Jorge Chahine

Bio: Jorge Chahine is an academic researcher from Sao Paulo State University. The author has contributed to research in topics: Protein folding & Energy landscape. The author has an hindex of 14, co-authored 33 publications receiving 775 citations. Previous affiliations of Jorge Chahine include Instituto Adolfo Lutz & University of California, San Diego.

The energy landscape theory of protein folding: insights into folding mechanisms and scenarios.

Abstract: Publisher Summary This chapter presents an overview of the theoretical aspects of the “new view” of protein folding based on the energy landscape theory and the funnel concept. The chapter discusses the lattice and off-lattice models that have played a central role in verifying the general ideas associated with minimal frustration and the protein folding funnel. The chapter describes how a theoretical framework can relate the kinetics of protein folding to thermodynamic quantifies as a function of appropriate reaction coordinates. Using this framework, one then establishes the connection with experiments and detailed all-atom simulations, moving toward a fully quantitative theory for protein folding. The chapter also discusses how the hydrophobic effect is coupled to folding. The chapter focuses the difficulties that have to be overcomed to have a good folding sequence that can be of two different natures: energetic or topologic. Energetic frustration can be reduced with the appropriate design for the protein sequence. Topologic frustration is more complicated since it is a consequence of the polymeric nature of the chain and the shape of the folding motif. The chapter reveals the importance of new generation of experiments that have been devised to probe the early folding events and to explore the details of the landscape of small fast-folding proteins will play in increasing the understanding of this field.

Configuration-dependent diffusion can shift the kinetic transition state and barrier height of protein folding

Abstract: We show that diffusion can play an important role in protein-folding kinetics. We explicitly calculate the diffusion coefficient of protein folding in a lattice model. We found that diffusion typically is configuration- or reaction coordinate-dependent. The diffusion coefficient is found to be decreasing with respect to the progression of folding toward the native state, which is caused by the collapse to a compact state constraining the configurational space for exploration. The configuration- or position-dependent diffusion coefficient has a significant contribution to the kinetics in addition to the thermodynamic free-energy barrier. It effectively changes (increases in this case) the kinetic barrier height as well as the position of the corresponding transition state and therefore modifies the folding kinetic rates as well as the kinetic routes. The resulting folding time, by considering both kinetic diffusion and the thermodynamic folding free-energy profile, thus is slower than the estimation from the thermodynamic free-energy barrier with constant diffusion but is consistent with the results from kinetic simulations. The configuration- or coordinate-dependent diffusion is especially important with respect to fast folding, when there is a small or no free-energy barrier and kinetics is controlled by diffusion. Including the configurational dependence will challenge the transition state theory of protein folding. The classical transition state theory will have to be modified to be consistent. The more detailed folding mechanistic studies involving phi value analysis based on the classical transition state theory also will have to be modified quantitatively.

Topography of Funneled Landscapes Determines the Thermodynamics and Kinetics of Protein Folding

Abstract: The energy landscape approach has played a fundamental role in advancing our understanding of protein folding. Here, we quantify protein folding energy landscapes by exploring the underlying density of states. We identify three quantities essential for characterizing landscape topography: the stabilizing energy gap between the native and nonnative ensembles δE, the energetic roughness ΔE, and the scale of landscape measured by the entropy S. We show that the dimensionless ratio between the gap, roughness, and entropy of the system Λ=δE/(ΔE√(2S)) accurately predicts the thermodynamics, as well as the kinetics of folding. Large Λ implies that the energy gap (or landscape slope towards the native state) is dominant, leading to more funneled landscapes. We investigate the role of topological and energetic roughness for proteins of different sizes and for proteins of the same size, but with different structural topologies. The landscape topography ratio Λ is shown to be monotonically correlated with the thermodynamic stability against trapping, as characterized by the ratio of folding temperature versus trapping temperature. Furthermore, Λ also monotonically correlates with the folding kinetic rates. These results provide the quantitative bridge between the landscape topography and experimental folding measurements.

Specific and Nonspecific Collapse in Protein Folding Funnels

Abstract: Experiments with fast folding proteins are beginning to address the relationship between collapse and folding. We investigate how different scenarios for folding can arise depending on whether the folding and collapse transitions are concurrent or whether a nonspecific collapse precedes folding. Many earlier studies have focused on the limit in which collapse is fast compared to the folding time; in this work we focus on the opposite limit where, at the folding temperature, collapse and folding occur simultaneously. Real proteins exist in both of these limits. The folding mechanism varies substantially in these two regimes. In the regime of concurrent folding and collapse, nonspecific collapse now occurs at a temperature below the folding temperature (but slightly above the glass transition temperature).

A surprising simplicity to protein folding

Abstract: The polypeptide chains that make up proteins have thousands of atoms and hence millions of possible inter-atomic interactions. It might be supposed that the resulting complexity would make prediction of protein structure and protein-folding mechanisms nearly impossible. But the fundamental physics underlying folding may be much simpler than this complexity would lead us to expect: folding rates and mechanisms appear to be largely determined by the topology of the native (folded) state, and new methods have shown great promise in predicting protein-folding mechanisms and the three-dimensional structures of proteins.