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Neveu E, Popov P, Hoffmann A, Migliosi A, Besseron X, Danoy G, Bouvry P, Grudinin S. RapidRMSD: rapid determination of RMSDs corresponding to motions of flexible molecules. Bioinformatics 2019; 34:2757-2765. [PMID: 29554205 DOI: 10.1093/bioinformatics/bty160] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 03/13/2018] [Indexed: 12/27/2022] Open
Abstract
Motivation The root mean square deviation (RMSD) is one of the most used similarity criteria in structural biology and bioinformatics. Standard computation of the RMSD has a linear complexity with respect to the number of atoms in a molecule, making RMSD calculations time-consuming for the large-scale modeling applications, such as assessment of molecular docking predictions or clustering of spatially proximate molecular conformations. Previously, we introduced the RigidRMSD algorithm to compute the RMSD corresponding to the rigid-body motion of a molecule. In this study, we go beyond the limits of the rigid-body approximation by taking into account conformational flexibility of the molecule. We model the flexibility with a reduced set of collective motions computed with e.g. normal modes or principal component analysis. Results The initialization of our algorithm is linear in the number of atoms and all the subsequent evaluations of RMSD values between flexible molecular conformations depend only on the number of collective motions that are selected to model the flexibility. Therefore, our algorithm is much faster compared to the standard RMSD computation for large-scale modeling applications. We demonstrate the efficiency of our method on several clustering examples, including clustering of flexible docking results and molecular dynamics (MD) trajectories. We also demonstrate how to use the presented formalism to generate pseudo-random constant-RMSD structural molecular ensembles and how to use these in cross-docking. Availability and implementation We provide the algorithm written in C++ as the open-source RapidRMSD library governed by the BSD-compatible license, which is available at http://team.inria.fr/nano-d/software/RapidRMSD/. The constant-RMSD structural ensemble application and clustering of MD trajectories is available at http://team.inria.fr/nano-d/software/nolb-normal-modes/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Emilie Neveu
- Inria/Univ. Grenoble Alpes/LJK-CNRS, Grenoble, France.,Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Petr Popov
- Inria/Univ. Grenoble Alpes/LJK-CNRS, Grenoble, France.,Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
| | | | - Angelo Migliosi
- Faculté des Sciences, de la Technologie et de la Communication, University of Luxembourg, Luxembourg, Luxembourg
| | - Xavier Besseron
- Faculté des Sciences, de la Technologie et de la Communication, University of Luxembourg, Luxembourg, Luxembourg
| | - Grégoire Danoy
- Faculté des Sciences, de la Technologie et de la Communication, University of Luxembourg, Luxembourg, Luxembourg
| | - Pascal Bouvry
- Faculté des Sciences, de la Technologie et de la Communication, University of Luxembourg, Luxembourg, Luxembourg
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de Araujo CB, Heimann AS, Remer RA, Russo LC, Colquhoun A, Forti FL, Ferro ES. Intracellular Peptides in Cell Biology and Pharmacology. Biomolecules 2019; 9:biom9040150. [PMID: 30995799 PMCID: PMC6523763 DOI: 10.3390/biom9040150] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 04/02/2019] [Accepted: 04/12/2019] [Indexed: 12/11/2022] Open
Abstract
Intracellular peptides are produced by proteasomes following degradation of nuclear, cytosolic, and mitochondrial proteins, and can be further processed by additional peptidases generating a larger pool of peptides within cells. Thousands of intracellular peptides have been sequenced in plants, yeast, zebrafish, rodents, and in human cells and tissues. Relative levels of intracellular peptides undergo changes in human diseases and also when cells are stimulated, corroborating their biological function. However, only a few intracellular peptides have been pharmacologically characterized and their biological significance and mechanism of action remains elusive. Here, some historical and general aspects on intracellular peptides' biology and pharmacology are presented. Hemopressin and Pep19 are examples of intracellular peptides pharmacologically characterized as inverse agonists to cannabinoid type 1 G-protein coupled receptors (CB1R), and hemopressin fragment NFKF is shown herein to attenuate the symptoms of pilocarpine-induced epileptic seizures. Intracellular peptides EL28 (derived from proteasome 26S protease regulatory subunit 4; Rpt2), PepH (derived from Histone H2B type 1-H), and Pep5 (derived from G1/S-specific cyclin D2) are examples of peptides that function intracellularly. Intracellular peptides are suggested as biological functional molecules, and are also promising prototypes for new drug development.
