Following Lin and Maldacena, we find exact supergravity solutions dual to a class of vacua of the plane wave matrix model by solving an electrostatics problem. These are asymptotically near-horizon D0-brane solutions with a throat associated with NS5-brane degrees of freedom. We determine the precise limit required to decouple the asymptotic geometry and leave an infinite throat solution found earlier by Lin and Maldacena, dual to Little String Theory on S 5 . By matching parameters with the gauge theory, we find that this corresponds to a double scaling limit of the plane wave matrix model in which N → ∞ and the 't Hooft coupling λ scales as ln 4 (N ), which we speculate allows all terms in the genus expansion to contribute even at infinite N . Thus, the double-scaled matrix quantum mechanics gives a Lagrangian description of Little String Theory on S 5 , or equivalently a ten-dimensional string theory with linear dilaton background.1 Unfortunately, the solution contains Ramond-Ramond fields, so string theory is difficult. 2 A similar situation occurs in [6,7], though in the present case, more of the R-symmetry is preserved. See also [8] and [9] for discussions of the type IIB Little String Theory compactified on S 2 and S 3 respectively.
The minimal folding pathway or trajectory for a biopolymer can be defined as the transformation that minimizes the total distance traveled between a folded and an unfolded structure. This involves generalizing the usual Euclidean distance from points to one-dimensional objects such as a polymer. We apply this distance here to find minimal folding pathways for several candidate protein fragments, including the helix, the beta-hairpin, and a nonplanar structure where chain noncrossing is important. Comparing the distances traveled with root mean-squared distance and mean root-squared distance, we show that chain noncrossing can have large effects on the kinetic proximity of apparently similar conformations. Structures that are aligned to the beta-hairpin by minimizing mean root-squared distance, a quantity that closely approximates the true distance for long chains, show globally different orientation than structures aligned by minimizing root mean-squared distance.
The calculation of Euclidean distance between points is generalized to one-dimensional objects such as strings or polymers. Necessary and sufficient conditions for the minimal transformation between two polymer configurations are derived. Transformations consist of piecewise rotations and translations subject to Weierstrass-Erdmann corner conditions. Numerous examples are given for the special cases of one and two links. The transition to a large number of links is investigated, where the distance converges to the polymer length times the mean root square distance (MRSD) between polymer configurations, assuming curvature and non-crossing constraints can be neglected. Applications of this metric to protein folding are investigated. Potential applications are also discussed for structural alignment problems such as pharmacophore identification, and inverse kinematic problems in motor learning and control.which represents the diagonal of a hypercube, as expected. At this point we could fix the parameterization by choosing |v o (t)| = |r B − r A | /T (constant speed), for example. The extremal transformation (3) is also a minimum. In section 2.4 we will give the sufficient conditions for an extremum to be a (local) minimum, where we will return to this example.The above idea can be generalized to space curves, surfaces, or higher dimensional manifolds [1]. The distance is defined through the transformation between the objects that minimizes the cumulative amount of arc-length travelled by all parts of the manifold. Distance for polymers or stringsDescribing the transformation r(s,t) between two space curves r A (s) and r B (s) requires two scalar parameters: s the arc-length along the space curve, and t the "time" as in the above zero-dimensional
Computer simulations can provide critical information on the unfolded ensemble of proteins under physiological conditions, by explicitly characterizing the geometrical properties of the diverse conformations that are sampled in the unfolded state. A general computational analysis across many proteins has not been implemented however. Here, we develop a method for generating a diverse conformational ensemble, to characterize properties of the unfolded states of intrinsically disordered or intrinsically folded proteins. The method allows unfolded proteins to retain disulfide bonds. We examined physical properties of the unfolded ensembles of several proteins, including chemical shifts, clustering properties, and scaling exponents for the radius of gyration with polymer length. A problem relating simulated and experimental residual dipolar couplings is discussed. We apply our generated ensembles to the problem of folding kinetics, by examining whether the ensembles of some proteins are closer geometrically to their folded structures than others. We find that for a randomly selected dataset of 15 non-homologous 2- and 3-state proteins, quantities such as the average root mean squared deviation between the folded structure and unfolded ensemble correlate with folding rates as strongly as absolute contact order. We introduce a new order parameter that measures the distance travelled per residue, which naturally partitions into a smooth "laminar" and subsequent "turbulent" part of the trajectory. This latter conceptually simple measure with no fitting parameters predicts folding rates in 0 M denaturant with remarkable accuracy (r = -0.95, p = 1 × 10(-7)). The high correlation between folding times and sterically modulated, reconfigurational motion supports the rapid collapse of proteins prior to the transition state as a generic feature in the folding of both two-state and multi-state proteins. This method for generating unfolded ensembles provides a powerful approach to address various questions in protein evolution, misfolding and aggregation, transient structures, and molten globule and disordered protein phases.
We introduce a method for calculating the extent to which chain non-crossing is important in the most efficient, optimal trajectories or pathways for a protein to fold. This involves recording all unphysical crossing events of a ghost chain, and calculating the minimal uncrossing cost that would have been required to avoid such events. A depth-first tree search algorithm is applied to find minimal transformations to fold , , , and knotted proteins. In all cases, the extra uncrossing/non-crossing distance is a small fraction of the total distance travelled by a ghost chain. Different structural classes may be distinguished by the amount of extra uncrossing distance, and the effectiveness of such discrimination is compared with other order parameters. It was seen that non-crossing distance over chain length provided the best discrimination between structural and kinetic classes. The scaling of non-crossing distance with chain length implies an inevitable crossover to entanglement-dominated folding mechanisms for sufficiently long chains. We further quantify the minimal folding pathways by collecting the sequence of uncrossing moves, which generally involve leg, loop, and elbow-like uncrossing moves, and rendering the collection of these moves over the unfolded ensemble as a multiple-transformation “alignment”. The consensus minimal pathway is constructed and shown schematically for representative cases of an , , and knotted protein. An overlap parameter is defined between pathways; we find that proteins have minimal overlap indicating diverse folding pathways, knotted proteins are highly constrained to follow a dominant pathway, and proteins are somewhere in between. Thus we have shown how topological chain constraints can induce dominant pathway mechanisms in protein folding.
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