
The formation of fibrillar aggregates seems to be a common characteristic of polypeptide chains, although the observation of these aggregates may depend on appropriate experimental conditions. Partially folded intermediates seem to have an important role in the generation of protein aggregates, and a mechanism for this fibril formation considers that these intermediates also correspond to metastable states with respect to the fibrillar ones. Here, using a coarse-grained (CG) off-lattice model, we carry out a comparative analysis of the thermodynamic aspects characterizing the folding transition with respect to the propensity for aggregation of four different systems: two isoforms of the amyloid β-protein, the Src SH3 domain, and the human prion proteins (hPrP). Microcanonical analysis of the data obtained from replica exchange method is conducted to evaluate the free-energy barrier and latent heat in these models. The simulations of the amyloid β isoforms and Src SH3 domain indicated that the folding process described by this CG model is related to a negative specific heat, a phenomenon that can only be verified in the microcanonical ensemble in first-order phase transitions. The CG simulation of the hPrP heteropolymer yielded a continuous folding transition. The absence of a free-energy barrier and latent heat favors the presence of partially unfolded conformations, and in this context, this thermodynamic aspect could explain the reason why the hPrP heteropolymer is more aggregation-prone than the other heteropolymers considered in this study. We introduced the hydrophobic radius of gyration as an order parameter and found that it can be used to obtain reliable information about the hydrophobic packing and the transition temperatures in the folding process.
Models, Molecular, Amyloid, Molecular Sequence Data, Proteins, FOS: Physical sciences, Biomolecules (q-bio.BM), Models, Theoretical, Computational Physics (physics.comp-ph), Quantitative Biology - Biomolecules, Biological Physics (physics.bio-ph), FOS: Biological sciences, Physics - Biological Physics, Amino Acid Sequence, Physics - Computational Physics, Algorithms
Models, Molecular, Amyloid, Molecular Sequence Data, Proteins, FOS: Physical sciences, Biomolecules (q-bio.BM), Models, Theoretical, Computational Physics (physics.comp-ph), Quantitative Biology - Biomolecules, Biological Physics (physics.bio-ph), FOS: Biological sciences, Physics - Biological Physics, Amino Acid Sequence, Physics - Computational Physics, Algorithms
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