Organisers

• Paolo Carloni (International School for Advanced Studies (SISSA), Italy)
• Michele Parrinello (Swiss Federal Institute of Technology Zurich (ETHZ), Lugano, Switzerland)
• Ursula Roethlisberger (Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland)

Supports

   CECAM

   Psi-k

Description

Cellular functions - like growth, (programmed) cell death, metabolism etc - ultimately depend of interactions between macromolecules encoded by DNA. Proteins and RNA directly control the cell and regulate its functions through the reactions they perform, by allosteric changes driven by endogeneous and exogeneous factors and by their mutual interactions.

All of these processes involve molecular recognition, i.e. the process by which two or more biological molecules interact to form a specific complex. Molecular recognition is dominated by short-range, often transient, interactions at the contact surface of the interacting molecules. Even conformational changes and assembly of very large macromolecular aggregates, which can be propagated through long distances (tens of Angstroms), are the sum of local interactions between small molecules (like messengers) or macromolecules with their cellular targets.

Ultimately, therefore, even the understanding of the integration of biological complexes into cellular pathways (the so called 'systems biology') requires mechanistic understanding of the physical basis of molecular recognition. A quantitative description of cellular pathways in molecular terms is still mostly missing, although it would strongly impact on pharmaceutical sciences, as drugs target (and mutations affect) pathways, rather than single biomolecules. Such information is also crucial in nanobiotechnology, e.g. to design artificial sensing devices, which in Nature involve entire cascades of events and not only a single protein.

Molecular simulation constitute a key field to contribute to this issue. It can predict structure, dynamics, energetics, reactivity and spectroscopic properties of the cellular components (i.e. large macromolecular aggregates) involved in these pathways.

Tremendous challenges have to be taken before this ambitious goal can be reached. First, the systems are very complex and so are the interactions involved. In addition, ligand-protein processes involve small changes of free energies (less than 1 eV for non-covalent protein-protein interactions), and they are often entropy-driven. Next, the environment is very complex: cell membranes are far from being a simple lipid bilayer whilst the cytoplasm is far from being a simple aqueous solution. Finally, most often experimental structural information is partially or totally lacking.

Website

More information available in the School website

Scientific Objectives

Approaches that are increasingly successful in taking these challenges include modeling large biomolecular aggregates (i), simulation of rare events (ii), prediction from first principles of spectroscopic and redox properties (iii), multi-scale modeling (iv), protein and nucleic acid structure prediction (v). A critical analysis of the force fields used in biomolecular simulation is also required (vi).

The organizers and speakers of this school have given contributions in all of these fields.

(i) By incorporating biological data, molecular docking programs are increasingly useful to predict the structure of complexes between proteins and their cellular targets. This approach represents a significant step towards modeling protein structure not as entities but increasingly closer to their biological environment. This is absolutely crucial for mechanistic studies and drug design of proteins whose function is intrinsically related to protein/protein interactions [1]. Moreover, the present docking methodologies are represented by an accurate treatment of solvation, ionic strength and pH, which in turn could pave the way towards a quantitative estimate of free energy of binding. Several of the authors (notably M. Klein, K. Schulten, M. Sansom and H. Grubmuller) are leading scientists in predicting structure and energetics of large protein aggregates using all-atom and coarse grained simulations, in which the molecular components (e.g. protein residues) are represented by a reduced number of particles.

(ii) The time scale problem is one of the key issues of modern computational biophysics. Most biological events occur on a time scale that cannot be covered by standard molecular dynamics simulations. One of the organizers have given a seminal contribution in simulating rare events by inventing the metadynamics approach [2]. By introducing a simulation history-based external biasing potential, the system is forced to explore regions of higher free energy than that of the equilibrium conformation. Rare events can thus be induced and the multidimensional free energy of complex systems can be reconstructed [3] [4].

(iii) More and more frequently, both classical and ab initiio molecular dynamics are used in combination [5] to investigate biological systems. In particular, such hybrid QM/MM methods [6] as well as large scale quantum calculations [7] can be used to predict redox properties of systems as large as a protein in solution. TDDFT [8] can be combined with Car-Parrinello QM/MM schemes to investigate photochemical processes in biology [9] as well as spectroscopic properties, including NMR [10] and vibrational spectroscopy [11]. Enzymatic activity can also be studied [12].

