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Proteus is developed by the Biocomputing team at Ecole Polytechnique and their collaborators

News about Proteus


Proteus 3.0 was released on May 21st, 2019

Proteus 3.0: introduction


Proteus is a general purpose program for protein design. It can be used to redesign entire proteins or functional sites such as ligand-binding pockets. It has several specific or unique features: it uses a physics-based energy function and a stochastic method to search sequence and conformation space. It provides a method, based on adaptive Wang-Landau Monte Carlo, to directly select mutations that increase ligand binding free energy or ligand specificity. It can perform constant-pH Monte Carlo, which yields acid/base constants or pKa’s. It has been used to successfully redesign two aminoacyl-tRNA synthetase enzymes and one entire PDZ domain.

Proteus is available free of charge to academic users under a Creative Commons license. To download Proteus 3.0 click the "Download" button.

Proteus 3.0: manual


The Proteus manual can be freely downloaded as a 4.2 Mbyte pdf file.

Download

Proteus is available free of charge to academic users under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, including code, manual and tutorials. Non academic users should send email to inquire about availability.

Creative Commons License

The data provided here will be stored by the Proteus development team at Ecole Polytechnique (see Contact link) and used to notify Proteus users of future releases, bug fixes, or other related information. It will not be made public or redistributed in any way. The data will be deleted upon request, after the user informs us that the downloaded software has been deleted from all institution computers. By proceeding, you agree to the terms above. For more information, contact the Ecole Polytechnique data protection officer (dpd<AT>polytechnique.fr) or CNIL (Commission Nationale Informatique et Libertés).

To download Proteus 3.0, please fill in the form below and click the Download button (academic users) or send message button (non academic users

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Proteus 3.0: credits


Proteus is developed by the Biocomputing team in the Biology department at Ecole Polytechnique, Paris and their collaborators.
The authors of the Proteus software are:
David Mignon, Karen Druart, Thomas Gaillard, Anne Lopes, Vaitea Opuu, Savvas Polydorides, Marcel Schmidt am Busch, Francesco Villa and Thomas Simonson.
Proteus is described in the following articles, which include theoretical and methodological developments:

  • Thomas Simonson, Thomas Gaillard, David Mignon, Marcel Schmidt am Busch, Anne Lopes, Najette Amara, Savvas Polydorides, Audrey Sedano, Karen Druart, and Georgios Archontis (2013) J. Comp. Chem., 34:2472–84; doi.org/10.1002/jcc.23418. Computational protein design: the Proteus software and selected applications.

  • David Mignon and Thomas Simonson (2016) J. Comp. Chem., 37:1781-93; doi.org/10.1002/jcc.24393. Comparing three stochastic search algorithms for computational protein design: Monte Carlo, Replica Exchange Monte Carlo, and a multistart, steepest-descent heuristic.

  • Francesco Villa, David Mignon, Savvas Polydorides and Thomas Simonson (2017) J. Comp. Chem., 38:2396–2410; doi.org/10.1002/jcc.24898. Comparing pairwise-additive and many-body Generalized Born models for acid/base calculations and protein design.

PROTEUS 3.0: main articles


Methodology, testing and applications are described in the following articles:


  • F. Villa & T. Simonson (2018) Journal of Chemical Theory and Computation, 14, 6714-21. doi.org/10.1021/acs.jctc.8b00970
        Protein pKas from adaptive landscape flattening instead of constant-pH simulations.

  • A. Charpentier, D. Mignon, S. Barbe, J. Cortes, T. Schiex, T. Simonson & D. Allouche (2018) Journal of Chemical Information and Modelling, 59, 127-36. doi.org/10.1021/acs.jcim.8b00510
        Variable Neighborhood Search with Cost Function Networks to solve large computational protein design problems.

  • F. Villa, N. Panel, X. Chen & T. Simonson (2018) Journal of Chemical Physics, 149, 072302. doi.org/10.1063/1.5022249
        Adaptive landscape flattening in amino acid sequence space for the computational design of protein:peptide binding. (Invited article)

  • T. Gaillard & T. Simonson (2017) Journal of Chemical Theory and Computation, 13, 4932-43. doi.org/10.1021/acs.jctc.7b00202
        Full protein sequence redesign with an MMGBSA energy function.

  • F. Villa, D. Mignon, S. Polydorides & T. Simonson (2017) Journal of Computational Chemistry, 38, 2396-2410. doi.org/10.1002/jcc.24898
        Comparing pairwise-additive and many-body Generalized Born models for acid/base calculations and protein design.

  • S. Polydorides, E. Michael, T. Simonson & G. Archontis (2017) Journal of Computational Chemistry, 38, 2509-19. doi.org/10.1002/jcc.24910
        Simple models for nonpolar solvation: parameterization and testing.

