Protein kinases operate in a large number of distinct signaling pathways, where the tight regulation of their catalytic activity is crucial to the development and maintenance of eukaryotic organisms. The catalytic domains of different kinases adopt strikingly similar structures when they are active. By contrast, crystal structures of inactive kinases have revealed a remarkable plasticity in the kinase domain that allows the adoption of distinct conformations in response to interactions with specific regulatory domains or proteins.
The spatial and temporal control of phosphorylation of specific serine, threonine, or tyrosine residues is crucial to cellular growth and development, and this control relies on the proper regulation of protein kinases.
Protein kinases are usually kept off, and the acquisition of catalytic activity is often buried under multiple layers of control, ranging from the binding of allosteric effectors to alterations in the subcellular localization of the enzyme.Protein kinases are molecular switches that can adopt at least two extreme conformations: an “on” state that is maximally active, and an “off” state that has minimal activity. All protein kinases catalyze the same reaction, the transfer of the γ-phosphate of ATP to the hydroxyl group of serine, threonine, or tyrosine.
In this review we survey the conformational changes undergone by Ser/Thr and Tyr kinase domains as they turn on and off. After briefly introducing the structure of the kinase domain in the active state, we discuss two key regulatory elements within the domain, the activation loop and the αC helix.
Optimal phosphotransfer requires the precise spatial arrangement of several catalytic residues that are absolutely conserved among all known kinases.
The activation loop has the capacity to undergo large conformational changes when the kinase switches between inactive and active states (Johnson et al., 1996). For example, insulin receptor kinase (IRK) is activated by phosphorylation of three tyrosine residues within its activation loop.
This network of interactions
The ability of the activation loops of different kinases to adopt distinct conformations when the kinase is off has recently been exploited to great medical benefit. The anticancer drug Gleevec is a potent therapeutic in the treatment of chronic myelogenous leukemia, and it acts by inhibiting the kinase activity of the Bcr-Abl oncoprotein directly (Druker et al., 2001). Gleevec binds to an inactive form of Abl kinase selectively, stabilizing the activation loop in a conformation that mimics bound substrate (Schindler et al., 2000).
Protein folded states are kinetic hubs
Understanding molecular kinetics, and particularly protein folding, is a classic grand challenge in molecular biophysics. Network models, such as Markov state models (MSMs), are one potential solution to this problem. MSMs have recently yielded quantitative agreement with experimentally derived structures and folding rates for specific systems, leaving them positioned to potentially provide a deeper understanding of molecular kinetics that can lead to experimentally testable hypotheses. Here we use existing MSMs for the villin headpiece and NTL9, which were constructed from atomistic simulations, to accomplish this goal. In addition, we provide simpler, humanly comprehensible networks that capture the essence of molecular kinetics and reproduce qualitative phenomena like the apparent two-state folding often seen in experiments. Together, these models show that protein dynamics are dominated by stochastic jumps between numerous metastable states and that proteins have heterogeneous unfolded states (many unfolded basins that interconvert more rapidly with the native state than with one another) yet often still appear two-state. Most importantly, we find that protein native states are hubs that can be reached quickly from any other state. However, metastability and a web of nonnative states slow the average folding rate. Experimental tests for these findings and their implications for other fields, like protein design, are also discussed.
Coarse-Grained Free Energy Functions for Studying Protein Conformational Changes: A Double-Well Network Model
In this work, a double-well network model (DWNM) is presented for generating a coarse-grained free energy function that can be used to study the transition between reference conformational states of a protein molecule. Compared to earlier work that uses a single, multidimensional double-well potential to connect two conformational states, the DWNM uses a set of interconnected double-well potentials for this purpose. The DWNM free energy function has multiple intermediate states and saddle points, and is hence a “rough” free energy landscape. In this implementation of the DWNM, the free energy function is reduced to an elastic-network model representation near the two reference states. The effects of free energy function roughness on the reaction pathways of protein conformational change is demonstrated by applying the DWNM to the conformational changes of two protein systems: the coil-to-helix transition of the DB-loop in G-actin and the open-to-closed transition of adenylate kinase. In both systems, the rough free energy function of the DWNM leads to the identification of distinct minimum free energy paths connecting two conformational states. These results indicate that while the elastic-network model captures the low-frequency vibrational motions of a protein, the roughness in the free energy function introduced by the DWNM can be used to characterize the transition mechanism between protein conformations.
