Driving Force For Folding Research Articles

Oscillatory Molecular Driving Force for Protein Folding at High Concentration: A Molecular Simulation

This paper presents a Langevin dynamics simulation that suggests a novel way to fold protein at high concentration, a fundamental issue in neurodegenerative diseases in vivo and the production of recombinant proteins in vitro. The simulation indicates that the folding of a coarse-grained beta-barrel protein at high concentration follows the "collapse-rearrangement" mechanism but it yields products of various forms, including single proteins in the native, misfolded, and uncollapsed forms and protein aggregates. Misfolded and uncollapased proteins are the "nucleus" of the aggregates that also encapsulate some correctly folded proteins (native proteins). An optimum hydrophobic interaction strength (epsilon*(p)) between the hydrophobic beads of the model protein, which results from a compromise between the kinetics of collapse and rearrangement, is identified for use in increasing the rate of folding over aggregating. Increased protein concentration hinders the structural transitions in both collapse and rearrangement and thus favors aggregation. A new method for protein folding at high concentration is proposed, which uses an oscillatory molecular driving force (epsilon*(p)) to promote the dissociation of aggregates in the low epsilon*(p) regime while promoting folding at a high epsilon*(p). The advantage of this method in enhancing protein folding while depressing aggregation is illustrated by a comparison with the methods based on direct dilution or applying a denaturant gradient.

A Procedure for Detection and Quantitation of Cavity Volumes in Proteins: APPLICATION TO MEASURE THE STRENGTH OF THE HYDROPHOBIC DRIVING FORCE IN PROTEIN FOLDING

Accurate identification of cavities is important in the study of protein structure, stability, design, and ligand binding. Identification and quantitation of cavities is a nontrivial problem because most cavities are connected to the protein exterior. We describe a computational procedure for quantitating cavity volumes and apply this to derive an estimate of the hydrophobic driving force in protein folding. A grid-based Monte Carlo procedure is used to position water molecules on the surface of a protein. A Voronoi procedure is used to identify and quantitate empty space within the solvated protein. Additional cavities not detected by other existing procedures can be identified. Most of these are close to surface concavities. Residue volumes for both the interior and the surface residues as well as cavity volumes are in good agreement with volumes calculated from fully hydrated protein structures obtained from molecular dynamic simulations. We show that the loss of stability because of cavity-creating mutations correlates better with cavity volumes determined by this procedure than with cavity volumes determined by other methods. Available structural and thermodynamic data for a number of cavity-containing mutants were analyzed to obtain estimates of 26.1 cal x mol(-1) x A(-3) and 18.5 cal x mol(-1) x A(-2) for the relative contributions of cavity formation and the hydrophobic effect to the observed stability changes. The present estimate for the hydrophobic driving force is at the lower end of estimates derived from model compound studies and considerably lower than previous estimates of approximately 50 cal x mol(-1) x A(-2) derived from protein mutational data. In the absence of structural rearrangement, on average, deletion of a single methylene group is expected to result in losses in stability of 0.41 and 0.70 kcal x mol(-1) resulting from decrease in hydrophobicity and packing, respectively.

Origin of the native driving force for protein folding.

We derive an expression with four adjustable parameters that reproduces well the 20x20 Miyazawa-Jernigan potential matrix extracted from known protein structures. The numerical values of the parameters can be approximately computed from the surface tension of water, water-screened dipole interactions between residues and water and among residues, and average exposures of residues in folded proteins.

Nature of Driving Force for Protein Folding: A Result From Analyzing the Statistical Potential

In a statistical approach to protein structure analysis, Miyazawa and Jernigan (MJ) derived a $20\times 20$ matrix of inter-residue contact energies between different types of amino acids. Using the method of eigenvalue decomposition, we find that the MJ matrix can be accurately reconstructed from its first two principal component vectors as $M_{ij}=C_0+C_1(q_i+q_j)+C_2 q_i q_j$, with constant $C$'s, and 20 $q$ values associated with the 20 amino acids. This regularity is due to hydrophobic interactions and a force of demixing, the latter obeying Hildebrand's solubility theory of simple liquids.