: 01/12/2020] [Revised: 03/05/2020] [Accepted: 03/10/2020] [Indexed: 01/11/2023] Open
Abstract
BioShell is an open-source package for processing biological data, particularly focused on structural applications. The package provides parsers, data structures and algorithms for handling and analyzing macromolecular sequences, structures and sequence profiles. The most frequently used routines are accessible by a set of easy-to-use command line utilities for a Linux environment. The full functionality of the package assumes knowledge of C++ or Python to assemble an application using this software library. Since the last publication that announced the version 2.0, the package has been greatly expanded and rewritten in C++ standard 11 (C++11) to improve its modularity and efficiency. A new testing platform has been implemented to continuously test the correctness and integrity of the package. More than two hundred test programs have been published to provide simple examples that can be used as templates. This makes BioShell an easy to use library that greatly speeds up development of bioinformatics applications and web services without compromising computational efficiency.
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Affiliation(s)
- Joanna M. Macnar
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland; (J.M.M.); (N.A.S.); (J.D.K.); (A.E.B.-D.)
- College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Stefana Banacha 2C, 02-097 Warsaw, Poland
| | - Natalia A. Szulc
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland; (J.M.M.); (N.A.S.); (J.D.K.); (A.E.B.-D.)
- Laboratory of Protein Metabolism, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Street, 02-109 Warsaw, Poland
| | - Justyna D. Kryś
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland; (J.M.M.); (N.A.S.); (J.D.K.); (A.E.B.-D.)
| | - Aleksandra E. Badaczewska-Dawid
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland; (J.M.M.); (N.A.S.); (J.D.K.); (A.E.B.-D.)
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland; (J.M.M.); (N.A.S.); (J.D.K.); (A.E.B.-D.)
- Correspondence:
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14
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Kopeć K, Pędziwiatr M, Gront D, Sztatelman O, Sławski J, Łazicka M, Worch R, Zawada K, Makarova K, Nyk M, Grzyb J. Comparison of α-Helix and β-Sheet Structure Adaptation to a Quantum Dot Geometry: Toward the Identification of an Optimal Motif for a Protein Nanoparticle Cover. ACS Omega 2019; 4:13086-13099. [PMID: 31460436 PMCID: PMC6705085 DOI: 10.1021/acsomega.9b00505] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 07/23/2019] [Indexed: 05/31/2023]
Abstract
While quantum dots (QDs) are useful as fluorescent labels, their application in biosciences is limited due to the stability and hydrophobicity of their surface. In this study, we tested two types of proteins for use as a cover for spherical QDs, composed of cadmium selenide. Pumilio homology domain (Puf), which is mostly α-helical, and leucine-rich repeat (LRR) domain, which is rich in β-sheets, were selected to determine if there is a preference for one of these secondary structure types for nanoparticle covers. The protein sequences were optimized to improve their interaction with the surface of QDs. The solubilization of the apoproteins and their assembly with nanoparticles required the application of a detergent, which was removed in subsequent steps. Finally, only the Puf-based cover was successful enough as a QD hydrophilic cover. We showed that a single polypeptide dimer of Puf, PufPuf, can form a cover. We characterized the size and fluorescent properties of the obtained QD:protein assemblies. We showed that the secondary structure of the Puf proteins was not destroyed upon contact with the QDs. We demonstrated that these assemblies do not promote the formation of reactive oxygen species during illumination of the nanoparticles. The data represent advances in the effort to obtain a stable biocompatible cover for QDs.
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Affiliation(s)
- Katarzyna Kopeć
- Institute
of Physics, Polish Academy of Sciences, Aleja Lotników 32/46, PL02668 Warsaw, Poland
| | - Marta Pędziwiatr
- Institute
of Physics, Polish Academy of Sciences, Aleja Lotników 32/46, PL02668 Warsaw, Poland
| | - Dominik Gront
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, PL02093 Warsaw, Poland
| | - Olga Sztatelman
- Institute
of Biochemistry and Biophysics, Polish Academy of Sciences, Pawińskiego 5a, PL02106 Warsaw, Poland
| | - Jakub Sławski
- Department
of Biophysics, Faculty of Biotechnology, University of Wrocław, F. Joliot-Curie Street 14a, PL50383 Wrocław, Poland
| | - Magdalena Łazicka
- Department
of Metabolic Regulation, Institute of Biochemistry, Faculty of Biology, University of Warsaw, Miecznikowa 1, PL02096 Warsaw, Poland
| | - Remigiusz Worch
- Institute
of Physics, Polish Academy of Sciences, Aleja Lotników 32/46, PL02668 Warsaw, Poland
| | - Katarzyna Zawada
- Department
of Physical Chemistry, Faculty of Pharmacy with the Laboratory Medicine
Division, The Medical University of Warsaw, Banacha 1 Street, PL02097 Warsaw, Poland
| | - Katerina Makarova
- Department
of Physical Chemistry, Faculty of Pharmacy with the Laboratory Medicine
Division, The Medical University of Warsaw, Banacha 1 Street, PL02097 Warsaw, Poland
| | - Marcin Nyk
- Advanced
Materials Engineering and Modelling Group, Faculty of Chemistry, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego
27, PL50370 Wrocław, Poland
| | - Joanna Grzyb
- Department
of Biophysics, Faculty of Biotechnology, University of Wrocław, F. Joliot-Curie Street 14a, PL50383 Wrocław, Poland
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15
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Migacz S, Dutka K, Gumienny P, Marchwiany M, Gront D, Rudnicki WR. Parallel Implementation of a Sequential Markov Chain in Monte Carlo Simulations of Physical Systems with Pairwise Interactions. J Chem Theory Comput 2019; 15:2797-2806. [PMID: 30908037 DOI: 10.1021/acs.jctc.8b01168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In molecular simulations performed by Markov Chain Monte Carlo (typically employing the Metropolis criterion), each state of a system is obtained by a small random modification of the previous state. Therefore, the process consists of an immense number of small, quick to calculate steps, which are inherently sequential and hence considered to be very hard to parallelise. Here, we present a novel protocol for efficient calculation of multiple sequential steps in parallel. To this end, we first precompute in parallel energy components of all states achievable in a sequence of steps. Then we select a single path through all achievable states, which is identical with the path obtained with the sequential algorithm. As an example, we carried out simulations of the TIP5P water model with the new protocol and compared results with those obtained using the standard Metropolis Monte Carlo scheme. The implementation on the Titan X (Pascal) graphic processor (GPU) architectures allows for a 30-fold speedup in comparison with a simulation on a single core of a multicore CPU. The protocol is general and not limited to the GPU; it can also be used on multicore CPU when the longest possible length of the single simulation is required.
