Proteus is developed by the Biocomputing team at Ecole Polytechnique and their collaborators
November 2020: bug fix in the Adaptive tutorial: an environment variable was not properly set, with disastrous consequences. Please grab the new version from Tutorials section.
October 2020: Very happy to welcome Ivan Reveguk as a new PhD student! Ivan comes from the Universit of Saint Petersburg and is working on his French....
Congrats to Vaitea Opuu for brilliantly defending his PhD thesis in October!.
In June 2020, we reported the first successful whole protein redesign with a physics-based energy function: Opuu, Sun, Hou, Panel, Fuentes, Simonson (2020) Scientific Reports, https://doi.org/10.1038/s41598-020-67972-w Congrats to everyone!
In January 2020, we reported the first enzyme redesign where the catalytic efficiency was directly selected for: Opuu, Nigro, Gaillard, Schmit, Mechulam, Simonson, PLoS Computational Biology, https://doi.org/10.1371/journal.pcbi.1007600. Congrats to Vaitea Opuu and our experimental collaborators!
Proteus 3.0 was released on May 21st, 2019
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 or government scientists under a Creative Commons license. To download Proteus 3.0 click the "Download" button.
Complete design of an octa peptide. Input files; ready to run (13 Kb)
Complete design of an octa peptide. Input files + main output files (80 Mb)
Complete design of a PDZ domain. Input files; ready to run (105 Kb)
Complete design of a PDZ domain. Input files + main output files (2 Gb)
Designing a protein for ligand binding with adaptive MC. Input files; ready to run (2.8 Mb)
Designing a protein for ligand binding with adaptive MC. Input files + main output files (50 Mb)
Acid/base constants of BPTI with constant-pH MC. Input files; ready to run (22 Kb)
Acid/base constants of BPTI with constant-pH MC. Input files + main output files (4.4 Mb)
Optimizing PDZ unfolded energies (to match natural amino acid frequencies). Input files (6.5 Gb)
Proteus is available free of charge to academic users or government scientists under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, including code, manual and tutorials. Other users should send email to inquire about availability.
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
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.
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.
Email: thomas.simonson<AT>polytechnique.fr
Laboratoire de Biochimie, Ecole Polytechnique, 91128 Palaiseau, France