Treffer: MLLPA: A Machine Learning-assisted Python module to study phase-specific events in lipid membranes.
Title:
MLLPA: A Machine Learning-assisted Python module to study phase-specific events in lipid membranes.
Authors:
Walter V; Department of Chemistry, King's College London, London, UK., Ruscher C; Institut Charles Sadron - UPR 22, CNRS and University of Strasbourg, Strasbourg, France., Benzerara O; Institut Charles Sadron - UPR 22, CNRS and University of Strasbourg, Strasbourg, France., Thalmann F; Institut Charles Sadron - UPR 22, CNRS and University of Strasbourg, Strasbourg, France.
Source:
Journal of computational chemistry [J Comput Chem] 2021 May 15; Vol. 42 (13), pp. 930-943. Date of Electronic Publication: 2021 Mar 06.
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: United States NLM ID: 9878362 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-987X (Electronic) Linking ISSN: 01928651 NLM ISO Abbreviation: J Comput Chem Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York : Wiley,
MeSH Terms:
References:
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S. Jo, T. Kim, V. Iyer, W. Im, J. Comput. Chem. 2008, 29, 1859.
B. Brooks, C. L. Brooks III., A. D. Mackerell Jr.., L. Nilsson, R. Petrella, B. Roux, Y. Won, G. Archontis, C. Bartels, S. Boresch, A. Caflisch, L. Caves, Q. Cui, A. R. Dinner, M. Feig, S. Fischer, J. Gao, M. Hodoscek, W. Im, K. Kuczera, T. Lazaridis, J. Ma, V. Ovchinnikov, E. Paci, R. W. Pastor, C. B. Post, J. Z. Pu, M. Schaefer, B. Tidor, R. M. Venable, H. L. Woodcock, X. Wu, W. Yang, D. M. York, M. Karplus, J. Comput. Chem. 2009, 30, 1545.
J. Lee, X. Cheng, J. Swails, M. Yeom, P. Eastman, J. Lemkul, S. Wei, J. Buckner, J. Jeong, Y. Qi, S. Jo, V. S. Pande, D. A. Case, C. L. Brooks III., A. D. MacKerell Jr.., J. B. Klauda, W. Im, J. Chem. Theory Comput. 2015, 12, 405.
E. Wu, X. Cheng, S. Jo, H. Rui, K. Song, E. Dávila-Contreras, Y. Qi, J. Lee, V. Monje-Galvan, R. Venable, J. B. Klauda, W. Im, J. Comput. Chem. 2014, 35, 1997.
S. Jo, J. B. Lim, J. B. Klauda, W. Im, Biophys. J. 2009, 97, 50.
H. J. C. Berendsen, D. van der Spoel, R. van Drunen, Comput. Phys. Commun. 1995, 91, 43.
M. J. Abraham, T. Murtola, R. Schulz, S. Páll, J. C. Smith, B. Hessa, E. Lindahl, SoftwareX 2015, 1-2, 19.
R. B. Best, X. Zhu, J. Shim, P. E. M. Lopes, J. Mittal, M. Feig, A. D. MacKerell, J. Chem. Theory Comput. 2012, 8, 3257.
S. J. Marrink, H. J. Risselada, S. Yefimov, D. P. Tieleman, A. H. de Vries, J. Phys. Chem. B 2007, 111, 7812.
Y. Qi, H. I. Ingólfsson, X. Cheng, J. Lee, S. J. Marrink, W. Im, J. Chem. Theory Comput. 2015, 11, 4486.
P. Hsu, B. M. H. Bruininks, D. Jefferies, P. C. T. de Souza, J. Lee, D. S. Patel, S. J. Marrink, Y. Qi, S. Khalid, W. Im, J. Comput. Chem. 2017, 38, 2354.
A. Stukowski, Modell. Simul. Mater. Sci. Eng. 2009, 18, 015012.
E. J. Shimshick, H. M. McConnell, Biochemistry 1973, 12, 2351.
J. H. Ipsen, G. Karlström, O. G. Mouritsen, H. Wennerström, M. J. Zuckermann, Biochim. Biophys. Acta, Biomembr. 1987, 905, 162.
S. L. Veatch, O. Soubias, S. L. Keller, K. Gawrisch, PNAS 2007, 104, 17650.
D. Stelter, T. Keyes, Soft Matter 2019, 15, 8102.
M. D. Franova, I. Vattulainen, O. Ollila, Biochim. Biophys. Acta, Biomembr. 2014, 1838, 1406.
J. H. Crowe, F. A. Hoekstra, K. H. N. Nguyen, L. M. Crowe, Biochim. Biophys. Acta, Biomembr. 1996, 1280, 187.
G. Rossi, L. Monticelli, J. Phys.: Condens. Matter 2014, 26, 503101.
S. Marcelja, Biochim. Biophys. Acta, Biomembr. 1976, 455, 1.
T. Gil, J. H. Ipsen, O. G. Mouritsen, M. C. Sabra, M. M. Sperotto, M. J. Zuckermann, Biochim. Biophys. Acta, Biomembr. 1998, 1376, 245.
