Intrinsic K-Ras dynamics: A novel molecular dynamics data analysis method shows causality between residue pair motions.
ABSTRACT: K-Ras is the most frequently mutated oncogene in human cancers, but there are still no drugs that directly target it in the clinic. Recent studies utilizing dynamics information show promising results for selectively targeting mutant K-Ras. However, despite extensive characterization, the mechanisms by which K-Ras residue fluctuations transfer allosteric regulatory information remain unknown. Understanding the direction of information flow can provide new mechanistic insights for K-Ras targeting. Here, we present a novel approach -conditional time-delayed correlations (CTC) - using the motions of all residue pairs of a protein to predict directionality in the allosteric regulation of the protein fluctuations. Analyzing nucleotide-dependent intrinsic K-Ras motions with the new approach yields predictions that agree with the literature, showing that GTP-binding stabilizes K-Ras motions and leads to residue correlations with relatively long characteristic decay times. Furthermore, our study is the first to identify driver-follower relationships in correlated motions of K-Ras residue pairs, revealing the direction of information flow during allosteric modulation of its nucleotide-dependent intrinsic activity: active K-Ras Switch-II region motions drive Switch-I region motions, while α-helix-3L7 motions control both. Our results provide novel insights for strategies that directly target mutant K-Ras.
Project description:Allosteric networks allow enzymes to transmit information and regulate their catalytic activities over vast distances. In principle, molecular dynamics (MD) simulations can be used to reveal the mechanisms that underlie this phenomenon; in practice, it can be difficult to discern allosteric signals from MD trajectories. Here, we describe how MD simulations can be analyzed to reveal correlated motions and allosteric networks, and provide an example of their use on the coagulation enzyme thrombin. Methods are discussed for calculating residue-pair correlations from atomic fluctuations and mutual information, which can be combined with contact information to identify allosteric networks and to dynamically cluster a system into highly correlated communities. In the case of thrombin, these methods show that binding of the antagonist hirugen significantly alters the enzyme's correlation landscape through a series of pathways between Exosite I and the catalytic core. Results suggest that hirugen binding curtails dynamic diversity and enforces stricter venues of influence, thus reducing the accessibility of thrombin to other molecules.
Project description:Allosteric regulation is a key component of cellular communication, but the way in which information is passed from one site to another within a folded protein is not often clear. While backbone motions have long been considered essential for long-range information conveyance, side-chain motions have rarely been considered. In this work, we demonstrate their potential utility using Monte Carlo sampling of side-chain torsional angles on a fixed backbone to quantify correlations amongst side-chain inter-rotameric motions. Results indicate that long-range correlations of side-chain fluctuations can arise independently from several different types of interactions: steric repulsions, implicit solvent interactions, or hydrogen bonding and salt-bridge interactions. These robust correlations persist across the entire protein (up to 60 Å in the case of calmodulin) and can propagate long-range changes in side-chain variability in response to single residue perturbations.
Project description:Proteins have often evolved sequences so as to acquire the ability for regulation via allosteric conformational change. Here we investigate how allosteric dynamics is designed through sequences with nonlinear interaction features. First, for 71 allosteric proteins of which two, open and closed, structures are available, a statistical survey of interactions using an all-atom model with effective solvation shows that those residue contact interactions specific to one of the two states are significantly weaker than are the contact interactions shared by the two states. This interaction feature indicates there is underlying sequence design to facilitate conformational change. Second, based on the energy landscape theory, we implement these interaction features into a new atomic-interaction-based coarse-grained model via a multiscale simulation protocol (AICG). The AICG model outperforms standard coarse-grained models for predictions of the native-state mean fluctuations and of the conformational change direction. Third, using the new model for adenylate kinase, we show that intrinsic fluctuations in one state contain rare and large-amplitude motions nearly reaching the other state. Such large-amplitude motions are realized partly by sequence specificity and partly by the nonlinear nature of contact interactions, leading to cracking. Both features enhance conformational transition rates.
Project description:Modern protein crystal structures are based nearly exclusively on X-ray data collected at cryogenic temperatures (generally 100 K). The cooling process is thought to introduce little bias in the functional interpretation of structural results, because cryogenic temperatures minimally perturb the overall protein backbone fold. In contrast, here we show that flash cooling biases previously hidden structural ensembles in protein crystals. By analyzing available data for 30 different proteins using new computational tools for electron-density sampling, model refinement, and molecular packing analysis, we found that crystal cryocooling remodels the conformational distributions of more than 35% of side chains and eliminates packing defects necessary for functional motions. In the signaling switch protein, H-Ras, an allosteric network consistent with fluctuations detected in solution by NMR was uncovered in the room-temperature, but not the cryogenic, electron-density maps. These results expose a bias in structural databases toward smaller, overpacked, and unrealistically unique models. Monitoring room-temperature conformational ensembles by X-ray crystallography can reveal motions crucial for catalysis, ligand binding, and allosteric regulation.
