Several sequence and structural factors have been proposed to contribute toward greater stability of thermophilic proteins. Here we present a statistical examination of structural and sequence parameters in representatives of 18 non-redundant families of thermophilic and mesophilic proteins. Our aim was to look for systematic differences among thermophilic and mesophilic proteins across the families. We observe that both thermophilic and mesophilic proteins have similar hydrophobicities, compactness, oligomeric states, polar and non-polar contribution to surface areas, main-chain and side-chain hydrogen bonds. Insertions/deletions and proline substitutions do not show consistent trends between the thermophilic and mesophilic members of the families. On the other hand, salt bridges and side chain-side chain hydrogen bonds increase in the majority of the thermophilic proteins. Additionally, comparisons of the sequences of the thermophile-mesophile homologous protein pairs indicate that Arg and Tyr are significantly more frequent, while Cys and Ser are less frequent in thermophilic proteins. Thermophiles both have a larger fraction of their residues in the alpha-helical conformation, and they avoid Pro in their alpha-helices to a greater extent than the mesophiles. These results indicate that thermostable proteins adapt dual strategies to withstand high temperatures. Our intention has been to explore factors contributing to the stability of proteins from thermophiles with respect to the melting temperatures (T(m)), the best descriptor of thermal stability. Unfortunately, T(m) values are available only for a few proteins in our high resolution dataset. Currently, this limits our ability to examine correlations in a meaningful way.
Folding funnels have been the focus of considerable attention during the last few years. These have mostly been discussed in the general context of the theory of protein folding. Here we extend the utility of the concept of folding funnels, relating them to biological mechanisms and function. In particular, here we describe the shape of the funnels in light of protein synthesis and folding; flexibility, conformational diversity, and binding mechanisms; and the associated binding funnels, illustrating the multiple routes and the range of complexed conformers. Specifically, the walls of the folding funnels, their crevices, and bumps are related to the complexity of protein folding, and hence to sequential vs. nonsequential folding. Whereas the former is more frequently observed in eukaryotic proteins, where the rate of protein synthesis is slower, the latter is more frequent in prokaryotes, with faster translation rates. The bottoms of the funnels reflect the extent of the flexibility of the proteins. Rugged floors imply a range of conformational isomers, which may be close on the energy landscape. Rather than undergoing an induced fit binding mechanism, the conformational ensembles around the rugged bottoms argue that the conformers, which are most complementary to the ligand, will bind to it with the equilibrium shifting in their favor. Furthermore, depending on the extent of the ruggedness, or of the smoothness with only a few minima, we may infer nonspecific, broad range vs. specific binding. In particular, folding and binding are similar processes, with similar underlying principles. Hence, the shape of the folding funnel of the monomer enables making reasonable guesses regarding the shape of the corresponding binding funnel. Proteins having a broad range of binding, such as proteolytic enzymes or relatively nonspecific endonucleases, may be expected to have not only rugged floors in their folding funnels, but their binding funnels will also behave similarly, with a range of complexed conformations. Hence, knowledge of the shape of the folding funnels is biologically very useful. The converse also holds: If kinetic and thermodynamic data are available, hints regarding the role of the protein and its binding selectivity may be obtained. Thus, the utility of the concept of the funnel carries over to the origin of the protein and to its function.
The long-held views on lock-and-key versus induced fit in binding arose from the notion that a protein exists in a single, most stable conformation, dictated by its sequence. However, in solution proteins exist in a range of conformations, which may be described by statistical mechanical laws and their populations follow statistical distributions. Upon binding, the equilibrium will shift in favor of the bound conformation from the ensemble of conformations around the bottom of the folding funnel. Hence here we extend the implications and the usefulness of the folding funnel concept to explain fundamental binding mechanisms.
