Understanding function of proteins in membrane by atomistic and multiscale simulations
- Vittorio Limongelli (University of Lugano, Switzerland)
- Mickael Lelimousin (Université Grenoble Alpes and CNRS, France)
- Mark Sansom (University of Oxford, United Kingdom)
To understand the way a protein functions it is important to consider its cellular environment. About 25 % of genes encode membrane proteins, and furthermore membrane proteins are targets for ~50 % of marketed drugs. Signalling mechanisms of membrane receptors involve subtle conformational changes of these proteins . Therefore it is of paramount importance from a biological and pharmaceutical point of view to elucidate the dynamics of these macromolecules in their native lipid environment. The use of molecular dynamics (MD) simulations represents the first natural choice to investigate such functional dynamics [2,3], providing an atomistic description of the interactions at work. Furthermore, the growing number of experimental structures released over the last fifteen years has represented a further stimulus for computational investigations . Thus, taking also advantage of modern hardware we may enhance our understanding of membrane protein functions and the role of specific lipid/protein interactions . For instance, mechanisms of gating, permeation and selectivity could be deciphered using MD simulations [6, 7]. Nevertheless, many functional mechanisms, such as ligand/protein binding/unbinding and large protein motion, remain inaccessible through standard calculations because of the limiting timescale of MD simulations. Furthermore, increasing the size and complexity of the systems is necessary to obtain models which more closely resemble the in vivo cell membrane environment. To overcome these limitations one needs to step up the computational strategy. In this view enhanced sampling methods like metadynamics , have proven to be successful to study long timescale biological phenomena [9,10], whilst coarse-grained modeling has enlarged the scales accessible to membrane protein studies . Complementary multiscale approaches have been determinant to optimize the choice of methods used to treat variety of biological and chemical problems . However the rearrangements involved in the function of complex membrane proteins, e.g. transporters, still require the development of advanced methodologies [13,14]. Allosteric mechanisms involved in the activity of signaling receptors also impose extension of the timescales reached by simulations . In addition there are increasing evidences that long-range and cooperative effects at membranes, e.g. curvature, nano-domains and clustering, play significant roles in the modulation of membrane proteins activity [16-20]. Thus, today there is a tremendous need to accurately consider the complexity in size and composition of cell membranes in computational models and to reach with simulations the real-life timescale. This workshop will draw together both the theoretical and experimental communities that study membrane proteins in order to address the challenges of developing a quantitative and predictive understanding of the relationship between membrane protein structure and function.
The main objectives of the workshop are:
1. To illustrate the state of the art in experimental and computational methods to describe and predict protein functional motions in membrane
2. To identify the most relevant open questions from both the experimental and computational points of view
3. To identify the most important areas in which computer simulation can complement experiments
4. To identify future challenges to deal with protein-protein and protein-membrane interaction (free energy calculations, accurate description of the interactions through selection of collective variables or coarse grained representation, atomistic simulations versus multiscale approaches)
5. To foster sharing of information and stronger collaboration among experimental and computational experts
Specific topics that will be covered are:
- Free energy calculations and measurements
- Kinetic rate constant estimation and measurements
- Computational methods based on unbiased MD
- Computational methods based on biased MD
- Innovative experimental techniques applied to membrane-protein systems
1. Venkatakrishnan, A. J.; Deupi, X.; Lebon, G.; Tate, C. G.; Schertler, G. F.; Babu, M. M. Nature 2013, 494, (7436), 185-94.
2. Karplus, M.; McCammon, J. A. Nat Struct Biol 2002, 9, (9), 646-52.
3. Karplus, M.; Kuriyan, J. Proc Natl Acad Sci U S A 2005, 102, (19), 6679-85.
4. White, S. H. Nature 2009, 459, (7245), 344-6.
5. Stansfeld, P. J.; Sansom, M. S. Structure 2011, 19, (11), 1562-72.
6. Nury, H.; Poitevin, F.; Van Renterghem, C.; Changeux, J. P.; Corringer, P. J.; Delarue, M.; Baaden, M. Proc Natl Acad Sci U S A 2010, 107, (14), 6275-80.
7. Hub, J. S.; de Groot, B. L. Proc Natl Acad Sci U S A 2008, 105, (4), 1198-203.
8. Laio, A.; Parrinello, M. Proc Natl Acad Sci USA 2002, 99, (20), 1256212566.
9. Limongelli, V.; Bonomi, M.; Marinelli, L.; Gervasio, F. L.; Cavalli, A.; Novellino, E.; Parrinello, M. Proc Natl Acad Sci U S A 2010, 107, (12), 5411-6.
10. Limongelli, V.; Bonomi, M.; Parrinello, M. Proc Natl Acad Sci USA 2013, 110, (16), 6358-6363.
11. Marrink, S. J.; Tieleman, D. P. Chem Soc Rev 2013, 42, (16), 6801-22.
12. Sherwood, P.; Brooks, B. R.; Sansom, M. S. Curr Opin Struct Biol 2008, 18, (5), 630-40.
13. Marinelli, F.; Kuhlmann, S. I.; Grell, E.; Kunte, H. J.; Ziegler, C.; Faraldo-Gomez, J. D. Proc Natl Acad Sci U S A 2011, 108, (49), E1285-92.
14. Moradi, M.; Tajkhorshid, E. Proc Natl Acad Sci U S A 2013, 110, (47), 18916-21.
15. Arkhipov, A.; Shan, Y.; Das, R.; Endres, N. F.; Eastwood, M. P.; Wemmer, D. E.; Kuriyan, J.; Shaw, D. E. Cell 2013, 152, (3), 557-69.
16. Hatzakis, N. S.; Bhatia, V. K.; Larsen, J.; Madsen, K. L.; Bolinger, P. Y.; Kunding, A. H.; Castillo, J.; Gether, U.; Hedegard, P.; Stamou, D. Nat Chem Biol 2009, 5, (11), 835-41.
17. Schafer, L. V.; de Jong, D. H.; Holt, A.; Rzepiela, A. J.; de Vries, A. H.; Poolman, B.; Killian, J. A.; Marrink, S. J. Proc Natl Acad Sci U S A 2011, 108, (4), 1343-8.
18. Kaiser, H. J.; Orlowski, A.; Rog, T.; Nyholm, T. K.; Chai, W.; Feizi, T.; Lingwood, D.; Vattulainen, I.; Simons, K. Proc Natl Acad Sci U S A 2011, 108, (40), 16628-33.
19. Neri, M.; Vanni, S.; Tavernelli, I.; Rothlisberger, U. Biochemistry 2010, 49, (23), 4827-32.
20. Periole, X.; Knepp, A. M.; Sakmar, T. P.; Marrink, S. J.; Huber, T. J Am Chem Soc 2012, 134, (26), 10959-65.