Summary
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 was awarded 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) 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: This approach is implemented in POEM@HOME world-wide distributed computing project (please join !)
Free energy forcefields for de-novo prediction of structure of 27 small proteins to 3 A resolution
All-atom free-energy methods offer a promising alternative to kinetic molecular mechanics simulations of protein folding and association. Here we report an accurate, transferable all-atom biophysical force-field (PFF02) that stabilizes the native conformation of a wide range of proteins as the global optimum of the free-energy landscape. For 32 proteins of the ROSETTA decoy set and 6 proteins that we have previously folded with PFF01 we find near-native conformations with an average backbone RMSD of 2.14Å to the native conformation and an average z-score of -3.46 to the corresponding decoy set. We used non-equilibrium sampling techniques starting from completely extended conformations to exhaustively sample the energy surface of three non-homologous hairpin-peptides, a three-stranded beta sheet, the all-helical 40 amino-acid HIV accessory protein and a zinc-finger ββα motif and find near-native conformations for the minimal energy for each proteins. Using a massively parallel evolutionary we also obtain a near-native low-energy conformation for the 54 amino-acid engrailed homeodomain. Our forcefield thus stabilized near-native conformations for a total of 20 proteins of all structure classes with an average RMSD of only 3.06 Å to their respective experimental conformations. Summary of proteins folded with PFF01/PFF02
Herges, T. and W. Wenzel,
An all-atom force field for tertiary structure prediction of helical proteins.
Biophysical Journal, 2004. 87(5): p. 3100-3109.
Verma, A. and W. Wenzel,
A Free-Energy Approach for All-Atom Protein Simulation.
Biophysical Journal, 2009. 96(9): p. 3483-3494.
De novo folding of a zinc-finger protein
Zinc fingers are among the most abundant proteins in eukaryotic genomes and occur in many DNA binding domains and transcription factors. They function in DNA recognition, RNA packaging, transcriptional activation, protein folding and assembly and apoptosis. Many zinc fingers contain a Cys2His2 binding motif that coordinates the Zn-ion in a-framework.Much effort is also directed towards the engineering of novel zinc fingers.
S. M. Gopal and W. Wenzel.
De-novo folding of the DNA-binding ATF-2 zinc finger motif in an all-atom free energy forcefield.
Angew. Chemie. (Intl. Ed.), 118:7890, 2006.
Folding a sixty amino acid protein with evolutionary algorithms
We have investigated an evolutionary algorithm for de-novo all-atom folding of the bacterial ribosomal protein L20. We report results of two simulations which converge to near-native conformations of this sixty amino-acid, our-helix protein. We observe a steady increase of "native content'' in both simulated ensembles and a large number of near native conformations in their final populations. We argue that these structures represent a significant fraction of the low-energy metastable conformations, which characterize the folding funnel of this protein. These data validate our all-atom free-energy forcefield PFF01 for tertiary structure prediction of a previously inaccessible structural family of proteins. We also compare folding simulations of the evolutionary algorithm with the basin hopping technique for the trp-cage protein. We find that the evolutionary algorithm generates a dynamic memory in the simulated population with leads to faster overall convergence.
A. Schug, W. Wenzel
In silico folding of a four-helix protein
J. Am. Chem. Soc. 129, 16736 (2004)
An evolutionary strategy for all-atom folding of the sixty amino acid bacterial ribosomal protein L20.
Biophysical Journal, 90, 4273, 2006.
All atom de novo folding of a 40 amino acid protein in a single day
The search for efficient and predictive methods to describe the protein folding process at the all-atom level remains an important grand-computational challenge. The development of multi-teraflop architectures, such as the IBM BlueGene used in this study, has been motivated in part by the large computational requirements of such studies. Here we report the predictive all-atom folding of the forty-amino acid HIV accessory protein using an evolutionary stochastic optimization technique. We implemented the optimization method as a master-client model on an IBM BlueGene, where the algorithm scales near perfectly from 64 to 4096 processors in virtual processor mode. Starting from a completely extended conformation we optimize a population of sixty-four conformations of the protein in our all-atom free-energy model PFF01. Using 2048 processors the algorithm predictively folds the protein to a near-native conformation with an RMS deviation of 3.43 Å in less then 24 hours.
A. Verma, S. M. Gopal, J.S.Ooh, K.H. Lee, and W. Wenzel.
All atom de-novo protein folding with a scalable evolutionary algorithm.
J. Comput. Chem. 28, 2552-2558 (2007)
A. Quintilla, E. Starikov, and W. Wenzel.
De novo folding of two-helix potassium channel blockers.
J. Chem. Theory and Computation. 3,1183 (2007)
