Chung-Jung Tsai

Allostery: Absence of a Change in Shape Does Not Imply that Allostery Is Not at Play

Allostery is essential for controlled catalysis, signal transmission, receptor trafficking, turning genes on and off, and apoptosis. It governs the organisms 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.

Allostery in disease and in drug discovery.

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.

The Origin of Allosteric Functional Modulation: Multiple Pre-existing Pathways

Although 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.

A new, structurally nonredundant, diverse data set of protein-protein interfaces and its implications

Here, we present a diverse, structurally nonredundant data set of two‐chain protein–protein interfaces derived from the PDB. Using a sequence order‐independent structural comparison algorithm and hierarchical clustering, 3799 interface clusters are obtained. These yield 103 clusters with at least five nonhomologous members. We divide the clusters into three types. In Type I clusters, the global structures of the chains from which the interfaces are derived are also similar. This cluster type is expected because, in general, related proteins associate in similar ways. In Type II, the interfaces are similar; however, remarkably, the overall structures and functions of the chains are different. The functional spectrum is broad, from enzymes/inhibitors to immunoglobulins and toxins. The fact that structurally different monomers associate in similar ways, suggests “good” binding architectures. This observation extends a paradigm in protein science: It has been well known that proteins with similar structures may have different functions. Here, we show that it extends to interfaces. In Type III clusters, only one side of the interface is similar across the cluster. This structurally nonredundant data set provides rich data for studies of protein–protein interactions and recognition, cellular networks and drug design. In particular, it may be useful in addressing the difficult question of what are the favorable ways for proteins to interact. (The data set is available at http://protein3d.ncifcrf.gov/∼keskino/ and http://home.ku.edu.tr/∼okeskin/INTERFACE/INTERFACES.html.)

Synonymous mutations and ribosome stalling can lead to altered folding pathways and distinct minima.

How can we understand a case in which a given amino acid sequence folds into structurally and functionally distinct molecules? Synonymous single-nucleotide polymorphisms in the MDR1 (multidrug resistance 1 or ABCB1) gene involving frequent-to-rare codon substitutions lead to identical protein sequences. Remarkably, these alternative sequences give a protein product with similar but different structures and functions. Here, we propose that long-enough ribosomal pause time scales may lead to alternate folding pathways and distinct minima on the folding free energy surface. While the conformational and functional differences between the native and alternate states may be minor, the MDR1 case illustrates that the barriers may nevertheless constitute sufficiently high hurdles in physiological time scales, leading to kinetically trapped states with altered structures and functions. Different folding pathways leading to conformationally similar trapped states may be due to swapping of (fairly symmetric) segments. Domain swapping is more likely in the no-pause case in which the chain elongates and folds simultaneously; on the other hand, sufficiently long pause times between such segments may be expected to lessen the chances of swapping events. Here, we review the literature in this light.

Structured disorder and conformational selection.

Traditionally, molecular disorder has been viewed as local or global instability. Molecules or regions displaying disorder have been considered inherently unstructured. The term has been routinely applied to cases for which no atomic coordinates can be derived from crystallized molecules. Yet, even when it appears that the molecules are disordered, prevailing conformations exist, with population times higher than those of all alternate conformations. Disordered molecules are the outcome of rugged energy landscapes away from the native state around the bottom of the funnel. Ruggedness has a biological function, creating a distribution of structured conformers that bind via conformational selection, driving association and multimolecular complex formation, whether chain‐linked in folding or unlinked in binding. We classify disordered molecules into two types. The first type possesses a hydrophobic core. Here, even if the native conformation is unstable, it still has a large enough population time, enabling its experimental detection. In the second type, no such hydrophobic core exists. Hence, the native conformations of molecules belonging to this category have shorter population times, hindering their experimental detection. Although there is a continuum of distribution of hydrophobic cores in proteins, an empirical, statistically based hydrophobicity function may be used as a guideline for distinguishing the two disordered molecule types. Furthermore, the two types relate to steps in the protein folding reaction. With respect to protein design, this leads us to propose that engineering‐optimized specific electrostatic interactions to avoid electrostatic repulsion would reduce the type I disordered state, driving the molten globule (MG) → native (N) state. In contrast, for overcoming the type II disordered state, in addition to specific interactions, a stronger hydrophobic core is also indicated, leading to the denatured → MG → N state. Proteins 2001;44:418–427.