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Affiliation(s)
- Christiane B de Araujo
- Special Laboratory of Cell Cycle, Center of Toxins, Immune Response and Cell Signaling - CeTICS, Butantan Institute, São Paulo SP 05503-900, Brazil.
| | | | | | - Lilian C Russo
- Department of Biochemistry, Chemistry Institute, University of São Paulo 1111, São Paulo 05508-000, Brazil.
| | - Alison Colquhoun
- Department of Cell and Developmental Biology, University of São Paulo (USP), São Paulo 05508-000, Brazil.
| | - Fábio L Forti
- Department of Biochemistry, Chemistry Institute, University of São Paulo 1111, São Paulo 05508-000, Brazil.
| | - Emer S Ferro
- Department of Pharmacology, Biomedical Sciences Institute, University of São Paulo (USP), São Paulo 05508-000, Brazil.
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Thirumuruganandham SP, Gómez EA, Lakshmanan S, Hamblin MR. Terahertz Frequency Spectroscopy to Determine Cold Shock Protein Stability upon Solvation and Evaporation - A Molecular Dynamics Study. IEEE TRANSACTIONS ON TERAHERTZ SCIENCE AND TECHNOLOGY 2017; 7:131-143. [PMID: 30881732 PMCID: PMC6419770 DOI: 10.1109/tthz.2016.2637380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Infrared (IR) and Terahertz (THz) spectroscopy simulations were carried out using CHARMM35b2 to determine protein stability. The stabilities of three bacterial cold shock proteins (Csps) originating from mesophiles, thermophiles and hyper- thermophiles respectively were investigated in this study. The three different Csps were investigated by Normal-Mode analysis and Molecular Dynamics simulation of THz spectra using the Hessian matrix for solvated systems, interpreted in the harmonic approximation at optimum near-melting temperatures of each homologue, by incorporating differences in the hydrous and anhydrous states of the Csps. The results show slight variations in the large scale protein motion. However, the IR spectra of Csps observed at the low frequency saddle surface region, clearly distinguishes the thermophilic and mesophilic proteins based on their stability. Further studies on protein stability employing low-frequency collective modes have the potential to reveal functionally important conformational changes that are biologically significant.
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Affiliation(s)
| | - Edgar A Gómez
- Programa de Física, Universidad del Quindío, Armenia, Colombia
| | - Shanmugamurthy Lakshmanan
- Department of Dermatology, Harvard Medical School, Boston, MA 02114, USA
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Michael R Hamblin
- Department of Dermatology, Harvard Medical School, Boston, MA 02114, USA
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA 02114, USA
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Dietzen M, Kalinina OV, Taškova K, Kneissl B, Hildebrandt AK, Jaenicke E, Decker H, Lengauer T, Hildebrandt A. Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces. Proteins 2015; 83:1887-99. [PMID: 26248608 PMCID: PMC5049452 DOI: 10.1002/prot.24873] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Revised: 07/20/2015] [Accepted: 07/29/2015] [Indexed: 11/06/2022]
Abstract
Macromolecular oligomeric assemblies are involved in many biochemical processes of living organisms. The benefits of such assemblies in crowded cellular environments include increased reaction rates, efficient feedback regulation, cooperativity and protective functions. However, an atom-level structural determination of large assemblies is challenging due to the size of the complex and the difference in binding affinities of the involved proteins. In this study, we propose a novel combinatorial greedy algorithm for assembling large oligomeric complexes from information on the approximate position of interaction interfaces of pairs of monomers in the complex. Prior information on complex symmetry is not required but rather the symmetry is inferred during assembly. We implement an efficient geometric score, the transformation match score, that bypasses the model ranking problems of state-of-the-art scoring functions by scoring the similarity between the inferred dimers of the same monomer simultaneously with different binding partners in a (sub)complex with a set of pregenerated docking poses. We compiled a diverse benchmark set of 308 homo and heteromeric complexes containing 6 to 60 monomers. To explore the applicability of the method, we considered 48 sets of parameters and selected those three sets of parameters, for which the algorithm can correctly reconstruct the maximum number, namely 252 complexes (81.8%) in, at least one of the respective three runs. The crossvalidation coverage, that is, the mean fraction of correctly reconstructed benchmark complexes during crossvalidation, was 78.1%, which demonstrates the ability of the presented method to correctly reconstruct topology of a large variety of biological complexes.