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Affiliation(s)
- Szymon Migacz
- Interdisciplinary Centre for Mathematical and Computational Modelling , University of Warsaw , Warsaw , Poland
| | - Kajetan Dutka
- Interdisciplinary Centre for Mathematical and Computational Modelling , University of Warsaw , Warsaw , Poland
| | - Przemysław Gumienny
- Interdisciplinary Centre for Mathematical and Computational Modelling , University of Warsaw , Warsaw , Poland
| | - Maciej Marchwiany
- Interdisciplinary Centre for Mathematical and Computational Modelling , University of Warsaw , Warsaw , Poland
| | - Dominik Gront
- Department of Chemistry , University of Warsaw , Warsaw , Poland
| | - Witold R Rudnicki
- Interdisciplinary Centre for Mathematical and Computational Modelling , University of Warsaw , Warsaw , Poland.,Institute of Informatics , University of Białystok , Białystok , Poland
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16
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Blaszczyk M, Gront D, Kmiecik S, Kurcinski M, Kolinski M, Ciemny MP, Ziolkowska K, Panek M, Kolinski A. Protein Structure Prediction Using Coarse-Grained Models. Springer Series on Bio- and Neurosystems 2019. [DOI: 10.1007/978-3-319-95843-9_2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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17
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Sicinska W, Gront D, Sicinski K. Mutation goals in the vitamin D receptor predicted by computational methods. J Steroid Biochem Mol Biol 2018; 183:210-220. [PMID: 29966696 DOI: 10.1016/j.jsbmb.2018.06.016] [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] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 05/21/2018] [Accepted: 06/27/2018] [Indexed: 10/28/2022]
Abstract
The mechanism through which nuclear receptors respond differentially to structurally distinct agonists is a poorly understood process. We present a computational method that identifies nuclear receptor amino acids that are likely involved in biological responses triggered by ligand binding. The method involves tracing how structural changes spread from the ligand binding pocket to the sites on the receptor surface, which makes it a good tool for studying allosteric effects. We employ the method to the vitamin D receptor and verify that the identified amino acids are biologically relevant using a broad range of experimental data and a genome browser. We infer that surface vitamin D receptor residues K141, R252, I260, T280, T287 and L417 are likely involved in cell differentiation and antiproliferation, whereas P122, D149, K321, E353 and Q385 are linked to carcinogenesis.
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Affiliation(s)
- Wanda Sicinska
- Department of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland.
| | - Dominik Gront
- Department of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Kamil Sicinski
- Center for Demography of Health and Aging, University of Wisconsin-Madison, 1180 Observatory Drive, Madison, WI 53706, United States
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18
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Dawid AE, Gront D, Kolinski A. Coarse-Grained Modeling of the Interplay between Secondary Structure Propensities and Protein Fold Assembly. J Chem Theory Comput 2018; 14:2277-2287. [PMID: 29486120 DOI: 10.1021/acs.jctc.7b01242] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We recently developed a new coarse-grained model of protein structure and dynamics [ Dawid et al. J. Chem. Theory Comput. 2017 , 13 ( 11 ), 5766 - 5779 ]. The model assumed a single bead representation of amino acid residues, where positions of such united residues were defined by centers of mass of four amino acid fragments. Replica exchange Monte Carlo sampling of the model chains provided good pictures of modeled structures and their dynamics. In its generic form the statistical knowledge-based force field of the model has been dedicated for single-domain globular proteins. Sequence-specific interactions are defined by three-letter secondary structure data. In the present work we demonstrate that different assignments and/or predictions of secondary structures are usually sufficient for enforcing cooperative formation of native-like folds of SURPASS chains for the majority of single-domain globular proteins. Simulations of native-like structure assembly for a representative set of globular proteins have shown that the accuracy of secondary structure data is usually not crucial for model performance, although some specific errors can strongly distort the obtained three-dimensional structures.