(iv) All-atoms MD simulations are a very powerful tool to predict structural, dynamical, and thermodynamical properties of biological molecules. Unfortunately, the current computational power constrains this analysis to time scales of submicrosecond, too short to follow important biological processes, such as ligand-protein recognition, protein-protein interactions, signaling, etc., which evolve on a much longer time scale. Coarse grained models attempt to bridge the gap between time scales of feasible simulations and those of biologically relevant motions. Unfortunately, however, the simplified models cannot describe accurately molecular recognition events, which is the key factor in any signaling cascade.
Therefore multiscale methods are emerging, in which the all-atom description is embedded in a simplified model [13], [14], [15]. These methods are expected to have a large impact on molecular systems biology.

(v) Structural bioinformatics is now routinely used to predict structural features of proteins. It is based on the fact that similarities of sequences are reflected in similarities of structures [16]. However, such predictions are challenged for nucleic acids, especially RNA, and molecular simulations might become a key player in his field in the near future. As discussed in the previous section, however, the predictive power of molecular dynamics is challenged by many factors, including the accuracy of force field (briefly discussed below) as well as insufficient sampling.

(vi) Force field deficiencies are much less discussed in the literature than sampling limitations. The force field is so simple that it cannot capture accurately all force contributions simultaneously. One can tune the force field to reproduce experimental data for one aspect of the simulated system, but this tends to increase errors elsewhere. Comparison with first-principles calculations can provide crucial insights [17].

Plan of lectures

The school will give the students a unique opportunity to get directly in touchwith computational physics applied to molecular and cellular biology and to convince them that they can play an important role in this field in the future.

List of speakers include:

Frank Alber (UCLA, Los Angeles, USA): Architecture determination of macromolecular assemblies.

Paolo Carloni (SISSA, Trieste, Italy): Computational molecular medicine.

Michele Cascella (University of Bern, Switzerland): Multi-scale modeling of biological systems.

Francesco Gervasio (ETH Zurich, Lugano, Switzerland): Electronic properties of DNA.

Helmut Grubmuller (MPI, Gottingen, Germany): Molecular dynamics of very large biological systems.

Leonardo Guidoni (university of l'Aquila, Italy)

Juerg Hutter (University of Zurich, Switzerland): Electronic structure codes for very large systems.

Mike Klein (University of Pennsylvania, Philadelphia, USA): Coarse grained modeling of biological systems.

Alessandro Laio (SISSA, Trieste, Italy): Free energy calculations of biomolecules.

Richard Lavery (Intitut de Biologie et Chimie des Protéines, Lyon, France): Mechanical properties of biopolymers.

Arthur M. Lesk (University of Pennsylvania, Philadelphia, USA): Methods for bioinformatics.

Alessandra Magistrato: (DEMOCRITOS and SISSA, Trieste, Italy): QM/MM molecular dynamics simulations of drug/target complexes.

Modesto Orozco (Institute for Research in Biomedicine, Barcelona, Spain)

Michele Parrinello (ETH Zurich, Lugano, Switzerland): Advances in molecular simulation for large biomolecular systems.

Matteo dal Peraro (EPFL, Lausanne, Switzerland): QM/MM simulations of metallo-enzyme systems.

Stefano Piana (Curtin University, Perth, Australia ): Enzimatic reaction pathways.

Ursula Rothlisberger (EPFL, Lausanne, Switzerland): Absorption spectra of biomolecules.

Carme Rovira (ICREA, Barcelona, Spain) Iron proteins investigated by ab-initio calculations.

Angel Rubio (ETSF, Donostia-San Sebastian, Spain): Electronic properties of biomolecules.

Simone Raugei (SISSA, Trieste, Italy): Computational spectroscopy for biomolecules.

Klaus J. Schulten (University of Illinois, Urbana, USA): Molecular dynamics beyond microsecond and million-atoms complexes.

Mark Samson (University of Oxford, Oxford, UK): Ion channels modeling.

Ivano Tavernelli (EPFL, Lausanne, Switzerland): Electron transfer in biology.