  • D. Mignon, N. Panel, X. Chen, E. Fuentes & T. Simonson (2017) Journal of Chemical Theory and Computation, 13, 2271-89. doi.org10.1021/acs.jctc.6b01255
        Computational design of the Tiam1 PDZ domain and its ligand binding.

  • K. Druart, J. Bigot, E. Audit & T. Simonson (2016) Journal of Chemical Theory and Computation, 12, 6035-48. doi.org/10.1021/acs.jctc.6b00421
        A hybrid Monte Carlo scheme for multibackbone protein design.

  • D. Mignon & T. Simonson (2016) Journal of Computational Chemistry, 37, 1781-93. doi.org/10.1002/jcc.24393
        Comparing three stochastic search algorithms for computational protein design: Monte Carlo, Replica Exchange Monte Carlo, and a multistart, steepest-descent heuristic.

  • S. Polydorides, E. Michael, D. Mignon, K. Druart, G. Archontis & T. Simonson (2016) In "Methods in Molecular Biology: Design and Creation of Protein Ligand Binding Proteins," editor: Barry Stoddard. Springer Verlag, New York.
        Proteus and the design of ligand binding sites.

  • T. Gaillard, N. Panel & T. Simonson (2016) Proteins, 84, 803-819. doi.org/10.1002/prot.25030
        Protein sidechain conformation predictions with an MMGBSA energy function.

  • T. Simonson, S. Ye-Lehmann, Z. Palmai, N. Amara, S. Wydau-Dematteis, E. Bigan, K. Druart, C. Moch & P. Plateau (2016) Proteins, 84, 240-253. doi.org/10.1002/prot.24972
        Redesigning the sterospecificity of tyrosyl-tRNA synthetase. (Cover article)

  • K. Druart, Z. Palmai, E. Omarjee & T. Simonson (2016) Journal of Computational Chemistry, 37, 404-15. doi.org/10.1002/jcc.24230
        Protein:ligand binding free energies: a stringent test for computational protein design.

  • T. Gaillard & T. Simonson (2014) Journal of Computational Chemistry, 35, 1371-1387. doi.org/10.1002/jcc.23637
        Pairwise Decomposition of an MMGBSA Energy Function for Computational Protein Design.

  • T. Simonson, T. Gaillard, D. Mignon, M. Schmidt am Busch, A. Lopes, N. Amara, S. Polydorides, A. Sedano, K. Druart & G. Archontis (2013) Journal of Computational Chemistry, 34, 2472-84.
        Computational protein design: the Proteus software and selected applications.

  • S. Polydorides & T. Simonson (2013) Journal of Computational Chemistry, 34, 2742–56.
        Monte Carlo simulations of proteins at constant pH with generalized Born solvent.

  • S. Polydorides, N. Amara, C. Aubard, P. Plateau, T. Simonson & G. Archontis (2011) Proteins, 79, 3448-3468.
        Computational protein design with a generalized Born solvent model: application to asparaginyl-tRNA synthetase.

  • A. Aleksandrov, S. Polydorides, G. Archontis & T. Simonson (2010) Journal of Physical Chemistry B,114, 10634-10648.
        Predicting the acid/base behavior of proteins: a constant-pH Monte Carlo approach with generalized Born solvent.

  • M. Schmidt am Busch, A. Sedano & T. Simonson (2010) Plos One, 5(5), e10410.
        Computational protein design: validation and possible relevance as a tool for homology searching and fold recognition.

  • A. Lopes, M. Schmidt am Busch & T. Simonson (2010) Journal of Computational Chemistry, 31, 1273-1286.
        Computational design of protein:ligand binding: modifying the specificity of asparaginyl-tRNA synthetase.

  • M. Schmidt am Busch, D. Mignon & T. Simonson (2009) Proteins, 77, 139–158.
        Computational protein design as a tool for fold recognition.

  • M. Schmidt am Busch, A. Lopes, N. Amara, C. Bathelt & T. Simonson (2008) BMC Bioinformatics, 9, 148-163. Testing the Coulomb/Accessible Surface Area solvent model for protein stability, ligand binding, and protein design.

  • M. Schmidt am Busch, A. Lopes, D. Mignon & T. Simonson (2008) Journal of Computational Chemistry, 29, 1092-1102.
        Computational protein design: software implementation, parameter optimization, and performance of a simple method.

  • A. Lopes, A. Alexandrov, C. Bathelt, G. Archontis & T. Simonson (2007) Proteins, 67, 853-867.
        Computational sidechain placement and protein mutagenesis with implicit solvent models.

Contact

  • Email: thomas.simonson<AT>polytechnique.fr

Laboratoire de Biochimie, Ecole Polytechnique, 91128 Palaiseau, France