L. A. B. Ole, G. Mouritsen, Life-As a Matter of Fat, Springer-Verlag GmbH, Berlin, Germany 2015.
S. Mabrey, J. M. Sturtevant, PNAS 1976, 73, 3862.
T. Heimburg, Thermal Biophysics of Membranes, Tutorials in Biophysics, Wiley-VCH, Weinheim, Germany 2007, p. 339.
D. Marsh, Handbook of Lipid Bilayers, 2nd ed., CRC Press, Boca Raton, FL 2013.
J. F. Nagle, J. Chem. Phys. 1973, 58, 252.
D. Marsh, J. Membrane Biol. 1974, 18, 145.
S. T. Marcelja, Biochim. Biophys. Acta, Biomembr. 1974, 367, 165.
M. J. Zuckermann, O. G. Mouritsen, Eur. Biophys. J. 1987, 15, 77. https://doi.org/10.1007/BF00257501.
H. I. Petrache, K. Tu, J. F. Nagle, Biophys. J. 1999, 76, 2479.
H. I. Petrache, S. W. Dodd, M. F. Brown, Biophys. J. 2000, 79, 3172.
E. Cubuk, S. Schoenholz, J. Rieser, B. Malone, J. Rottler, D. Durian, E. Kaxiras, A. Liu, Phys. Rev. Lett. 2015, 114, 108001.
J. Carrasquilla, R. G. Melko, Nat. Phys. 2017, 13, 413.
T. C. Le, N. Tran, ACS Appl. Nano Mater. 2019, 2, 1637.
C. A. Lopez, V. V. Vesselinov, S. Gnanakaran, B. S. Alexandrov, J. Chem. Theory Comput. 2019, 15, 6343.
S. S. Iyer, A. Negi, A. Srivastava, J. Chem. Theory Comput. 2020, 16, 2736.
M. Aghaaminiha, S. A. Ghanadian, E. Ahmadi, A. M. Farnoud, Biomembranes 1862, 2020, 183350.
T. E. de Oliveira, F. Leonforte, L. Nicolas-Morgantini, A.-L. Fameau, B. Querleux, F. Thalmann, C. M. Marques, Phys. Rev. Res. 2020, 2, 013075.
V. Walter, C. Ruscher, O. Benzerara, C. M. Marques, F. Thalmann, Phys. Chem. Chem. Phys. 2020, 22, 19147.
D. Sánchez-Gutiérrez, M. Tozluoglu, J. D. Barry, A. Pascual, Y. Mao, L. M. Escudero, Embo J. 2016, 35, 77.
G. Voronoi, J. Reine Angew. Math. 1908, 133, 97.
G. Voronoi, J. Reine Angew. Math. 1908, 134, 198.
W. J. Allen, J. A. Lemkul, D. R. Bevan, J. Comput. Chem. 2008, 30, 1952.
S. Buchoux, Bioinformatics 2017, 33, 133.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, J. Mach. Learn. Res. 2011, 12, 2825.
C. H. Rycroft, Chaos Interdiscip. J. Nonlinear Sci. 2009, 19, 041111.
W. Smith, Tess, https://github.com/wackywendell/tess. (accessed 3 March 2021).
N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, O. Beckstein, J. Comput. Chem. 2011, 32, 2319.
R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler, J. Domanski, D. L. Dotson, S. Buchoux, I. M. Kenney, Oliver Beckstein, presented at Proc. 15th Python in Science Conference, Austin, Texas USA, 2016, pp. 98-105.
GitHub repo, github.com/vivien-walter/mllpa. (accessed 3 March 2021).
Zenodo repo, https://doi.org/10.5281/zenodo.4300706. (accessed 3 March 2021).
MDAnalysis - Trajectory Readers, https://docs.mdanalysis.org/stable/documentation_pages/topology/init.html. (accessed 3 March 2021).
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, T. E. Oliphant, Nature 2020, 585, 357.
S. Jo, T. Kim, V. Iyer, W. Im, J. Comput. Chem. 2008, 29, 1859.
B. Brooks, C. L. Brooks III., A. D. Mackerell Jr.., L. Nilsson, R. Petrella, B. Roux, Y. Won, G. Archontis, C. Bartels, S. Boresch, A. Caflisch, L. Caves, Q. Cui, A. R. Dinner, M. Feig, S. Fischer, J. Gao, M. Hodoscek, W. Im, K. Kuczera, T. Lazaridis, J. Ma, V. Ovchinnikov, E. Paci, R. W. Pastor, C. B. Post, J. Z. Pu, M. Schaefer, B. Tidor, R. M. Venable, H. L. Woodcock, X. Wu, W. Yang, D. M. York, M. Karplus, J. Comput. Chem. 2009, 30, 1545.