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Affiliation(s)
- Aleksandra E Dawid
- Faculty of Chemistry, Biological and Chemical Research Center , University of Warsaw , Pasteura 1 , 02-093 Warsaw , Poland
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center , University of Warsaw , Pasteura 1 , 02-093 Warsaw , Poland
| | - Andrzej Kolinski
- Faculty of Chemistry, Biological and Chemical Research Center , University of Warsaw , Pasteura 1 , 02-093 Warsaw , Poland
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19
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Dawid AE, Koliński A, Gront D. Novel Coarse-Graining Approaches for Large Scale Protein Modeling. Biophys J 2018. [DOI: 10.1016/j.bpj.2017.11.3141] [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: 11/15/2022] Open
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20
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Abstract
Coarse-grained modeling of biomolecules has a very important role in molecular biology. In this work we present a novel SURPASS (Single United Residue per Pre-Averaged Secondary Structure fragment) model of proteins that can be an interesting alternative for existing coarse-grained models. The design of the model is unique and strongly supported by the statistical analysis of structural regularities characteristic for protein systems. Coarse-graining of protein chain structures assumes a single center of interactions per residue and accounts for preaveraged effects of four adjacent residue fragments. Knowledge-based statistical potentials encode complex interaction patterns of these fragments. Using the Replica Exchange Monte Carlo sampling scheme and a generic version of the SURPASS force field we performed test simulations of a representative set of single-domain globular proteins. The method samples a significant part of conformational space and reproduces protein structures, including native-like, with surprisingly good accuracy. Future extension of the SURPASS model on large biomacromolecular systems is briefly discussed.
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Affiliation(s)
- Aleksandra E Dawid
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
| | - Andrzej Kolinski
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
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21
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Matowane RG, Wieteska L, Bamal HD, Kgosiemang IKR, Van Wyk M, Manume NA, Abdalla SMH, Mashele SS, Gront D, Syed K. In silico analysis of cytochrome P450 monooxygenases in chronic granulomatous infectious fungus Sporothrix schenckii: Special focus on CYP51. Biochim Biophys Acta Proteins Proteom 2017; 1866:166-177. [PMID: 28989052 DOI: 10.1016/j.bbapap.2017.10.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2017] [Revised: 09/29/2017] [Accepted: 10/02/2017] [Indexed: 01/19/2023]
Abstract
Sporotrichosis is an emerging chronic, granulomatous, subcutaneous, mycotic infection caused by Sporothrix species. Sporotrichosis is treated with the azole drug itraconazole as ketoconazole is ineffective. It is a well-known fact that azole drugs act by inhibiting cytochrome P450 monooxygenases (P450s), heme-thiolate proteins. To date, nothing is known about P450s in Sporothrix schenckii and the molecular basis of its resistance to ketoconazole. Here we present genome-wide identification, annotation, phylogenetic analysis and comprehensive P450 family-level comparative analysis of S. schenckii P450s with pathogenic fungi P450s, along with a rationale for ketoconazole resistance by S. schenckii based on in silico structural analysis of CYP51. Genome data-mining of S. schenckii revealed 40 P450s in its genome that can be grouped into 32 P450 families and 39 P450 subfamilies. Comprehensive comparative analysis of P450s revealed that S. schenckii shares 11 P450 families with plant pathogenic fungi and has three unique P450 families: CYP5077, CYP5386 and CYP5696 (novel family). Among P450s, CYP51, the main target of azole drugs was also found in S. schenckii. 3D modeling of S. schenckii CYP51 revealed the presence of characteristic P450 motifs with exceptionally large reductase interaction site 2. In silico analysis revealed number of mutations that can be associated with ketoconazole resistance, especially at the channel entrance to the active site. One of possible reason for better stabilization of itraconazole, compared to ketoconazole, is that the more extended molecule of itraconazole may form a hydrogen bond with ASN-230. This in turn may explain its effectiveness against S. schenckii vis-a-vis resistant to ketoconazole. This article is part of a Special Issue entitled: Cytochrome P450 biodiversity and biotechnology, edited by Erika Plettner, Gianfranco Gilardi, Luet Wong, Vlada Urlacher, Jared Goldstone.
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Affiliation(s)
- Retshedisitswe Godfrey Matowane
- Unit for Drug Discovery Research, Department of Health Sciences, Faculty of Health and Environmental Sciences, Central University of Technology, Bloemfontein 9300, Free State, South Africa
| | - Lukasz Wieteska
- Laboratory of Theory of Biopolymers, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Hans Denis Bamal
- Unit for Drug Discovery Research, Department of Health Sciences, Faculty of Health and Environmental Sciences, Central University of Technology, Bloemfontein 9300, Free State, South Africa
| | - Ipeleng Kopano Rosinah Kgosiemang
- Unit for Drug Discovery Research, Department of Health Sciences, Faculty of Health and Environmental Sciences, Central University of Technology, Bloemfontein 9300, Free State, South Africa
| | - Mari Van Wyk
- Unit for Drug Discovery Research, Department of Health Sciences, Faculty of Health and Environmental Sciences, Central University of Technology, Bloemfontein 9300, Free State, South Africa
| | - Nessie Agnes Manume
- Unit for Drug Discovery Research, Department of Health Sciences, Faculty of Health and Environmental Sciences, Central University of Technology, Bloemfontein 9300, Free State, South Africa
| | - Sara Mohamed Hasaan Abdalla
- Unit for Drug Discovery Research, Department of Health Sciences, Faculty of Health and Environmental Sciences, Central University of Technology, Bloemfontein 9300, Free State, South Africa
| | - Samson Sitheni Mashele
- Unit for Drug Discovery Research, Department of Health Sciences, Faculty of Health and Environmental Sciences, Central University of Technology, Bloemfontein 9300, Free State, South Africa
| | - Dominik Gront
- Laboratory of Theory of Biopolymers, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Khajamohiddin Syed
- Unit for Drug Discovery Research, Department of Health Sciences, Faculty of Health and Environmental Sciences, Central University of Technology, Bloemfontein 9300, Free State, South Africa.