Douglas J. Tobias (University of California, Irvine, USA)

Gregory A. Voth (University of Utah, Salt Lake City, USA): Coarse-grained modeling of large biomolecules.

Rebecca Wade (EMBL, Heidelberg, Germany): Structural bioinformatics of large systems.

References

[1] Fenollar-Ferrer C., Carnevale V., Raugei S., Carloni P., HIV-1 integrase-DNA interactions investigated by molecular modelling. Comp. Math. Met. Med. 2008, 9(3-4): 231

[2] Laio A, Parrinello M., Escaping free-energy minima. Proc. Natl. Acad. Sci. USA 2002, 99(20):12562.

[3] Biarnés X, Ardèvol A, Planas A, Rovira C, Laio A, Parrinello M., The conformational free energy landscape of beta-D-glucopyranose. Implications for substrate preactivation in beta-glucoside hydrolases. J. Am. Chem. Soc. 2007, 129(35):10686.

[4] Fiorin G, Pastore A, Carloni P, Parrinello M., Using Metadynamics to Understand the Mechanism of Calmodulin/Target Recognition at Atomic Detail. Biophys. J. 2006, 91(8): 2768.

[5] Mantz Y.A., Gervasio F.L., Laino T, Parrinello M., Solvent effects on charge spatial extent in DNA and implications for transfer Phys Rev Lett. 2007, 99(5):058104.

[6] Cascella M, Magistrato A, Tavernelli I, Carloni P, Rothlisberger U. Free in PMC Role of protein frame and solvent for the redox properties of azurin from Pseudomonas aeruginosa. Proc. Natl. Acad. Sci. U S A. 2006, 103(52):19641

[7] Sulpizi M, Raugei S, VandeVondele J, Carloni, P. Sprik, M., Calculation of redox properties: Understanding short- and long-range effects in rubredoxin. J. Phys. Chem. B 2007, 111(15): 3969.

[8] Miguel A. L. Marques, and Eberhard K. U., Gross, Time-Dependent Density Functional Theory, Springer-VerlagmBerlin Heidelberg (2003).

[9] Sulpizi M., Roehrig U. F., Hutter J., and Roethlisberger U., Optical properties of molecules in solution via hybrid TDDFT/MM simulations. Int. J. Quantum Chem. 2004, 101: 671.

[10] Vidossich P., Piana S., Miani A. Carloni P., Deuterium isotope effects in A : T and A : U base pairs: A computational NMR study. Am. Chem. Soc. 2006, 128(22):7215.

[11] Miani A., Raugei S., Carloni P., Helfand M. S., Structure and Raman spectrum of clavulanic acid in aqueous solution. J. Phys. Chem. B 2007, 111(10):2621.

[12] Dal Peraro M., Ruggerone P., Raugei S., Gervasio F. L., Carloni P., Investigating biological systems using first principles Car-Parrinello molecular dynamics simulations, Curr. Opin. Struct. Biol. 2007, 17(2):149.

[13] Neri M., Anselmi C., Cascella M., Maritan A., Carloni P., Coarse-grained model of proteins incorporating atomistic detail of the active site. Phys. Rev. Lett. 2005, 95(21)218102

[14] Ayton G. S., Izvekov S., Noid W. G., Voth, G. A.,Multiscale simulation of membranes and membrane proteins: Connecting molecular interactions to mesoscopic behavior. Computational Modeling of Membrane bilayers, in Current Topics In Membranes 2008, 60:181

[15] Tozzini V., Rocchia W., McCammon J. A. Mapping all-atom models onto one-bead coarse-grained models: General properties and applications to a minimal polypeptide model. J. Chem. Th. Comp. 2006, 2(3)667.

[16] Zanuy D., Gunasekaran K., Lesk A. M., Nussinov R., Computational study of the fibril organization of polyglutamine repeats reveals a common motif identified in beta-helices. J. Mol. Biol. 2006, 358(1)330.

[17] Maurer P., Laio A., Hugosson H. W., Colombo M. C., Rothlisberger U., Automated parametrization of biomolecular force fields from quantum mechanics/molecular mechanics (QM/MM) simulations through force matching. J. Chem. Theory Comput. 2007, 3(2), 628.