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Affiliation(s)
- Matthias Dietzen
- Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany
| | - Olga V Kalinina
- Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany
| | - Katerina Taškova
- Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany.,Institute for Molecular Biology, Johannes Gutenberg University, Ackermannweg 4, Mainz, 55128, Germany
| | - Benny Kneissl
- Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany.,Roche Pharma Research and Early Development, pRED Informatics, Roche Innovation Center Penzberg, Nonnenwald 2, Penzberg, 82377, Germany
| | | | - Elmar Jaenicke
- Institute of Molecular Biophysics, Johannes Gutenberg University, Jakob-Welder-Weg 26, Mainz, 55128, Germany
| | - Heinz Decker
- Institute of Molecular Biophysics, Johannes Gutenberg University, Jakob-Welder-Weg 26, Mainz, 55128, Germany
| | - Thomas Lengauer
- Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany
| | - Andreas Hildebrandt
- Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany
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Hildebrandt AK, Stöckel D, Fischer NM, de la Garza L, Krüger J, Nickels S, Röttig M, Schärfe C, Schumann M, Thiel P, Lenhof HP, Kohlbacher O, Hildebrandt A. ballaxy: web services for structural bioinformatics. ACTA ACUST UNITED AC 2014; 31:121-2. [PMID: 25183489 DOI: 10.1093/bioinformatics/btu574] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION Web-based workflow systems have gained considerable momentum in sequence-oriented bioinformatics. In structural bioinformatics, however, such systems are still relatively rare; while commercial stand-alone workflow applications are common in the pharmaceutical industry, academic researchers often still rely on command-line scripting to glue individual tools together. RESULTS In this work, we address the problem of building a web-based system for workflows in structural bioinformatics. For the underlying molecular modelling engine, we opted for the BALL framework because of its extensive and well-tested functionality in the field of structural bioinformatics. The large number of molecular data structures and algorithms implemented in BALL allows for elegant and sophisticated development of new approaches in the field. We hence connected the versatile BALL library and its visualization and editing front end BALLView with the Galaxy workflow framework. The result, which we call ballaxy, enables the user to simply and intuitively create sophisticated pipelines for applications in structure-based computational biology, integrated into a standard tool for molecular modelling. AVAILABILITY AND IMPLEMENTATION ballaxy consists of three parts: some minor modifications to the Galaxy system, a collection of tools and an integration into the BALL framework and the BALLView application for molecular modelling. Modifications to Galaxy will be submitted to the Galaxy project, and the BALL and BALLView integrations will be integrated in the next major BALL release. After acceptance of the modifications into the Galaxy project, we will publish all ballaxy tools via the Galaxy toolshed. In the meantime, all three components are available from http://www.ball-project.org/ballaxy. Also, docker images for ballaxy are available at https://registry.hub.docker.com/u/anhi/ballaxy/dockerfile/. ballaxy is licensed under the terms of the GPL.
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Affiliation(s)
- Anna Katharina Hildebrandt
- Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, University of Tübingen, 72607 Tübingen and Chair for Software-Engineering and Bioinformatics, Institute for Informatics, Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany
| | - Daniel Stöckel
- Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, University of Tübingen, 72607 Tübingen and Chair for Software-Engineering and Bioinformatics, Institute for Informatics, Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany
| | - Nina M Fischer
- Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, University of Tübingen, 72607 Tübingen and Chair for Software-Engineering and Bioinformatics, Institute for Informatics, Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany
| | - Luis de la Garza
- Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, University of Tübingen, 72607 Tübingen and Chair for Software-Engineering and Bioinformatics, Institute for Informatics, Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany
| | - Jens Krüger
- Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, University of Tübingen, 72607 Tübingen and Chair for Software-Engineering and Bioinformatics, Institute for Informatics, Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany
| | - Stefan Nickels
- Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, University of Tübingen, 72607 Tübingen and Chair for Software-Engineering and Bioinformatics, Institute for Informatics, Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany
| | - Marc Röttig
- Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, University of Tübingen, 72607 Tübingen and Chair for Software-Engineering and Bioinformatics, Institute for Informatics, Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany
| | - Charlotta Schärfe
- Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, University of Tübingen, 72607 Tübingen and Chair for Software-Engineering and Bioinformatics, Institute for Informatics, Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany
| | - Marcel Schumann
- Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, University of Tübingen, 72607 Tübingen and Chair for Software-Engineering and Bioinformatics, Institute for Informatics, Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany
| | - Philipp Thiel
- Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, University of Tübingen, 72607 Tübingen and Chair for Software-Engineering and Bioinformatics, Institute for Informatics, Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany
| | - Hans-Peter Lenhof
- Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, University of Tübingen, 72607 Tübingen and Chair for Software-Engineering and Bioinformatics, Institute for Informatics, Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany
| | - Oliver Kohlbacher
- Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, University of Tübingen, 72607 Tübingen and Chair for Software-Engineering and Bioinformatics, Institute for Informatics, Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany
| | - Andreas Hildebrandt
- Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, University of Tübingen, 72607 Tübingen and Chair for Software-Engineering and Bioinformatics, Institute for Informatics, Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany
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