@article {48394, title = {Mimicking the folding pathway to improve homology-free protein structure prediction.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {106}, year = {2009}, month = {2009 Mar 10}, pages = {3734-9}, abstract = {

Since the demonstration that the sequence of a protein encodes its structure, the prediction of structure from sequence remains an outstanding problem that impacts numerous scientific disciplines, including many genome projects. By iteratively fixing secondary structure assignments of residues during Monte Carlo simulations of folding, our coarse-grained model without information concerning homology or explicit side chains can outperform current homology-based secondary structure prediction methods for many proteins. The computationally rapid algorithm using only single (phi,psi) dihedral angle moves also generates tertiary structures of accuracy comparable with existing all-atom methods for many small proteins, particularly those with low homology. Hence, given appropriate search strategies and scoring functions, reduced representations can be used for accurately predicting secondary structure and providing 3D structures, thereby increasing the size of proteins approachable by homology-free methods and the accuracy of template methods that depend on a high-quality input secondary structure.

}, keywords = {Algorithms, Amino Acid Sequence, Molecular Mimicry, Molecular Sequence Data, Protein Folding, Protein Structure, Secondary, Protein Structure, Tertiary, Proteins, Structural Homology, Protein}, issn = {1091-6490}, doi = {10.1073/pnas.0811363106}, author = {DeBartolo, Joe and Colubri, Andr{\'e}s and Jha, Abhishek K and Fitzgerald, James E and Freed, Karl F and Sosnick, Tobin R} } @article {48395, title = {Benchmarking implicit solvent folding simulations of the amyloid beta(10-35) fragment.}, journal = {The journal of physical chemistry. B}, volume = {112}, year = {2008}, month = {2008 May 15}, pages = {6175-86}, abstract = {

A pathogenetic feature of Alzhemier disease is the aggregation of monomeric beta-amyloid proteins (Abeta) to form oligomers. Usually these oligomers of long peptides aggregate on time scales of microseconds or longer, making computational studies using atomistic molecular dynamics models prohibitively expensive and making it essential to develop computational models that are cheaper and at the same time faithful to physical features of the process. We benchmark the ability of our implicit solvent model to describe equilibrium and dynamic properties of monomeric Abeta(10-35) using all-atom Langevin dynamics (LD) simulations, since Alphabeta(10-35) is the only fragment whose monomeric properties have been measured. The accuracy of the implicit solvent model is tested by comparing its predictions with experiment and with those from a new explicit water MD simulation, (performed using CHARMM and the TIP3P water model) which is approximately 200 times slower than the implicit water simulations. The dependence on force field is investigated by running multiple trajectories for Alphabeta(10-35) using the CHARMM, OPLS-aal, and GS-AMBER94 force fields, whereas the convergence to equilibrium is tested for each force field by beginning separate trajectories from the native NMR structure, a completely stretched structure, and from unfolded initial structures. The NMR order parameter, S2, is computed for each trajectory and is compared with experimental data to assess the best choice for treating aggregates of Alphabeta. The computed order parameters vary significantly with force field. Explicit and implicit solvent simulations using the CHARMM force fields display excellent agreement with each other and once again support the accuracy of the implicit solvent model. Alphabeta(10-35) exhibits great flexibility, consistent with experiment data for the monomer in solution, while maintaining a general strand-loop-strand motif with a solvent-exposed hydrophobic patch that is believed to be important for aggregation. Finally, equilibration of the peptide structure requires an implicit solvent LD simulation as long as 30 ns.

}, keywords = {Amyloid beta-Peptides, Computer Simulation, Diffusion, Magnetic Resonance Spectroscopy, Models, Molecular, Peptide Fragments, Protein Folding, Protein Structure, Tertiary, Solvents, Surface Properties, Water}, issn = {1520-6106}, doi = {10.1021/jp077099h}, author = {Kent, Andrew and Jha, Abhishek K and Fitzgerald, James E and Freed, Karl F} } @article {48397, title = {Polypeptide motions are dominated by peptide group oscillations resulting from dihedral angle correlations between nearest neighbors.}, journal = {Biochemistry}, volume = {46}, year = {2007}, month = {2007 Jan 23}, pages = {669-82}, abstract = {

To identify basic local backbone motions in unfolded chains, simulations are performed for a variety of peptide systems using three popular force fields and for implicit and explicit solvent models. A dominant "crankshaft-like" motion is found that involves only a localized oscillation of the plane of the peptide group. This motion results in a strong anticorrelated motion of the phi angle of the ith residue (phi(i)) and the psi angle of the residue i - 1 (psi(i-1)) on the 0.1 ps time scale. Only a slight correlation is found between the motions of the two backbone dihedral angles of the same residue. Aside from the special cases of glycine and proline, no correlations are found between backbone dihedral angles that are separated by more than one torsion angle. These short time, correlated motions are found both in equilibrium fluctuations and during the transit process between Ramachandran basins, e.g., from the beta to the alpha region. A residue{\textquoteright}s complete transit from one Ramachandran basin to another, however, occurs in a manner independent of its neighbors{\textquoteright} conformational transitions. These properties appear to be intrinsic because they are robust across different force fields, solvent models, nonbonded interaction routines, and most amino acids.

}, keywords = {Alanine, Computer Simulation, Glycine, Models, Molecular, Molecular Conformation, Motion, Peptides, Proline, Protein Folding}, issn = {0006-2960}, doi = {10.1021/bi061575x}, author = {Fitzgerald, James E and Jha, Abhishek K and Sosnick, Tobin R and Freed, Karl F} } @article {48396, title = {Reduced C(beta) statistical potentials can outperform all-atom potentials in decoy identification.}, journal = {Protein science : a publication of the Protein Society}, volume = {16}, year = {2007}, month = {2007 Oct}, pages = {2123-39}, abstract = {

We developed a series of statistical potentials to recognize the native protein from decoys, particularly when using only a reduced representation in which each side chain is treated as a single C(beta) atom. Beginning with a highly successful all-atom statistical potential, the Discrete Optimized Protein Energy function (DOPE), we considered the implications of including additional information in the all-atom statistical potential and subsequently reducing to the C(beta) representation. One of the potentials includes interaction energies conditional on backbone geometries. A second potential separates sequence local from sequence nonlocal interactions and introduces a novel reference state for the sequence local interactions. The resultant potentials perform better than the original DOPE statistical potential in decoy identification. Moreover, even upon passing to a reduced C(beta) representation, these statistical potentials outscore the original (all-atom) DOPE potential in identifying native states for sets of decoys. Interestingly, the backbone-dependent statistical potential is shown to retain nearly all of the information content of the all-atom representation in the C(beta) representation. In addition, these new statistical potentials are combined with existing potentials to model hydrogen bonding, torsion energies, and solvation energies to produce even better performing potentials. The ability of the C(beta) statistical potentials to accurately represent protein interactions bodes well for computational efficiency in protein folding calculations using reduced backbone representations, while the extensions to DOPE illustrate general principles for improving knowledge-based potentials.

}, keywords = {Carbon, Computational Biology, Models, Statistical, Protein Folding, Protein Structure, Secondary, Proteins}, issn = {0961-8368}, doi = {10.1110/ps.072939707}, author = {Fitzgerald, James E and Jha, Abhishek K and Colubri, Andr{\'e}s and Sosnick, Tobin R and Freed, Karl F} }