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22
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Gront D. Optimal Temperature Set for Replica Exchange Sampling. Biophys J 2017. [DOI: 10.1016/j.bpj.2016.11.287] [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: 11/28/2022] Open
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23
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Janna Olmos JD, Becquet P, Gront D, Sar J, Dąbrowski A, Gawlik G, Teodorczyk M, Pawlak D, Kargul J. Biofunctionalisation of p-doped silicon with cytochrome c553minimises charge recombination and enhances photovoltaic performance of the all-solid-state photosystem I-based biophotoelectrode. RSC Adv 2017. [DOI: 10.1039/c7ra10895h] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Passivation of p-doped silicon substrate was achieved by its biofunctionalisation with hexahistidine-tagged cytochrome c553, a soluble electroactive photosynthetic protein responsible for electron donation to photooxidised photosystem I.
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Affiliation(s)
| | | | - Dominik Gront
- Laboratory of Theory of Biopolymers
- Faculty of Chemistry
- University of Warsaw
- 02-093 Warsaw
- Poland
| | - Jarosław Sar
- Institute of Electronic Materials Technology
- 01-919 Warsaw
- Poland
| | | | - Grzegorz Gawlik
- Institute of Electronic Materials Technology
- 01-919 Warsaw
- Poland
| | | | - Dorota Pawlak
- Institute of Electronic Materials Technology
- 01-919 Warsaw
- Poland
- Laboratory of Materials Technology
- Centre for New Technologies
| | - Joanna Kargul
- Solar Fuels Laboratory
- Centre for New Technologies
- University of Warsaw
- 02-097 Warsaw
- Poland
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Affiliation(s)
- Sebastian Kmiecik
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Dominik Gront
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Michal Kolinski
- Bioinformatics
Laboratory, Mossakowski Medical Research Center of the Polish Academy of Sciences, Pawinskiego 5, 02-106 Warsaw, Poland
| | - Lukasz Wieteska
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
- Department
of Medical Biochemistry, Medical University of Lodz, Mazowiecka 6/8, 92-215 Lodz, Poland
| | | | - Andrzej Kolinski
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
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25
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Gniewek P, Kolinski A, Kloczkowski A, Gront D. BioShell-Threading: versatile Monte Carlo package for protein 3D threading. BMC Bioinformatics 2014; 15:22. [PMID: 24444459 PMCID: PMC3937128 DOI: 10.1186/1471-2105-15-22] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [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: 11/25/2012] [Accepted: 11/18/2013] [Indexed: 11/26/2022] Open
Abstract
Background The comparative modeling approach to protein structure prediction inherently relies on a template structure. Before building a model such a template protein has to be found and aligned with the query sequence. Any error made on this stage may dramatically affects the quality of result. There is a need, therefore, to develop accurate and sensitive alignment protocols. Results BioShell threading software is a versatile tool for aligning protein structures, protein sequences or sequence profiles and query sequences to a template structures. The software is also capable of sub-optimal alignment generation. It can be executed as an application from the UNIX command line, or as a set of Java classes called from a script or a Java application. The implemented Monte Carlo search engine greatly facilitates the development and benchmarking of new alignment scoring schemes even when the functions exhibit non-deterministic polynomial-time complexity. Conclusions Numerical experiments indicate that the new threading application offers template detection abilities and provides much better alignments than other methods. The package along with documentation and examples is available at: http://bioshell.pl/threading3d.
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Affiliation(s)
| | | | | | - Dominik Gront
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland.
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26
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Gront D. Modular Platform for Biomolecular Modeling and Simulations. Biophys J 2014. [DOI: 10.1016/j.bpj.2013.11.3633] [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: 11/24/2022] Open
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27
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Kloczkowski A, Gniewek P, Faraggi E, Zimmermann M, Gront D, Pawlowski M, Jernigan RL, Kolinski A. New Methods to Improve Protein Structure Prediction and Refinement. Biophys J 2013. [DOI: 10.1016/j.bpj.2012.11.1292] [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: 11/28/2022] Open
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28
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Gront D, Kloczkowski A. De Novo Protein Structure Determination from Incomplete Experimental Data. Biophys J 2013. [DOI: 10.1016/j.bpj.2012.11.1289] [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: 11/26/2022] Open
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29
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Abstract
The development of automatic approaches for the comparison of protein sequences has become increasingly important. Methods that compare profiles allow for the use of information about whole protein families, resulting in more sensitive and accurate detection of distantly related sequences. In this contribution, we describe a thorough optimization and tests of a profile-to-profile alignment method. A number of different scoring schemes has been implemented and compared on the basis of their ability to identify a template protein from the same SCOP family as a query. In addition to sequence profiles, secondary structure profiles were used to increase the rate of successful detection. Our results show that a properly tuned one-dimensional threading method can recognize a correct template from the same SCOP family nearly as well as structural alignment. Our benchmark set, which might be useful in other similar studies, as well as the fold-recognition software we developed may be downloaded (www.bioshell.pl/profile-alignments).
