The protein folding transition state: Insights from kinetics and thermodynamics

Protein misfolding involving entanglements providesa structural explanation for the origin of stretched-exponential refolding kinetics

Stretched-exponential protein refolding kinetics, first observed decades ago, were attributed to a nonnative ensemble of structures with parallel, non-interconverting folding pathways. However, the structural origin of the large energy barriers preventing interconversion between these folding pathways is unknown. Here, we combine simulations with limited proteolysis (LiP) and cross-linking (XL) mass spectrometry (MS) to study the protein phosphoglycerate kinase (PGK). Simulations recapitulate its stretched-exponential folding kinetics and reveal that misfolded states involving changes of entanglement underlie this behavior: either formation of a nonnative, noncovalent lasso entanglement or failure to form a native entanglement. These misfolded states act as kinetic traps, requiring extensive unfolding to escape, which results in a distribution of free energy barriers and pathway partitioning. Using LiP-MS and XL-MS, we propose heterogeneous structural ensembles consistent with these data that represent the potential long-lived misfolded states PGK populates. This structural and energetic heterogeneity creates a hierarchy of refolding timescales, explaining stretched-exponential kinetics.

Computational methods in the study of self-entangled proteins: a critical appraisal

The existence of self-entangled proteins, the native structure of which features a complex topology, unveils puzzling, and thus fascinating, aspects of protein biology and evolution. The discovery that a polypeptide chain can encode the capability to self-entangle in an efficient and reproducible way during folding, has raised many questions, regarding the possible function of these knots, their conservation along evolution, and their role in the folding paradigm. Understanding the function and origin of these entanglements would lead to deep implications in protein science, and this has stimulated the scientific community to investigate self-entangled proteins for decades by now. In this endeavour, advanced experimental techniques are more and more supported by computational approaches, that can provide theoretical guidelines for the interpretation of experimental results, and for the effective design of new experiments. In this review we provide an introduction to the computational study of self-entangled proteins, focusing in particular on the methodological developments related to this research field. A comprehensive collection of techniques is gathered, ranging from knot theory algorithms, that allow detection and classification of protein topology, to Monte Carlo or molecular dynamics strategies, that constitute crucial instruments for investigating thermodynamics and kinetics of this class of proteins.

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.

Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins

Protein folding is a complex process that can lead to disease when it fails. Especially poorly understood are the very early stages of protein folding, which are likely defined by intrinsic local interactions between amino acids close to each other in the protein sequence. We here present EFoldMine, a method that predicts, from the primary amino acid sequence of a protein, which amino acids are likely involved in early folding events. The method is based on early folding data from hydrogen deuterium exchange (HDX) data from NMR pulsed labelling experiments, and uses backbone and sidechain dynamics as well as secondary structure propensities as features. The EFoldMine predictions give insights into the folding process, as illustrated by a qualitative comparison with independent experimental observations. Furthermore, on a quantitative proteome scale, the predicted early folding residues tend to become the residues that interact the most in the folded structure, and they are often residues that display evolutionary covariation. The connection of the EFoldMine predictions with both folding pathway data and the folded protein structure suggests that the initial statistical behavior of the protein chain with respect to local structure formation has a lasting effect on its subsequent states.

Influence of pH and sequence in peptide aggregation via molecular simulation

We employ a recently developed coarse-grained model for peptides and proteins where the effect of pH is automatically included. We explore the effect of pH in the aggregation process of the amyloidogenic peptide KTVIIE and two related sequences, using three different pH environments. Simulations using large systems (24 peptides chains per box) allow us to describe the formation of realistic peptide aggregates. We evaluate the thermodynamic and kinetic implications of changes in sequence and pH upon peptide aggregation, and we discuss how a minimalistic coarse-grained model can account for these details.

Knotted proteins: A tangled tale of Structural Biology

Knotted proteins have their native structures arranged in the form of an open knot. In the last ten years researchers have been making significant efforts to reveal their folding mechanism and understand which functional advantage(s) knots convey to their carriers. Molecular simulations have been playing a fundamental role in this endeavor, and early computational predictions about the knotting mechanism have just been confirmed in wet lab experiments. Here we review a collection of simulation results that allow outlining the current status of the field of knotted proteins, and discuss directions for future research.