Project description:Inferring structural dependencies among a protein's side chains helps us understand their coupled motions. It is known that coupled fluctuations can reveal pathways of communication used for information propagation in a molecule. Side-chain conformations are commonly represented by multivariate angular variables, but existing partial correlation methods that can be applied to this inference task are not capable of handling multivariate angular data. We propose a novel method to infer direct couplings from this type of data, and show that this method is useful for identifying functional regions and their interactions in allosteric proteins.We developed a novel extension of canonical correlation analysis (CCA), which we call 'kernelized partial CCA' (or simply KPCCA), and used it to infer direct couplings between side chains, while disentangling these couplings from indirect ones. Using the conformational information and fluctuations of the inactive structure alone for allosteric proteins in the Ras and other Ras-like families, our method identified allosterically important residues not only as strongly coupled ones but also in densely connected regions of the interaction graph formed by the inferred couplings. Our results were in good agreement with other empirical findings. By studying distinct members of the Ras, Rho and Rab sub-families, we show further that KPCCA was capable of inferring common allosteric characteristics in the small G protein super-family.https://github.com/lsgh/ismb15
Project description:K-Ras is the most frequently mutated protein in human cancers. However, until very recently, its oncogenic mutants were viewed as undruggable. To develop inhibitors that directly target oncogenic K-Ras mutants, we need to understand both their mutant-specific and pan-mutant dynamics and conformations. Recently, we have investigated how the most frequently observed K-Ras mutation in cancer patients, G12D, changes its local dynamics and conformations (Vatansever et al., 2019). Here, we extend our analysis to study and compare the local effects of other frequently observed oncogenic mutations, G12C, G12V, G13D and Q61H. For this purpose, we have performed Molecular Dynamics (MD) simulations of each mutant when active (GTP-bound) and inactive (GDP-bound), analyzed their trajectories, and compared how each mutant changes local residue conformations, inter-protein distance distributions, local flexibility and residue pair correlated motions. Our results reveal that in the four active oncogenic mutants we have studied, the ?2 helix moves closer to the C-terminal of the ?3 helix. However, P-loop mutations cause ?3 helix to move away from Loop7, and only G12 mutations change the local conformational state populations of the protein. Furthermore, the motions of coupled residues are mutant-specific: G12 mutations lead to new negative correlations between residue motions, while Q61H destroys them. Overall, our findings on the local conformational states and protein dynamics of oncogenic K-Ras mutants can provide insights for both mutant-selective and pan-mutant targeted inhibition efforts.
Project description:Resistance to Inhibitors of Cholinesterase A (Ric-8A) is a 60-kDa cytosolic protein that has chaperone and guanine nucleotide exchange (GEF) activity toward heterotrimeric G protein ? subunits of the i, q, and 12/13 classes, catalyzing the release of GDP from G? and subsequent binding of GTP. In the absence of GTP or GTP analogs, and subsequent to GDP release, G? forms a stable nucleotide-free complex with Ric-8A. In this study, time-resolved fluorescence anisotropy measurements were employed to detect local motions of G?i1 labeled at selected sites with Alexa 488 (C5) fluorescent dye (Ax) in the GDP, GTP?S (collectively, GXP), and Ric-8A-bound states. Sites selected for Alexa 488 (C5) derivatization were in the ?-helical domain (residue 106), the ?-helical domain-Ras-like domain hinge (residue 63), Switch I (residue 180), Switch II (residue 209), Switch III (residue 238), the ?4 helix (residue 305), and at the junction between the purine-binding subsite in the ?6-?5 loop and the C-terminal ? helix (residue 330). In the GXP-bound states, the Alexa fluorophore reports local motions with correlation times ranging from 1.0 to 1.8 ns. The dynamics at Ax180 is slower in G?i1•GDP than in G?i1•GTP?S. The reverse is true at Ax209. The order parameters, S(2), for Alexa probes at switch residues are high (0.78-0.88) in G?i1•GDP and lower (0.67-0.75) in G?i1•GTP?S, although in crystal structures, switch segments are more ordered in the latter. Local motions at Ax63, Ax180, Ax209, and Ax330 are all markedly slower (2.3-2.8 ns) in G?i1:Ric-8A than in G?i1•GXP, and only modest (± 0.1) differences in S(2) are observed at most sites in G?i1:Ric-8A relative to G?i1•GXP. The slow dynamics suggests long-range correlated transitions within an ensemble of states and, particularly in the hinge and switch segments that make direct contact with Ric-8A. Induction of G?i1 structural heterogeneity by Ric-8A provides a mechanism for nucleotide release.
