Protein Structure Prediction: The Next Generation

Abstract

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. We explore how second generation prediction energy functions can be developed by introducing information from an ensemble of previously simulated structures. This procedure relies on the assumption of a funnelled energy landscape keeping with the principle of minimal frustration. First generation simulated structures provide an improved input for associative memory energy functions in comparison to the experimental protein structures chosen on the basis of sequence alignment.

References in corpus (1)

• Variational theory for site resolved protein folding free energy surfaces

Cited by in corpus (4)

• Protein Structure Prediction Using Basin-Hopping
• The Energy Landscape, Folding Pathways and the Kinetics of a Knotted Protein
• A solvable model of the genesis of amino-acid sequences via coupled dynamics of folding and slow genetic variation
• Iterative derivation of effective potentials to sample the conformational space of proteins at atomistic scale