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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

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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


Sequences Processes.

at the protein &

nucleic acids levels

3D & electronic

structure

Function

Dynamics, classical and/or quantum one in the real molecular environment

1 RPDFCLEPPY 10 11 TGPCKARIIR 20 21 YFYNAKAGLC 30 31 QTFVYGGCRA 40 41 KRNNFKSAED 50 51 CMRTCGGA 58

Cell(s), structure(s) & functions

Metabolic pathways & signalling

Sub-cellular

structures & processes


In our organisms Processes.

we have ~ 103

protein kinases

and phosphatases

which

phosphorylate/

dephosphorylate

other proteins

activating or

disactivating

them.

These are

controllers

of most of

methabolic

pathways.



Designing inhibitors Processes.

Information, conference on

”Inhibitors of Protein Kinases”,

and workshops on

”Molecular Recognition Processes”

June 26-30, 2005 Warsaw

http://www.icm.edu.pl/

ipk2005/


Limitations of conventional bioinformatics approaches in structure predicion
Limitations of conventional bioinformatics approaches Processes.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)


Multi scale modeling classes of models
Multi-scale modeling. Classes of models Processes.

Microscopic models

Mesoscopic models


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.


Microscopic generators of the potential energy function Conference on „Chemistry of Self-Organizing Hybrid Materials”, Houston, Oct.25- 26, 2004. Selected topics below:

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)

  • ....


SCC-DFTB Method Conference on „Chemistry of Self-Organizing Hybrid Materials”, Houston, Oct.25- 26, 2004. Selected topics below:

(Self Consistent Charge Density Functional Based Tight Binding Method,

SCC DFTB, Frauenheim et al. Phys Stat. Sol. 217, 41, 2000)

basic DFT concepts:

total electron

density

1-electron orbitals

1-electron

Hamiltonian

(Kohn-Sham equation)


New generation of charges capable reproducing electrostatic properties, in particular molecular dipole moments.

J.Li, T.Zhu, C.Cramer, D.Truhlar, J. Phys. Chem. A, 102, 1821(1998)

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)


  • 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 !


Coulomb Field appr. (mesoscopic) electrostatic energy.

(I)

Kirkwood Model

(II)

(III)

M.Feig, W.Im, C.L.Brooks, J.Chem.Phys.,120,903-911(2004)

(IV)



There are non-solved problems energy(like hydrophobic potentials), but it looks likein 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.


Acknowledgements: energy

PhD students:

Jarek Kalinowski

Piotr Kmieć

Magda Gruziel

Michał Wojciechowski

Collaboration:

Prof. T. Frauenheim SCC-DFTB, University of Paderborn, Germany

Dr. M. Elstner

Prof. D. Truhlar CM3-charges, Minnesota Solvation Data Base

Dr. J. Thompson University of Minnesota, USA

Dr. C. Cramer

Prof. J.A.McCammon Titration 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.


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