Project description:K-Ras is the most frequently mutated oncoprotein in human cancers, and G12D is its most prevalent mutation. To understand how G12D mutation impacts K-Ras function, we need to understand how it alters the regulation of its dynamics. Here, we present local changes in K-Ras structure, conformation and dynamics upon G12D mutation, from long-timescale Molecular Dynamics simulations of active (GTP-bound) and inactive (GDP-bound) forms of wild-type and mutant K-Ras, with an integrated investigation of atomistic-level changes, local conformational shifts and correlated residue motions. Our results reveal that the local changes in K-Ras are specific to bound nucleotide (GTP or GDP), and we provide a structural basis for this. Specifically, we show that G12D mutation causes a shift in the population of local conformational states of K-Ras, especially in Switch-II (SII) and ?3-helix regions, in favor of a conformation that is associated with a catalytically impaired state through structural changes; it also causes SII motions to anti-correlate with other regions. This detailed picture of G12D mutation effects on the local dynamic characteristics of both active and inactive protein helps enhance our understanding of local K-Ras dynamics, and can inform studies on the development of direct inhibitors towards the treatment of K-RasG12D-driven cancers.
Project description:A complete understanding of gene expression relies on a comprehensive understanding of the protein-RNA recognition process. However, the study of protein-RNA recognition is complicated by many factors that contribute to both binding affinity and specificity, including structure, energetics, dynamical motions, and cooperative interactions. Several recent studies have suggested that energetic coupling between residues contributes to formation of the complex between the U1A protein and stem loop 2 of U1 snRNA as a consequence of a cooperative network of interactions. We have performed molecular dynamics simulations on the U1A-RNA complex, including explicit water and counterions, and analyzed the results based on the calculated positional cross-correlations of atomic fluctuations. The results indicate that cross-correlations calculated on a per residue basis agree well with the observed inter-residue cooperativity and predict that the networks identified to date may also be coupled into an extensive hyper-network that reflects the intrinsic rigidity of the RNA recognition motif. In addition, we report a comparison of the MD calculated correlations with the results of a positional covariance analysis based on the sequences of 330 RNA recognition motifs, including U1A. The calculated inter-residue cross-correlations agree very well with the results of the sites exhibiting positional covariance. Collectively, these results strongly support the hypothesis that collective fluctuations contribute to cooperativity and the corresponding observed thermodynamic coupling. Predictions of additional sites in U1A that may be involved in cooperative networks are advanced.
Project description:Molecular dynamics (MD) simulations of proteins provide important information to understand their functional mechanisms, which are, however, likely to be hidden behind their complicated motions with a wide range of spatial and temporal scales. A straightforward and intuitive analysis of protein dynamics observed in MD simulation trajectories is therefore of growing significance with the large increase in both the simulation time and system size. In this study, we propose a novel description of protein motions based on the hierarchical clustering of fluctuations in the inter-atomic distances calculated from an MD trajectory, which constructs a single tree diagram, named a "Motion Tree", to determine a set of rigid-domain pairs hierarchically along with associated inter-domain fluctuations. The method was first applied to the MD trajectory of substrate-free adenylate kinase to clarify the usefulness of the Motion Tree, which illustrated a clear-cut dynamics picture of the inter-domain motions involving the ATP/AMP lid and the core domain together with the associated amplitudes and correlations. The comparison of two Motion Trees calculated from MD simulations of ligand-free and -bound glutamine binding proteins clarified changes in inherent dynamics upon ligand binding appeared in both large domains and a small loop that stabilized ligand molecule. Another application to a huge protein, a multidrug ATP binding cassette (ABC) transporter, captured significant increases of fluctuations upon binding a drug molecule observed in both large scale inter-subunit motions and a motion localized at a transmembrane helix, which may be a trigger to the subsequent structural change from inward-open to outward-open states to transport the drug molecule. These applications demonstrated the capabilities of Motion Trees to provide an at-a-glance view of various sizes of functional motions inherent in the complicated MD trajectory.