Proteins are the nanoscale machinery of all known cellular life. Amazingly, these large biomolecules, which contain up to 100,000 atoms, fold into unique three-dimensional shapes, in which they function. These functions include all cellular chemistry (metabolism), energy conversion (photosynthesis) and transport (oxygen transport), signal processing in the brain (neurons), the immune response and many others, often with an efficiency unmatched by any man-made process. Protein malfunction is often related to diseases and thousands disease-related proteins have been identified to date, many with still unknown structure. To understand, control or even design proteins we need to study protein structure, which is experimentally much harder to obtain than the information about the chemical composition (sequence) of a specific protein.
C. B. Anfinsen got the 1972 Nobel prize in chemistry for the observation that proteins are in thermodynamic equilibrium with their environment. This means that the free energy of a protein, which depends on the coordinates, assumes its minium over all conformations in the biologically active conformation.
POEM (Protein Optimization with free Energy Methods) (key results) is strategy for in-silico reproducible and predictive protein folding and protein tertiary structure prediction on the basis of thermodynamic hypothesis. Since 1999 we have developed all-atom biophysical forcefields (PFF01, PFF02) and efficient stochastic optimization techniques for:
- protein folding,
- protein-structure prediction,
- protein-protein interactions,
- proteins in non-physiological environments.
This approach is presently implemented in POEM@HOME world-wide distributed computing project (please join !)