Theoretical volume profiles as a tool for probing transition states: folding kinetics

The mechanism by which conformational changes, particularly folding and unfolding, occur in proteins and other biopolymers has been widely discussed in the literature. Molecular dynamics (MD) simulations of protein folding present a formidable challenge since these conformational changes occur on a time scale much longer than what can be afforded at the current level of computational technology. Transition state (TS) theory offers a more economic description of kinetic properties of a reaction system by relating them to the properties of the TS, or for flexible systems, the TS ensemble (TSE). The application of TS theory to protein folding is limited by ambiguity in the definition of the TSE for this process. We propose to identify the TSE for conformational changes in flexible systems by comparison of its experimentally determined volumetric property, known as the volume of activation, to the structure-specific volume profile of the process calculated using MD. We illustrate this approach by its successful application to unfolding of a model chain system.

Structural and energetic determinants of co-translational folding

We performed extensive lattice Monte Carlo simulations of ribosome-bound stalled nascent chains (RNCs) to explore the relative roles of native topology and non-native interactions in co-translational folding of small proteins. We found that the formation of a substantial part of the native structure generally occurs towards the end of protein synthesis. However, multi-domain structures, which are rich in local interactions, are able to develop gradually during chain elongation, while those with proximate chain termini require full protein synthesis to fold. A detailed assessment of the conformational ensembles populated by RNCs with different lengths reveals that the directionality of protein synthesis has a fine-tuning effect on the probability to populate low-energy conformations. In particular, if the participation of non-native interactions in folding energetics is mild, the formation of native-like conformations is majorly determined by the properties of the contact map around the tethering terminus. Likewise, a pair of RNCs differing by only 1-2 residues can populate structurally well-resolved low energy conformations with significantly different probabilities. An interesting structural feature of these low-energy conformations is that, irrespective of native structure, their non-native interactions are always long-ranged and marginally stabilizing. A comparison between the conformational spectra of RNCs and chain fragments folding freely in the bulk reveals drastic changes amongst the two set-ups depending on the native structure. Furthermore, they also show that the ribosome may enhance (up to 20%) the population of low energy conformations for chains folding to native structures dominated by local interactions. In contrast, a RNC folding to a non-local topology is forced to remain largely unstructured but can attain low energy conformations in bulk.

Why do protein folding rates correlate with metrics of native topology?

For almost 15 years, the experimental correlation between protein folding rates and the contact order parameter has been under scrutiny. Here, we use a simple simulation model combined with a native-centric interaction potential to investigate the physical roots of this empirical observation. We simulate a large set of circular permutants, thus eliminating dependencies of the folding rate on other protein properties (e.g. stability). We show that the rate-contact order correlation is a consequence of the fact that, in high contact order structures, the contact order of the transition state ensemble closely mirrors the contact order of the native state. This happens because, in these structures, the native topology is represented in the transition state through the formation of a network of tertiary interactions that are distinctively long-ranged.

Effect of local thermal fluctuations on folding kinetics: a study from the perspective of nonextensive statistical mechanics

The search through the proteins conformational space is thought as an early independent stage of the folding process, governed mainly by the hydrophobic effect. Because of the nanoscopic size of proteins, we assume that the effects of local thermal fluctuations work like folding assistants, managed by the nonextensive parameter q. Using a 27-mer heteropolymer on a cubic lattice, we obtained--by Monte Carlo simulations--kinetic and thermodynamic amounts (such as the characteristic folding time and the native stability) as a function of temperature T and q for a few distinct native targets. We found that for each native structure, at a specific system temperature T, there exists an optimum q* that minimizes the folding characteristic time τ(min); for T=1, it is found that q* lies in the interval 1.15±0.05, even for native structures presenting significantly different topological complexities. The distribution of τ(min) obtained for specific q>1 (nonextensive approach) and temperature T can be fully reproduced for q=1 (Boltzmann approach), but only at higher temperatures T'>T. However, assuming that the complete set of proteins of each organism is optimized to work in a narrow range of temperature, we conclude that--for the present problem--the two approaches, namely, (T,q>1) and (T>T',q=1), cannot be equivalent; it is not a simple matter of reparametrization. Finally, by associating the nonextensive parameter q with the instantaneous degree of compactness of the globule, q becomes a dynamic variable, self-adjusted along the simulation. The results obtained through the q-variable approach are utterly consistent with those obtained by using a target-tuned parameter q*. However, in the former approach, q is automatically adjusted by the chain conformational evolution, eliminating the need to seek for a specific optimized value of q for each case. Besides, using the q-variable approach, different target structures are promptly characterized by inherent distributions of q, which reflect the overall complexity of their corresponding native topologies and energy landscapes.