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Allostery describes the functional coupling between sites in biomolecules. Recently, the role of changes in protein dynamics for allosteric communication has been highlighted. A quantitative and predictive description of allostery is fundamental for understanding biological processes. Here, we integrate an ensemble-based perturbation approach with the analysis of biomolecular rigidity and flexibility to construct a model of dynamic allostery. Our model, by definition, excludes the possibility of conformational changes, evaluates static, not dynamic, properties of molecular systems, and describes allosteric effects due to ligand binding in terms of a novel free-energy measure. We validated our model on three distinct biomolecular systems: eglin c, protein tyrosine phosphatase 1B, and the lymphocyte function-associated antigen 1 domain. In all cases, it successfully identified key residues for signal transmission in very good agreement with the experiment. It correctly and quantitatively discriminated between positively or negatively cooperative effects for one of the systems. Our model should be a promising tool for the rational discovery of novel allosteric drugs.

Modulating protein activity with small molecules binding to cryptic pockets offers great opportunities to overcome hurdles in drug design. Cryptic sites are atypical binding sites in proteins that are closed in the absence of a stabilizing ligand and are thus inherently difficult to identify. Many studies have proposed methods to predict cryptic sites. However, a general approach to prospectively sample open conformations of these sites and to identify cryptic pockets in an unbiased manner suitable for structure-based drug design remains elusive. Here, we describe an all-atom, explicit cosolvent, molecular dynamics (MD) simulations-based workflow to sample the open states of cryptic sites and identify opened pockets, in a manner that does not require a priori knowledge about these sites. Furthermore, the workflow relies on a target-independent parameterization that only distinguishes between binding pockets for peptides or small-molecules. We validated our approach on a diverse test set of seven proteins with crystallographically determined cryptic sites. The known cryptic sites were found among the three highest-ranked predicted cryptic sites, and an open site conformation was sampled and selected for most of the systems. Crystallographic ligand poses were well reproduced by docking into these identified open conformations for five of the systems. When the fully open state could not be reproduced, we were still able to predict the location of the cryptic site, or identify other cryptic sites that could be retrospectively validated with knowledge of the protein target. These characteristics render our approach valuable for investigating novel protein targets without any prior information.

The expertise of protein kinases lies in their dynamic structure, wherein they are able to modulate cellular signaling by their phosphotransferase activity. Only a few hundreds of protein kinases regulate key processes in human cells, and protein kinases play a pivotal role in health and disease. The present study dwells on understanding the working of the protein kinase-molecular switch as an allosteric network of “communities” composed of congruently dynamic residues that make up the protein kinase core. Girvan–Newman algorithm-based community maps of the kinase domain of cAMP-dependent protein kinase A allow for a molecular explanation for the role of protein conformational entropy in its catalytic cycle. The community map of a mutant, Y204A, is analyzed vis-a-vis the wild-type protein to study the perturbations in its dynamic profile such that it interferes with transfer of the γ-phosphate to a protein substrate. Conventional biochemical measurements are used to ascertain the effect of these dynamic perturbations on the kinetic profiles of both proteins. These studies pave the way for understanding how mutations far from the kinase active site can alter its dynamic properties and catalytic function even when major structural perturbations are not obvious from static crystal structures.

Conformational fluctuations play a central role in enzymatic catalysis. However, it is not clear how the rates and the coordination of the motions affect the different catalytic steps. Here, we used NMR spectroscopy to analyze the conformational fluctuations of the catalytic subunit of the cAMP-dependent protein kinase (PKA-C), a ubiquitous enzyme involved in a myriad of cell signaling events. We found that the wild-type enzyme undergoes synchronous motions involving several structural elements located in the small lobe of the kinase, which is responsible for nucleotide binding and release. In contrast, a mutation (Y204A) located far from the active site desynchronizes the opening and closing of the active cleft without changing the enzyme's structure, rendering it catalytically inefficient. Since the opening and closing motions govern the rate-determining product release, we conclude that optimal and coherent conformational fluctuations are necessary for efficient turnover of protein kinases.

SummaryRemoval of the 5′ cap structure by Dcp2 is a major step in several 5′–3′ mRNA decay pathways. The activity of Dcp2 is enhanced by Dcp1 and bound coactivators, yet the details of how these interactions are linked to chemistry are poorly understood. Here, we report three crystal structures of the catalytic Nudix hydrolase domain of Dcp2 that demonstrate binding of a catalytically essential metal ion, and enzyme kinetics are used to identify several key active site residues involved in acid/base chemistry of decapping. Using nuclear magnetic resonance and molecular dynamics, we find that a conserved metal binding loop on the catalytic domain undergoes conformational changes during the catalytic cycle. These findings describe key events during the chemical step of decapping, suggest local active site conformational changes are important for activity, and provide a framework to explain stimulation of catalysis by the regulatory domain of Dcp2 and associated coactivators.