J. Lee, X. Cheng, J. Swails, M. Yeom, P. Eastman, J. Lemkul, S. Wei, J. Buckner, J. Jeong, Y. Qi, S. Jo, V. S. Pande, D. A. Case, C. L. Brooks III., A. D. MacKerell Jr.., J. B. Klauda, W. Im, J. Chem. Theory Comput. 2015, 12, 405.
E. Wu, X. Cheng, S. Jo, H. Rui, K. Song, E. Dávila-Contreras, Y. Qi, J. Lee, V. Monje-Galvan, R. Venable, J. B. Klauda, W. Im, J. Comput. Chem. 2014, 35, 1997.
S. Jo, J. B. Lim, J. B. Klauda, W. Im, Biophys. J. 2009, 97, 50.
H. J. C. Berendsen, D. van der Spoel, R. van Drunen, Comput. Phys. Commun. 1995, 91, 43.
M. J. Abraham, T. Murtola, R. Schulz, S. Páll, J. C. Smith, B. Hessa, E. Lindahl, SoftwareX 2015, 1-2, 19.
R. B. Best, X. Zhu, J. Shim, P. E. M. Lopes, J. Mittal, M. Feig, A. D. MacKerell, J. Chem. Theory Comput. 2012, 8, 3257.
S. J. Marrink, H. J. Risselada, S. Yefimov, D. P. Tieleman, A. H. de Vries, J. Phys. Chem. B 2007, 111, 7812.
Y. Qi, H. I. Ingólfsson, X. Cheng, J. Lee, S. J. Marrink, W. Im, J. Chem. Theory Comput. 2015, 11, 4486.
P. Hsu, B. M. H. Bruininks, D. Jefferies, P. C. T. de Souza, J. Lee, D. S. Patel, S. J. Marrink, Y. Qi, S. Khalid, W. Im, J. Comput. Chem. 2017, 38, 2354.
A. Stukowski, Modell. Simul. Mater. Sci. Eng. 2009, 18, 015012.
E. J. Shimshick, H. M. McConnell, Biochemistry 1973, 12, 2351.
J. H. Ipsen, G. Karlström, O. G. Mouritsen, H. Wennerström, M. J. Zuckermann, Biochim. Biophys. Acta, Biomembr. 1987, 905, 162.
S. L. Veatch, O. Soubias, S. L. Keller, K. Gawrisch, PNAS 2007, 104, 17650.
D. Stelter, T. Keyes, Soft Matter 2019, 15, 8102.
M. D. Franova, I. Vattulainen, O. Ollila, Biochim. Biophys. Acta, Biomembr. 2014, 1838, 1406.
J. H. Crowe, F. A. Hoekstra, K. H. N. Nguyen, L. M. Crowe, Biochim. Biophys. Acta, Biomembr. 1996, 1280, 187.
G. Rossi, L. Monticelli, J. Phys.: Condens. Matter 2014, 26, 503101.
S. Marcelja, Biochim. Biophys. Acta, Biomembr. 1976, 455, 1.
T. Gil, J. H. Ipsen, O. G. Mouritsen, M. C. Sabra, M. M. Sperotto, M. J. Zuckermann, Biochim. Biophys. Acta, Biomembr. 1998, 1376, 245.
Contributed Indexing:
Keywords: lipid membrane analysis; machine learning; molecular dynamics; phase transition; tessellation
Substance Nomenclature:
0 (Membrane Lipids)
Entry Date(s):
Date Created: 20210306 Date Completed: 20210927 Latest Revision: 20210927
Update Code:
20250114
DOI:
10.1002/jcc.26508
PMID:
33675541
Database:
MEDLINE
Weitere Informationen
Machine Learning-assisted Lipid Phase Analysis (MLLPA) is a new Python 3 module developed to analyze phase domains in a lipid membrane based on lipid molecular states. Reading standard simulation coordinate and trajectory files, the software first analyze the phase composition of the lipid membrane by using machine learning tools to label each individual molecules with respect to their state, and then decompose the simulation box using Voronoi tessellations to analyze the local environment of all the molecules of interest. MLLPA is versatile as it can read from multiple format (e.g., GROMACS, LAMMPS) and from either all-atom (e.g., CHARMM36) or coarse-grain models (e.g., Martini). It can also analyze multiple geometries of membranes (e.g., bilayers, vesicles). Finally, the software allows for training with more than two phases, allowing for multiple phase coexistence analysis.
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