Learning To Fold Proteins Using Energy Landscape Theory

Abstract

This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models that are optimized using lessons learned from the energy landscape theory of protein folding. We also present a review of the results of the AMH/AMC/AMW/AWSEM family of coarse-grained molecular dynamics protein folding models to illustrate the points covered in the first part of the article. Accurate coarse-grained structure prediction models can be used to investigate a wide range of conceptual and mechanistic issues outside of protein structure prediction; specifically, the paper concludes by reviewing how AWSEM has in recent years been able to elucidate questions related to the unusual kinetic behavior of artificially designed proteins, multidomain protein misfolding, and the initial stages of protein aggregation.

Exploring the Free Energy Landscape of Nucleosomes

“…Instead, we adopt a coarse grained modeling approach, which has already proven fruitful in investigating a wide range of biological systems. [28][29][30] To investigate protein-DNA interactions in the nucleosome, we combine the Associative memory, Water mediated, Structure and Energy Model (AWSEM) for protein 9 with an improved version of the three site per nucleotide model (3SPN.2C) for DNA. 10,11 Each amino acid in AWSEM is modeled with three atoms, C α , C β and O, and the transferable interactions among amino acids are parameterized following the energy landscape theory prescription to maximize the ratio of folding temperature over glass transition temperature for a set of training proteins.…”

Evolution, energy landscapes and the paradoxes of protein folding

“…Yet today, a variety of computer algorithms can indeed translate, for the simpler systems, one dimensional sequence data into three dimensional structure albeit at moderate resolution [5,6,7]. The easiest to use algorithms rely ultimately on recoding existing biological information into the form of an energy landscape for computer simulations [5] and thus ultimately these algorithms rely on having “seen it in operation” rather than deriving their results directly from quantum mechanics.…”

“…The easiest to use algorithms rely ultimately on recoding existing biological information into the form of an energy landscape for computer simulations [5] and thus ultimately these algorithms rely on having “seen it in operation” rather than deriving their results directly from quantum mechanics. Delbrück’s demand to pull folding out of quantum mechanics, has, however, almost been satisfied by using very powerful computers to simulate fully atomistic models, the forces in which are only lightly parametrized by protein structural data [7].…”

Repeat proteins challenge the concept of structural domains

“…For this, the tertiary energy of the AWSEM (associated memory, water mediated, structure and energy model) energy function [38] of every possible continuous fragment is computed and compared with the energy of decoys, i.e. every other fragment of the same length.…”