1. Modeling and Understanding Complex Biomolecular Systems and Processes. Application in Nanosciences, Biotechnology and Biomedicine Bogdan Lesyng ICM and Faculty of Physiscs, Warsaw University (http://www.icm.edu.pl/~lesyng/) and European Centre of Excellence for Multiscale Biomolecular Modelling, Bioinformatics and Applications (http://www.icm.edu.pl/mamba) Trento, 16-17 December, 2004

2. Dynamics, classical and/or quantum one in the real molecular environment Sequences at the protein & nucleic acids levels 3D & electronic structure Function Metabolic pathways & signalling Sub-cellular structures & processes Cell(s), structure(s) & functions 1 RPDFCLEPPY 10 11 TGPCKARIIR 20 21 YFYNAKAGLC 30 31 QTFVYGGCRA 40 41 KRNNFKSAED 50 51 CMRTCGGA 58

3. In our organisms we have ~ 10 3 protein kinases and phosphatases which phosphorylate/ dephosphorylate other proteins activating or disactivating them. These are controllers of most of methabolic pathways.

4. A Protein Kinase Molecule with ATP (catalytic domain)

5. Designing inhibitors Information, conference on ” Inhibitors of Protein Kinases”, and workshops on ”Molecular Recognition Processes” June 26-30, 2005 Warsaw http://www.icm.edu.pl/ ipk2005/

6. Limitations of conventional bioinformatics approaches in structure predicion Homology based structure prediction methods are effective for those families of proteins which crystallize. They fail, for example, for membrane proteins. Methods developed for proteins fail for nucleic acids. Folding of nucleic acids, like folding of single-stranded RNA, could be even more important than protein folding (to learn what is the role of noncoding regions)

7. Multi-scale modeling. Classes of models Microscopic models Mesoscopic models

8. Recently I participated in the Robert Welch Foundation Conference on „Chemistry of Self-Organizing Hybrid Materials”, Houston, Oct.25- 26, 2004. Selected topics below: Biologically Active Self-Assembling Peptide Nanotubes Conditional Control of BiopolymerSelf Assembly and Activity Electroactive Functional Polymers and Nanocomposites Nanotechnology : Carbon Nanotubes, Nanomachines and Molecular Computers Using Self-Assembly to Create Electronic Materials Objects and processes listed above require, amongst others, the knowledge of effective iteraction potentials – refer to the following port of my talk.

9. Microscopic generators of the potential energy function AVB – (quantum) AVB/GROMOS - (quantum-classical) SCC-DFTB - (quantum) SCC-DFTB/GROMOS - (quantum-classical) SCC-DFTB/CHARMM - (quantum -classical).... Dynamics MD (classical) QD (quantum) QCMD (quantum-classical).... Mesoscopic potential energy functions Poisson-Boltzmann (PB) Generalized Born (GB)....

10. SCC-DFTB Method (Self Consistent Charge Density Functional Based Tight Binding Method, SCC DFTB, Frauenheim et al. Phys Stat. Sol. 217, 41, 2000) basic DFT concepts: 1-electron orbitals total electron density 1-electron Hamiltonian (Kohn-Sham equation)

12. CM3/SCC-DFTB charges J.A. Kalinowski, B.Lesyng, J.D. Thompson, Ch.J. Cramer, D.G. Truhlar, Class IV Charge Model for the Self-Consistent Charge Density-Functional Tight- Binding Method, J. Phys. Chem. A, 108, 2545-2549 (2004) J.Li, T.Zhu, C.Cramer, D.Truhlar, J. Phys. Chem. A, 102, 1821(1998) New generation of charges capable reproducing electrostatic properties, in particular molecular dipole moments.

15. Looking for very fast algorithms to compute the mean-field (mesoscopic) electrostatic energy. Born models: M.Born, Z.Phys., 1,45(1920) R.Constanciel and R.Contreas, Theor.Chim.Acta, 65,111(1984) W.C.Still, A.Tempczyk,R.C.Hawlely,T.Hendrikson, J.Am.Chem.Soc.,112,6127(1990) D.Bashford, D.Case, Annu.Rev.Phys.Chem., 51,129(2000) If we know, so called Born-radii of atoms, we can very quickly compute the electrostatic energy. A Born radius is a geometrical property !

17. M.Feig, W.Im, C.L.Brooks, J.Chem.Phys.,120,903-911(2004) (I) (II) (III) (IV) Coulomb Field appr. Kirkwood Model

18. Ratio of the GB solvation enery to the Kirkwood solvation energy

19. There are non-solved problems (like hydrophobic potentials), but it looks like in the near future we will have a new generation of effective (mean-field, mesoscopic) molecular interaction potentials, which can be applied to structure prediction problems (regardless of the type of biopolymers !) or ligand – biomolecule interactions.

21. Acknowledgements: PhD students: Jarek Kalinowski Piotr Kmieć Magda Gruziel Michał Wojciechowski Collaboration: Prof. T. FrauenheimSCC-DFTB, University of Paderborn, Germany Dr. M. Elstner Prof. D. TruhlarCM3-charges, Minnesota Solvation Data Base Dr. J. ThompsonUniversity of Minnesota, USA Dr. C. Cramer Prof. J.A.McCammonTitration of proteins University of California at San Diego, USA Studies supported in part by ”European CoE for Multiscale Biomolecular Modelling, Bioinformatics and Applications”, ICM, Warsaw University.