alexa Energy landscapes for adsorption of a protein-like HP chain as a function of native-state stability.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Computer Science & Systems Biology

Author(s): Liu SM, Haynes CA

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Abstract Dynamic Monte Carlo (DMC) simulations of the adsorption of simple protein-like chains are used to more clearly define the molecular basis for the dependence of adsorption thermodynamics on the stability of the unique lowest-energy "native state" conformation of the chain. Arai and Norde were among the first to show that proteins of low native-state stability strongly denature upon adsorption to weakly attractive sorbent surfaces, while relatively modest changes in conformation are observed in stable proteins under identical adsorption conditions. When the protein has a low native-state stability, favorable adsorption entropies are typically observed in such systems, leading to the general belief that the chain gains conformational entropy during adsorption through a net reduction in intramolecular interactions specific to the native-state structure. Analysis of energy landscapes generated from our DMC simulation results show that a net loss in specific intramolecular interactions can lead to a positive delta(ads)S under certain adsorption conditions. However, the influence of chain conformation on delta(ads)S is found to correlate more directly with the manner in which the unique states of the system are distributed among the energy levels available to the adsorbed chain. Delta(ads)S is found to tend toward a maximum for adsorption processes described by thermally averaged energy landscapes in which the energy levels carrying the highest Boltzmann weights have a high degree of conformational degeneracy. This condition is met when the average interaction energy between the chain and the sorbent equals that between two hydrophobic segments of the chain. This article was published in J Colloid Interface Sci and referenced in Journal of Computer Science & Systems Biology

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