Dynamic Allostery: Linkers Are Not Merely Flexible

Most proteins consist of multiple domains. How do linkers efficiently transfer information between sites that are on different domains to activate the protein? Mere flexibility only implies that the conformations would be sampled. For fast timescales between triggering events and cellular response, which often involves large conformational change, flexibility on its own may not constitute a good solution. We posit that successive conformational states along major allosteric propagation pathways are pre-encoded in linker sequences where each state is encoded by the previous one. The barriers between these states that are hierarchically populated are lower, achieving faster timescales even for large conformational changes. We further propose that evolution has optimized the linker sequences and lengths for efficiency, which explains why mutations in linkers may affect protein function and review the literature in this light.

Electrostatic Strengths of Salt Bridges in Thermophilic and Mesophilic Glutamate Dehydrogenase Monomers

Here we seek to understand the higher frequency of occurrence of salt bridges in proteins from thermophiles as compared to their mesophile homologs. We focus on glutamate dehydrogenase, owing to the availability of high resolution thermophilic (from Pyrococcus furiosus) and mesophilic (from Clostridium symbiosum) protein structures, the large protein size and the large difference in melting temperatures. We investigate the location, statistics and electrostatic strengths of salt bridges and of their networks within corresponding monomers of the thermophilic and mesophilic enzymes. We find that many of the extra salt bridges which are present in the thermophilic glutamate dehydrogenase monomer but absent in the mesophilic enzyme, form around the active site of the protein. Furthermore, salt bridges in the thermostable glutamate dehydrogenase cluster within the hydrophobic folding units of the monomer, rather than between them. Computation of the electrostatic contribution of salt bridge energies by solving the Poisson equation in a continuum solvent medium, shows that the salt bridges in Pyrococcus furiosus glutamate dehydrogenase are highly stabilizing. In contrast, the salt bridges in the mesophilic Clostridium symbiosum glutamate dehydrogenase are only marginally stabilizing. This is largely the outcome of the difference in the protein environment around the salt bridges in the two proteins. The presence of a larger number of charges, and hence, of salt bridges contributes to an electrostatically more favorable protein energy term. Our results indicate that salt bridges and their networks may have an important role in resisting deformation/unfolding of the protein structure at high temperatures, particularly in critical regions such as around the active site. Proteins 2000;38:368–383. Published 2000 Wiley‐Liss, Inc.

Mechanisms of transcription factor selectivity

The initiation of transcription is regulated by transcription factors (TFs) binding to DNA response elements (REs). How do TFs recognize specific binding sites among the many similar ones available in the genome? Recent research has illustrated that even a single nucleotide substitution can alter the selective binding of TFs to coregulators, that prior binding events can lead to selective DNA binding, and that selectivity is influenced by the availability of binding sites in the genome. Here, we combine structural insights with recent genomics screens to address the problem of TF-DNA interaction specificity. The emerging picture of selective binding site sequence recognition and TF activation involves three major factors: the cellular network, protein and DNA as dynamic conformational ensembles and the tight packing of multiple TFs and coregulators on stretches of regulatory DNA. The classification of TF recognition mechanisms based on these factors impacts our understanding of how transcription initiation is regulated.

Allosteric post-translational modification codes

Post-translational modifications (PTMs) have been recognized to impact protein function in two ways: (i) orthosterically, via direct recognition by protein domains or through interference with binding; and (ii) allosterically, via conformational changes induced at the functional sites. Because different chemical types of PTMs elicit different structural alterations, the effects of combinatorial codes of PTMs are vastly larger than previously believed. Combined with orthosteric PTMs, the impact of PTMs on cellular regulation is immense. From an evolutionary standpoint, harnessing this immense, yet highly specific, PTM code is an extremely efficient vehicle that can save a cell several-fold in gene number and speed up its response to environmental change.