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Affiliation(s)
- Pawel Gniewek
- Faculty of Chemistry, Warsaw University, Warsaw, Poland
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30
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Gront D, Blaszczyk M, Wojciechowski P, Kolinski A. BioShell Threader: protein homology detection based on sequence profiles and secondary structure profiles. Nucleic Acids Res 2012; 40:W257-62. [PMID: 22693216 PMCID: PMC3394251 DOI: 10.1093/nar/gks555] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [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] [Indexed: 11/21/2022] Open
Abstract
The BioShell package has recently been extended with a web server for protein homology detection based on profile-to-profile alignment (known as 1D threading). Its aim is to assign structural templates to each domain of the query. The server uses sequence profiles that describe observed sequence variability and secondary structure profiles providing expected probability for a certain secondary structure type at a given position in a protein. Three independent predictors are used to increase the rate of successful predictions. Careful evaluation shows that there is nearly 80% chance that the query sequence belongs to the same SCOP family as the top scoring template. The Bioshell Threader server is freely available at: http://www.bioshell.pl/threader/.
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Affiliation(s)
- Dominik Gront
- University of Warsaw, Faculty of Chemistry, Pasteura 1, 02-093 Warsaw, Poland.
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31
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Kmiecik S, Gront D, Kouza M, Kolinski A. From coarse-grained to atomic-level characterization of protein dynamics: transition state for the folding of B domain of protein A. J Phys Chem B 2012; 116:7026-32. [PMID: 22486297 DOI: 10.1021/jp301720w] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Atomic-level molecular dynamics simulations are widely used for the characterization of the structural dynamics of proteins; however, they are limited to shorter time scales than the duration of most of the relevant biological processes. Properly designed coarse-grained models that trade atomic resolution for efficient sampling allow access to much longer time-scales. In-depth understanding of the structural dynamics, however, must involve atomic details. In this study, we tested a method for the rapid reconstruction of all-atom models from α carbon atom positions in the application to convert a coarse-grained folding trajectory of a well described model system: the B domain of protein A. The results show that the method and the spatial resolution of the resulting coarse-grained models enable computationally inexpensive reconstruction of realistic all-atom models. Additionally, by means of structural clustering, we determined the most persistent ensembles of the key folding step, the transition state. Importantly, the analysis of the overall structural topologies suggests a dominant folding pathway. This, together with the all-atom characterization of the obtained ensembles, in the form of contact maps, matches the experimental results well.
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Affiliation(s)
- Sebastian Kmiecik
- Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland.
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Affiliation(s)
- Dominik Gront
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Sebastian Kmiecik
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Maciej Blaszczyk
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Dariusz Ekonomiuk
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Andrzej Koliński
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
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Gront D, Kulp DW, Vernon RM, Strauss CEM, Baker D. Generalized fragment picking in Rosetta: design, protocols and applications. PLoS One 2011; 6:e23294. [PMID: 21887241 PMCID: PMC3160850 DOI: 10.1371/journal.pone.0023294] [Citation(s) in RCA: 132] [Impact Index Per Article: 10.2] [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: 04/22/2011] [Accepted: 07/12/2011] [Indexed: 11/21/2022] Open
Abstract
The Rosetta de novo structure prediction and loop modeling protocols begin with coarse grained Monte Carlo searches in which the moves are based on short fragments extracted from a database of known structures. Here we describe a new object oriented program for picking fragments that greatly extends the functionality of the previous program (nnmake) and opens the door for new approaches to structure modeling. We provide a detailed description of the code design and architecture, highlighting its modularity, and new features such as extensibility, total control over the fragment picking workflow and scoring system customization. We demonstrate that the program provides at least as good building blocks for ab-initio structure prediction as the previous program, and provide examples of the wide range of applications that are now accessible.
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Affiliation(s)
- Dominik Gront
- Faculty of Chemistry, University of Warsaw, Warsaw, Poland.
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Wang S, Kirillova O, Chruszcz M, Gront D, Zimmerman MD, Cymborowski MT, Shumilin IA, Skarina T, Gorodichtchenskaia E, Savchenko A, Edwards AM, Minor W. The crystal structure of the AF2331 protein from Archaeoglobus fulgidus DSM 4304 forms an unusual interdigitated dimer with a new type of alpha + beta fold. Protein Sci 2009; 18:2410-9. [PMID: 19768810 PMCID: PMC2788295 DOI: 10.1002/pro.251] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [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: 07/31/2009] [Accepted: 09/09/2009] [Indexed: 11/10/2022]
Abstract
The structure of AF2331, a 11-kDa orphan protein of unknown function from Archaeoglobus fulgidus, was solved by Se-Met MAD to 2.4 A resolution. The structure consists of an alpha + beta fold formed by an unusual homodimer, where the two core beta-sheets are interdigitated, containing strands alternating from both subunits. The decrease in solvent-accessible surface area upon dimerization is unusually large (3960 A(2)) for a protein of its size. The percentage of the total surface area buried in the interface (41.1%) is one of the largest observed in a nonredundant set of homodimers in the PDB and is above the mean for nearly all other types of homo-oligomers. AF2331 has no sequence homologs, and no structure similar to AF2331 could be found in the PDB using the CE, TM-align, DALI, or SSM packages. The protein has been identified in Pfam 23.0 as the archetype of a new superfamily and is topologically dissimilar to all other proteins with the "3-Layer (BBA) Sandwich" fold in CATH. Therefore, we propose that AF2331 forms a novel alpha + beta fold. AF2331 contains multiple negatively charged surface clusters and is located on the same operon as the basic protein AF2330. We hypothesize that AF2331 and AF2330 may form a charge-stabilized complex in vivo, though the role of the negatively charged surface clusters is not clear.
