Topology-based potentials and the study of the competition between protein folding and aggregation

Steric confinement and enhanced local flexibility assist knotting in simple models of protein folding

The chaperonin complex GroEL-GroES is able to accelerate the folding process of knotted proteins considerably. However, the folding mechanism inside the chaperonin cage is elusive. Here we use a combination of lattice and off-lattice Monte Carlo simulations of simple Gō models to study the effect of physical confinement and local flexibility on the folding process of protein model systems embedding a trefoil knot in their native structure. This study predicts that steric confinement plays a specific role in the folding of knotted proteins by increasing the knotting probability for very high degrees of confinement. This effect is observed for protein MJ0366 even above the melting temperature for confinement sizes compatible with the size of the GroEL/GroES chaperonin cage. An enhanced local flexibility produces the same qualitative effects on the folding process. In particular, we observe that knotting probability increases up to 40% in the transition state of protein MJ0366 when flexibility is enhanced. This is underlined by a structural change in the transition state, which becomes devoid of helical content. No relation between the knotting mechanism and flexibility was found in the context of the off-lattice model adopted in this work.

How determinant is N-terminal to C-terminal coupling for protein folding?

The existence of native interactions between the protein termini is a major determinant of the free energy barrier in a two-state folding transition being therefore a critical modulator of protein folding cooperativity.

Prediction of the structure of a silk-like protein in oligomeric states using explicit and implicit solvent models

We perform Replica Exchange Molecular Dynamics (REMD) simulations on a silk-like protein design with amino-acid sequence [(Gly-Ala)3-Gly-Glu]5 to investigate the stability of a single protein, a dimer, a trimer and a tetramer made up of these proteins starting from β-roll and β-sheet structures in both explicit (TIP3P) and implicit (GBSA) solvent models. Our simulation results for the implicit solvent model agree with those for the explicit solvent model for simulation times up to the longest tested, being 30 ns per replica. From this we infer that the implicit solvent model that we use is reliable, allowing us to reach much longer time scales (up to 200 ns per replica). We find that the self-assembly of fibers of these proteins in solution must be a nucleated process, involving nuclei made up of at least three monomers. We also find that the conformation of the protein changes upon assembly, i.e., there is a transition from a disordered globular state to an ordered β-sheet structure in the self-assembled state of aggregates containing more than two monomers. This indicates that autosteric effects must be important in the polymerization of this protein, reminiscent of what is observed for β-amyloids. Our findings are consistent with recent experimental results on a protein with an amino acid sequence similar to that of the protein we study.

Accounting for protein-solvent contacts facilitates design of nonaggregating lattice proteins

The folding specificity of proteins can be simulated using simplified structural models and knowledge-based pair-potentials. However, when the same models are used to simulate systems that contain many proteins, large aggregates tend to form. In other words, these models cannot account for the fact that folded, globular proteins are soluble. Here we show that knowledge-based pair-potentials, which include explicitly calculated energy terms between the solvent and each amino acid, enable the simulation of proteins that are much less aggregation-prone in the folded state. Our analysis clarifies why including a solvent term improves the foldability. The aggregation for potentials without water is due to the unrealistically attractive interactions between polar residues, causing artificial clustering. When a water-based potential is used instead, polar residues prefer to interact with water; this leads to designed protein surfaces rich in polar residues and well-defined hydrophobic cores, as observed in real protein structures. We developed a simple knowledge-based method to calculate interactions between the solvent and amino acids. The method provides a starting point for modeling the folding and aggregation of soluble proteins. Analysis of our simple model suggests that inclusion of these solvent terms may also improve off-lattice potentials for protein simulation, design, and structure prediction.