The energy landscape, folding pathways and the kinetics of a knotted protein.

The folding pathway and rate coefficients of the folding of a knotted protein are calculated for a potential energy function with minimal energetic frustration. A kinetic transition network is constructed using the discrete path sampling approach, and the resulting potential energy surface is visualized by constructing disconnectivity graphs. Owing to topological constraints, the low-lying portion of the landscape consists of three distinct regions, corresponding to the native knotted state and to configurations where either the N or C terminus is not yet folded into the knot.

Protein structure prediction using basin-hopping.

Associative memory Hamiltonian structure prediction potentials are not overly rugged, thereby suggesting their landscapes are like those of actual proteins. In the present contribution we show how basin-hopping global optimization can identify low-lying minima for the corresponding mildly frustrated energy landscapes. For small systems the basin-hopping algorithm succeeds in locating both lower minima and conformations closer to the experimental structure than does molecular dynamics with simulated annealing.

Induced fit, folding, and recognition of the NF-kappaB-nuclear localization signals by IkappaBalpha and IkappaBbeta.

Protein structure prediction codes based on the associative memory Hamiltonian were used to probe the binding modes between the nuclear localization signal (NLS) polypeptide of NF-kappaB and the inhibitors IkappaBalpha and IkappaBbeta. Experimentally, it is known that the NLS polypeptide is unstructured in the NF-kappaB complex with DNA but it forms an extended helical structure with the NLS (residues 301-304) between the two helices in the NF-kappaB/IkappaBalpha complex. The simulations included the NF-kappaB(p65) and (p50) NLS polypeptides and various mutants alone and in the presence of IkappaBalpha and IkappaBbeta.

Protein Structure Prediction: The Next Generation.

Over the last 10-15 years a general understanding of the chemical reaction of protein folding has emerged from statistical mechanics. The lessons learned from protein folding kinetics based on energy landscape ideas have benefited protein structure prediction, in particular the development of coarse grained models. We survey results from blind structure prediction.

Associative memory Hamiltonians for structure prediction without homology: alpha/beta proteins.

We describe a method for predicting the structure of alpha beta class proteins in the absence of information from homologous structures. The method is based on an associative memory model for short to intermediate range in sequence contacts and a contact potential for long range in sequence contacts. The coefficients in the energy function are chosen to maximize the ratio of the folding temperature to the glass transition temperature.