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Affiliation(s)
- Shuren Wang
- Department of Molecular Physiology and Biological Physics, University of VirginiaCharlottesville, Virginia 22908
- Midwest Center for Structural Genomics, University of TorontoToronto, Ontario M5G 1L6, Canada
| | - Olga Kirillova
- Department of Molecular Physiology and Biological Physics, University of VirginiaCharlottesville, Virginia 22908
- Midwest Center for Structural Genomics, University of TorontoToronto, Ontario M5G 1L6, Canada
| | - Maksymilian Chruszcz
- Department of Molecular Physiology and Biological Physics, University of VirginiaCharlottesville, Virginia 22908
- Midwest Center for Structural Genomics, University of TorontoToronto, Ontario M5G 1L6, Canada
| | - Dominik Gront
- Department of Molecular Physiology and Biological Physics, University of VirginiaCharlottesville, Virginia 22908
- Midwest Center for Structural Genomics, University of TorontoToronto, Ontario M5G 1L6, Canada
| | - Matthew D Zimmerman
- Department of Molecular Physiology and Biological Physics, University of VirginiaCharlottesville, Virginia 22908
- Midwest Center for Structural Genomics, University of TorontoToronto, Ontario M5G 1L6, Canada
| | - Marcin T Cymborowski
- Department of Molecular Physiology and Biological Physics, University of VirginiaCharlottesville, Virginia 22908
- Midwest Center for Structural Genomics, University of TorontoToronto, Ontario M5G 1L6, Canada
| | - Igor A Shumilin
- Department of Molecular Physiology and Biological Physics, University of VirginiaCharlottesville, Virginia 22908
- Midwest Center for Structural Genomics, University of TorontoToronto, Ontario M5G 1L6, Canada
| | - Tatiana Skarina
- Midwest Center for Structural Genomics, University of TorontoToronto, Ontario M5G 1L6, Canada
- Banting and Best Department of Medical Research, University of TorontoToronto, Ontario M5G 1L6, Canada
| | - Elena Gorodichtchenskaia
- Midwest Center for Structural Genomics, University of TorontoToronto, Ontario M5G 1L6, Canada
- Banting and Best Department of Medical Research, University of TorontoToronto, Ontario M5G 1L6, Canada
| | - Alexei Savchenko
- Midwest Center for Structural Genomics, University of TorontoToronto, Ontario M5G 1L6, Canada
- Banting and Best Department of Medical Research, University of TorontoToronto, Ontario M5G 1L6, Canada
| | - Aled M Edwards
- Midwest Center for Structural Genomics, University of TorontoToronto, Ontario M5G 1L6, Canada
- Banting and Best Department of Medical Research, University of TorontoToronto, Ontario M5G 1L6, Canada
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of VirginiaCharlottesville, Virginia 22908
- Midwest Center for Structural Genomics, University of TorontoToronto, Ontario M5G 1L6, Canada
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Gront D, Kolinski A. Fast and accurate methods for predicting short-range constraints in protein models. J Comput Aided Mol Des 2008; 22:783-8. [PMID: 18415023 DOI: 10.1007/s10822-008-9213-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2007] [Accepted: 03/25/2008] [Indexed: 11/28/2022]
Abstract
Protein modeling tools utilize many kinds of structural information that may be predicted from amino acid sequence of a target protein or obtained from experiments. Such data provide geometrical constraints in a modeling process. The main aim is to generate the best possible consensus structure. The quality of models strictly depends on the imposed conditions. In this work we present an algorithm, which predicts short-range distances between Calpha atoms as well as a set of short structural fragments that possibly share structural similarity with a query sequence. The only input of the method is a query sequence profile. The algorithm searches for short protein fragments with high sequence similarity. As a result a statistics of distances observed in the similar fragments is returned. The method can be used also as a scoring function or a short-range knowledge-based potential based on the computed statistics.
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Affiliation(s)
- Dominik Gront
- Faculty of Chemistry, University of Warsaw, Warsaw, Poland.
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Abstract
MOTIVATION The number of known protein sequences is about thousand times larger than the number of experimentally solved 3D structures. For more than half of the protein sequences a close or distant structural analog could be identified. The key starting point in a classical comparative modeling is to generate the best possible sequence alignment with a template or templates. With decreasing sequence similarity, the number of errors in the alignments increases and these errors are the main causes of the decreasing accuracy of the molecular models generated. Here we propose a new approach to comparative modeling, which does not require the implicit alignment - the model building phase explores geometric, evolutionary and physical properties of a template (or templates). RESULTS The proposed method requires prior identification of a template, although the initial sequence alignment is ignored. The model is built using a very efficient reduced representation search engine CABS to find the best possible superposition of the query protein onto the template represented as a 3D multi-featured scaffold. The criteria used include: sequence similarity, predicted secondary structure consistency, local geometric features and hydrophobicity profile. For more difficult cases, the new method qualitatively outperforms existing schemes of comparative modeling. The algorithm unifies de novo modeling, 3D threading and sequence-based methods. The main idea is general and could be easily combined with other efficient modeling tools as Rosetta, UNRES and others.
