Removal of kinetic traps and enhanced protein folding by strategic substitution of amino acids in a model α‐helical hairpin peptide

Abstract

The presence of non-native kinetic traps in the free energy landscape of a protein may significantly lengthen the overall folding time so that the folding process becomes unreliable. We use a computational model alpha-helical hairpin peptide to calculate structural free energy landscapes and relate them to the kinetics of folding. We show how protein engineering through strategic changes in only a few amino acid residues along the primary sequence can greatly increase the speed and reliability of the folding process, as seen experimentally. These strategic substitutions also prevent the formation of long-lived misfolded configurations that can cause unwanted aggregations of peptides. These results support arguments that removal of kinetic traps, obligatory or nonobligatory, is crucial for fast folding.

Conformational Temperature-Dependent Behavior of a Histone H2AX: A Coarse-Grained Monte Carlo Approach Via Knowledge-Based Interaction Potentials

“…The knowledge-based interaction potential matrix is derived from an ensemble of a large number of protein structures in the protein data bank (PDB). A number of such interaction tables are frequently used to investigate a range of questions related to protein structure including protein folding which has been studied extensively with a variety of models and methods involving all-atom details to minimalist coarse-grained descriptions [22] – [28] , [39] – [44] . We resort here to the classic residue-residue contact interaction table [19] which is employed in studying scaffolding of short peptides [20] .…”

Free-energy landscapes and thermodynamic parameters of complex molecules from nonequilibrium simulation trajectories

“…Under these conditions, it is still possible to estimatē τ f from survival probability [37][38][39]. The survival probability is the fraction of simulations that are unsuccessful in folding and remain in the unfolded state [40]. The survival probability to remain unfolded after a simulation time t is denoted by p(t) = 1 − n(t)/N 0 , where n is the number of successfully folded simulation runs.…”

Globular structure of a human immunodeficiency virus-1 protease (1DIFA dimer) in an effective solvent medium by a Monte Carlo simulation

“…The last few decades have witnessed an enormous surge in interest [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] in modeling proteins. Much attention is focused on identifying the universal characteristics, e.g., folding pathways via analysis of the energy landscapes of protein chains as well as their specific characteristics that entail local structures to understand binding to pertinent targets.…”