The General Structure of MAPK Pathways
In molecular biology the MAPK (mitogen-activated protein kinase) pathway is considered to be a paradigm for signal transduction, as it occupies a central role in key cellular processes and is evolutionarily conserved. Various manifestations of the MAPK pathway are found in all eukaryotic cells so far examined and have been studied extensively in a multitude of organisms, ranging from yeast to humans. On the basis of the substantial body of data available in the literature, this pathway has frequently been the system of choice for computational modelling of biological signal transduction over the last decade.
The term ‘MAPK pathway’ refers to a module of three kinases which are activated by sequentially phosphorylating each other in response to a diverse range of stimuli, such as cytokines, growth factors, neurotransmitters, cellular stress and cell adherence. Accordingly, the pathway plays a pivotal role in many key cellular processes, ranging from growth control in all its variations, cell differentiation and survival to cellular adaptation to chemical and physical stress (for reviews, see [1–3]).
The MAPK pathway employs one of the most generic signalling designs found in biological signal transduction, namely that of a cycle formed by a kinase phosphorylating a target protein and an opposing phosphatase that is in charge of dephosphorylating the target (Figure 1). This type of protein phosphorylation presents a fundamental mechanism by which the activities of numerous enzymes, receptors, transporters, docking and scaffolding proteins are regulated.
The Details of a Protein’s Conformation Determine Its Chemistry
Proteins have impressive chemical capabilities because the neighboring chemical groups on their surface often interact in ways that enhance the chemical reactivity of amino acid side chains. These interactions fall into two main categories.
First, neighboring parts of the polypeptide chain may interact in a way that restricts the access of water molecules to a ligand binding site. Because water molecules tend to form hydrogen bonds, they can compete with ligands for sites on the protein surface. The tightness of hydrogen bonds (and ionic interactions) between proteins and their ligands is therefore greatly increased if water molecules are excluded. Initially, it is hard to imagine a mechanism that would exclude a molecule as small as water from a protein surface without affecting the access of the ligand itself. Because of the strong tendency of water molecules to form water–water hydrogen bonds, however, water molecules exist in a large hydrogen-bonded network (see Panel 2-2, pp. 112–113). In effect, a ligand binding site can be kept dry because it is energetically unfavorable for individual water molecules to break away from this network, as they must do to reach into a crevice on a protein’s surface.
Second, the clustering of neighboring polar amino acid side chains can alter their reactivity. If a number of negatively charged side chains are forced together against their mutual repulsion by the way the protein folds, for example, the affinity of the site for a positively charged ion is greatly increased. In addition, when amino acid side chains interact with one another through hydrogen bonds, normally unreactive side groups (such as the –CH2OH on the serine shown in Figure 3-39) can become reactive, enabling them to enter into reactions that make or break selected covalent bonds.
The surface of each protein molecule therefore has a unique chemical reactivity that depends not only on which amino acid side chains are exposed, but also on their exact orientation relative to one another. For this reason, even two slightly different conformations of the same protein molecule may differ greatly in their chemistry.
The most common type of control occurs when a molecule other than one of the substrates binds to an enzyme at a special regulatory site outside the active site, thereby altering the rate at which the enzyme converts its substrates to products. In feedback inhibition, an enzyme acting early in a reaction pathway is inhibited by a late product of that pathway. Thus, whenever large quantities of the final product begin to accumulate, this product binds to the first enzyme and slows down its catalytic action, thereby limiting the further entry of substrates into that reaction pathway (Figure 3-55). Where pathways branch or intersect, there are usually multiple points of control by different final products, each of which works to regulate its own synthesis (Figure 3-56). Feedback inhibition can work almost instantaneously and is rapidly reversed when the level of the product falls.