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Affiliation(s)
- Andrzej Kolinski
- University of Warsaw, Faculty of Chemistry, Pasteura 1 02-093 Warsaw, Poland
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Gront D, Kmiecik S, Kolinski A. Backbone building from quadrilaterals: a fast and accurate algorithm for protein backbone reconstruction from alpha carbon coordinates. J Comput Chem 2007; 28:1593-1597. [PMID: 17342707 DOI: 10.1002/jcc.20624] [Citation(s) in RCA: 76] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this contribution, we present an algorithm for protein backbone reconstruction that comprises very high computational efficiency with high accuracy. Reconstruction of the main chain atomic coordinates from the alpha carbon trace is a common task in protein modeling, including de novo structure prediction, comparative modeling, and processing experimental data. The method employed in this work follows the main idea of some earlier approaches to the problem. The details and careful design of the present approach are new and lead to the algorithm that outperforms all commonly used earlier applications. BBQ (Backbone Building from Quadrilaterals) program has been extensively tested both on native structures as well as on near-native decoy models and compared with the different available existing methods. Obtained results provide a comprehensive benchmark of existing tools and evaluate their applicability to a large scale modeling using a reduced representation of protein conformational space. The BBQ package is available for downloading from our website at http://biocomp.chem.uw.edu.pl/services/BBQ/. This webpage also provides a user manual that describes BBQ functions in detail.
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Affiliation(s)
- Dominik Gront
- Faculty of Chemistry, Warsaw University, Pasteura 1 02-093, Warsaw
| | | | - Andrzej Kolinski
- Faculty of Chemistry, Warsaw University, Pasteura 1 02-093, Warsaw
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Abstract
UNLABELLED Molecular dynamics and Monte Carlo, usually conducted in canonical ensemble, deliver a plethora of biomolecular conformations. Proper analysis of the simulation data is a crucial part of biophysical and bioinformatics studies. Sequence alignment problem can be also formulated in terms of Boltzmann distribution. Therefore tools for efficient analysis of canonical ensemble data become extremely valuable. T-Pile package, presented here provides a user-friendly implementation of most important algorithms such as multihistogram analysis and reweighting technique. The package can be used in studies of virtually any system governed by Boltzmann distribution. AVAILABILITY T-Pile can be downloaded from: http://biocomp.chem.uw.edu.pl/services/tpile. These pages provide a comprehensive tutorial and documentation with illustrative examples of applications. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dominik Gront
- Warsaw University, Faculty of Chemistry, Pasteura 1 02-093 Warsaw, Poland.
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Gront D, Kurcinski M, Kolinski A. Clustering as a supporting tool for structural drug design. Acta Pol Pharm 2006; 63:436-8. [PMID: 17357607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Affiliation(s)
- Dominik Gront
- Warsaw University, Faculty of Chemistry, Pasteura 1, 02-093 Warsaw, Poland
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Abstract
SUMMARY BioShell is a suite of programs performing common tasks accompanying protein structure modeling. BioShell design is based on UNIX shell flexibility and should be used as its extension. Using BioShell various molecular modeling procedures can be integrated in a single pipeline. AVAILABILITY BioShell package can be downloaded from its website http://biocomp.chem.uw.edu.pl/BioShell and these pages provide many examples and a detailed documentation for the newest version.
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Affiliation(s)
- Dominik Gront
- Faculty of Chemistry, Warsaw University Pasteura 1, 02-093 Warsaw, Poland.
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Kmiecik S, Kurcinski M, Rutkowska A, Gront D, Kolinski A. Denatured proteins and early folding intermediates simulated in a reduced conformational space. Acta Biochim Pol 2006; 53:131-44. [PMID: 16365636] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2005] [Revised: 10/05/2005] [Accepted: 11/06/2005] [Indexed: 05/05/2023]
Abstract
Conformations of globular proteins in the denatured state were studied using a high-resolution lattice model of proteins and Monte Carlo dynamics. The model assumes a united-atom and high-coordination lattice representation of the polypeptide conformational space. The force field of the model mimics the short-range protein-like conformational stiffness, hydrophobic interactions of the side chains and the main-chain hydrogen bonds. Two types of approximations for the short-range interactions were compared: simple statistical potentials and knowledge-based protein-specific potentials derived from the sequence-structure compatibility of short fragments of protein chains. Model proteins in the denatured state are relatively compact, although the majority of the sampled conformations are globally different from the native fold. At the same time short protein fragments are mostly native-like. Thus, the denatured state of the model proteins has several features of the molten globule state observed experimentally. Statistical potentials induce native-like conformational propensities in the denatured state, especially for the fragments located in the core of folded proteins. Knowledge-based protein-specific potentials increase only slightly the level of similarity to the native conformations, in spite of their qualitatively higher specificity in the native structures. For a few cases, where fairly accurate experimental data exist, the simulation results are in semiquantitative agreement with the physical picture revealed by the experiments. This shows that the model studied in this work could be used efficiently in computational studies of protein dynamics in the denatured state, and consequently for studies of protein folding pathways, i.e. not only for the modeling of folded structures, as it was shown in previous studies. The results of the present studies also provide a new insight into the explanation of the Levinthal's paradox.