Allostery is largely associated with conformational and functional transitions in individual proteins. This concept can be extended to consider the impact of conformational perturbations on cellular function and disease states. Here, we clarify the concept of allostery and how it controls physiological activities. We focus on the challenging questions of how allostery can both cause disease and contribute to development of new therapeutics. We aim to increase the awareness of the linkage between disease symptoms on the cellular level and specific aberrant allosteric actions on the molecular level and to emphasize the potential of allosteric drugs in innovative therapies.
Whereas previously we have successfully utilized the folding funnels concept to rationalize binding mechanisms~Ma B, , Protein Eng 12:713-720! and to describe binding~Tsai CJ, Kumar S, Ma B, Nussinov R, 1999, Protein Sci 8:1181-1190!, here we further extend the concept of folding funnels, illustrating its utility in explaining enzyme pathways, multimolecular associations, and allostery. This extension is based on the recognition that funnels are not stationary; rather, they are dynamic, depending on the physical or binding conditions~Tsai CJ, Ma B, Nussinov R, 1999, Proc Natl Acad Sci USA 96:9970-9972!. Different binding states change the surrounding environment of proteins. The changed environment is in turn expressed in shifted energy landscapes, with different shapes and distributions of populations of conformers. Hence, the function of a protein and its properties are not only decided by the static folded three-dimensional structure; they are determined by the distribution of its conformational substates, and in particular, by the redistributions of the populations under different environments. That is, protein function derives from its dynamic energy landscape, caused by changes in its surroundings.
Allostery is essential for controlled catalysis, signal transmission, receptor trafficking, turning genes on and off, and apoptosis. It governs the organism's response to environmental and metabolic cues, dictating transient partner interactions in the cellular network. Textbooks taught us that allostery is a change of shape at one site on the protein surface brought about by ligand binding to another. For several years, it has been broadly accepted that the change of shape is not induced; rather, it is observed simply because a larger protein population presents it. Current data indicate that while side chains can reorient and rewire, allostery may not even involve a change of (backbone) shape. Assuming that the enthalpy change does not reverse the free-energy change due to the change in entropy, entropy is mainly responsible for binding.
The question of how allostery works was posed almost 50 years ago. Since then it has been the focus of much effort. This is for two reasons: first, the intellectual curiosity of basic science and the desire to understand fundamental phenomena, and second, its vast practical importance. Allostery is at play in all processes in the living cell, and increasingly in drug discovery. Many models have been successfully formulated, and are able to describe allostery even in the absence of a detailed structural mechanism. However, conceptual schemes designed to qualitatively explain allosteric mechanisms usually lack a quantitative mathematical model, and are unable to link its thermodynamic and structural foundations. This hampers insight into oncogenic mutations in cancer progression and biased agonists' actions. Here, we describe how allostery works from three different standpoints: thermodynamics, free energy landscape of population shift, and structure; all with exactly the same allosteric descriptors. This results in a unified view which not only clarifies the elusive allosteric mechanism but also provides structural grasp of agonist-mediated signaling pathways, and guides allosteric drug discovery. Of note, the unified view reasons that allosteric coupling (or communication) does not determine the allosteric efficacy; however, a communication channel is what makes potential binding sites allosteric.
While allostery draws increasing attention, not much is known about allosteric mechanisms. Here we argue that in all proteins, allosteric signals transmit through multiple, pre-existing pathways; which pathways dominate depend on protein topologies, specific binding events, covalent modifications and cellular (environmental) conditions. Further, perturbation events at any site on the protein surface (or in the interior) will not create new pathways but only shift the pre-existing ensemble of pathways. Drugs binding at different sites or mutational events in disease shift the ensemble toward the same conformations; however, the relative populations of the different states will change. Consequently the observed functional, conformational, and dynamic effects will be different. This is the origin of allosteric functional modulation in dynamic proteins: allostery does not necessarily need to invoke conformational rearrangements to control protein activity and pre-existing pathways are always defaulted to during allostery regardless of the stimulant and perturbation site in the protein.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.