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Kmiecik S, Kurcinski M, Rutkowska A, Gront D, Kolinski A. Denatured proteins and early folding intermediates simulated in a reduced conformational space. Acta Biochim Pol 2005. [DOI: 10.18388/abp.2006_3371] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Conformations of globular proteins in the denatured state were studied using a high-resolution lattice model of proteins and Monte Carlo dynamics. The model assumes a united-atom and high-coordination lattice representation of the polypeptide conformational space. The force field of the model mimics the short-range protein-like conformational stiffness, hydrophobic interactions of the side chains and the main-chain hydrogen bonds. Two types of approximations for the short-range interactions were compared: simple statistical potentials and knowledge-based protein-specific potentials derived from the sequence-structure compatibility of short fragments of protein chains. Model proteins in the denatured state are relatively compact, although the majority of the sampled conformations are globally different from the native fold. At the same time short protein fragments are mostly native-like. Thus, the denatured state of the model proteins has several features of the molten globule state observed experimentally. Statistical potentials induce native-like conformational propensities in the denatured state, especially for the fragments located in the core of folded proteins. Knowledge-based protein-specific potentials increase only slightly the level of similarity to the native conformations, in spite of their qualitatively higher specificity in the native structures. For a few cases, where fairly accurate experimental data exist, the simulation results are in semiquantitative agreement with the physical picture revealed by the experiments. This shows that the model studied in this work could be used efficiently in computational studies of protein dynamics in the denatured state, and consequently for studies of protein folding pathways, i.e. not only for the modeling of folded structures, as it was shown in previous studies. The results of the present studies also provide a new insight into the explanation of the Levinthal's paradox.
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Abstract
The probability to predict correctly a protein structure can be enhanced through introduction of spatial constraints - either from NMR experiments or from homologous structures. However, the additional constraints lead often to new local energy minima and worse sampling efficiency in simulations. In this work, we present a new parallel tempering variant that alleviates the energy barriers resulting from spatial constraints and therefore yields to an enhanced sampling in structure prediction simulations.
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Affiliation(s)
- Dominik Gront
- Department of Physics, Michigan Technological University, Houghton, MI 49931-1295, USA
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Abstract
HCPM is a tool for clustering protein structures from comparative modeling, ab initio structure prediction, etc. A hierarchical clustering algorithm is designed and tested, and a heuristic is provided for an optimal cluster selection. The method has been successfully tested during the CASP6 experiment.
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Affiliation(s)
- Dominik Gront
- Faculty of Chemistry, Warsaw University Pasteura 1, 02-093 Warsaw, Poland.
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Abstract
In this work we present a new method for investigating local energy minima on a protein energy landscape. The CABS (CAlpha, CBeta and the center of mass of the Side chain) method was employed for generating protein models, but any other method could be used instead. Cα traces from an ensemble of models are hierarchical clustered with the HCPM (Hierarchical Clustering of Protein Models) method. The efficiency of this method for sampling and analyzing energy landscapes is shown.
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Abstract
MOTIVATION Knowledge-based potentials are valuable tools for protein structure modeling and evaluation of the quality of the structure prediction obtained by a variety of methods. Potentials of such type could be significantly enhanced by a proper exploitation of the evolutionary information encoded in related protein sequences. The new potentials could be valuable components of threading algorithms, ab-initio protein structure prediction, comparative modeling and structure modeling based on fragmentary experimental data. RESULTS A new potential for scoring local protein geometry is designed and evaluated. The approach is based on the similarity of short protein fragments measured by an alignment of their sequence profiles. Sequence specificity of the resulting energy function has been compared with the specificity of simpler potentials using gapless threading and the ability to predict specific geometry of protein fragments. Significant improvement in threading sensitivity and in the ability to generate sequence-specific protein-like conformations has been achieved.
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Affiliation(s)
- Dominik Gront
- Faculty of Chemistry, Warsaw University Pasteura 1, 02-093 Warsaw, Poland.
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Abstract
In a recent paper (D. Gront et al., Journal of Chemical Physics, Vol. 115, pp. 1569, 2001) we applied a simple combination of the Replica Exchange Monte Carlo and the Histogram methods in the computational studies of a simplified protein lattice model containing hydrophobic and polar units and sequence-dependent local stiffness. A well-defined, relatively complex Greek-key topology, ground (native) conformations was found; however, the cooperativity of the folding transition was very low. Here we describe a modified minimal model of the same Greek-key motif for which the folding transition is very cooperative and has all the features of the "all-or-none" transition typical of real globular proteins. It is demonstrated that the all-or-none transition arises from the interplay between local stiffness and properly defined tertiary interactions. The tertiary interactions are directional, mimicking the packing preferences seen in proteins. The model properties are compared with other minimal protein-like models, and we argue that the model presented here captures essential physics of protein folding (structurally well-defined protein-like native conformation and cooperative all-or-none folding transition).
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Affiliation(s)
- Andrzej Kolinski
- Faculty of Chemistry, Warsaw University, Pasteura 1, 02-093 Warsaw, Poland.
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Gront D, Kolinski A, Skolnick J. A new combination of replica exchange Monte Carlo and histogram analysis for protein folding and thermodynamics. J Chem Phys 2001. [DOI: 10.1063/1.1381062] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Gront D, Kolinski A, Skolnick J. Comparison of three Monte Carlo conformational search strategies for a proteinlike homopolymer model: Folding thermodynamics and identification of low-energy structures. J Chem Phys 2000. [DOI: 10.1063/1.1289533] [Citation(s) in RCA: 62] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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