Oligomerization of G protein-coupled receptors is a recognized mode of regulation of receptor activities, with alternate oligomeric states resulting in different signaling functions. The CXCR4 chemokine receptor is a G protein-coupled receptor that is post-translationally modified by tyrosine sulfation at three sites on its N-terminus (Y7, Y12, Y21), leading to enhanced affinity for its ligand, stromal cell derived factor (SDF-1, also called CXCL12). The complex has been implicated in cancer metastasis and is a therapeutic target in cancer treatment. Using molecular dynamics simulation of NMR-derived structures of the CXCR4 N-terminus in complex with SDF-1, and calculations of electrostatic binding energies for these complexes, we address the role of tyrosine sulfation in this complex. Our results show that sulfation stabilizes the dimeric state of the CXCR4:SDF-1 complex through hydrogen bonding across the dimer interface, conformational changes in residues at the dimer interface, and an enhancement in electrostatic binding energies associated with dimerization. These findings suggest a mechanism through which post-translational modifications such as tyrosine sulfation might regulate downstream function through modulation of the oligomeric state of the modified system.

The catalytic subunit of protein kinase A (PKA-C) is subject to several post- or cotranslational modifications that regulate its activity both spatially and temporally. Among those, N-myristoylation increases the kinase affinity for membranes and might also be implicated in substrate recognition and allosteric regulation. Here, we investigated the effects of N-myristoylation on the structure, dynamics, and conformational equilibrium of PKA-C using atomistic molecular dynamics simulations. We found that the myristoyl group inserts into the hydrophobic pocket and leads to a tighter packing of the A-helix against the core of the enzyme. As a result, the conformational dynamics of the A-helix are reduced and its motions are more coupled with the active site. Our simulations suggest that cation-π interactions among W30, R190, and R93 are responsible for coupling these motions. Two major conformations of the myristoylated N-terminus are the most populated: a long loop (LL conformation), similar to Protein Data Bank (PDB) entry 1CMK , and a helix-turn-helix structure (HTH conformation), similar to PDB entry 4DFX , which shows stronger coupling between the conformational dynamics observed at the A-helix and active site. The HTH conformation is stabilized by S10 phosphorylation of the kinase via ionic interactions between the protonated amine of K7 and the phosphate group on S10, further enhancing the dynamic coupling to the active site. These results support a role of N-myristoylation in the allosteric regulation of PKA-C.

We present a thermodynamical approach to identify changes in macromolecular structure and dynamics in response to perturbations such as mutations or ligand binding, using an expansion of the Kullback-Leibler Divergence that connects local population shifts in torsion angles to changes in the free energy landscape of the protein. While the Kullback-Leibler Divergence is a known formula from information theory, the novelty and power of our implementation lies in its formal developments, connection to thermodynamics, statistical filtering, ease of visualization of results, and extendability by adding higher-order terms. We present a formal derivation of the Kullback-Leibler Divergence expansion and then apply our method at a first-order approximation to molecular dynamics simulations of four protein systems where ligand binding or pH titration is known to cause an effect at a distant site. Our results qualitatively agree with experimental measurements of local changes in structure or dynamics, such as NMR chemical shift perturbations and hydrogen-deuterium exchange mass spectrometry. The approach produces easy-to-analyze results with low background, and as such has the potential to become a routine analysis when molecular dynamics simulations in two or more conditions are available. Our method is implemented in the MutInf code package and is available on the SimTK website at https://simtk.org/home/mutinf.

For disease network intervention, up-regulating enzyme activities is equally as important as down-regulating activities. However, the design of enzyme activators presents a challenging route for drug discovery. Previous studies have suggested that activating 15-lipoxygenase (15-LOX) is a promising strategy to intervene the arachidonic acid (AA) metabolite network and control inflammation. To prove this concept, we used a computational approach to discover a previously unknown allosteric site on 15-LOX. Both allosteric inhibitors and novel activators were discovered using this site. The influence of activating 15-LOX on the AA metabolite network was then investigated experimentally. The activator was found to increase levels of 15-LOX products and reduce production of pro-inflammatory mediators in human whole blood assays. These results demonstrate the promising therapeutic value of enzyme activators and aid in further development of activators of other proteins.

Seryl-tRNA synthetase (SerRS) charges serine to tRNA^(Ser) following the formation of a seryl adenylate intermediate, but the extent to which other non-cognate amino acids compete with serine to bind to SerRS or for the formation of the activated seryl adenylate intermediate is not known. To examine the mechanism of discrimination against non-cognate amino acids, we calculated the relative binding energies of the 20 natural amino acids to SerRS. Starting with the crystal structure of SerRS from Thermus thermophilus with seryl adenylate bound, we used the HierDock and SCREAM (Side-Chain Rotamer Energy Analysis Method) computational methods to predict the binding conformation and binding energy of each of the 20 natural amino acids in the binding site in the best-binding mode and the activating mode. The ordering of the calculated binding energies in the activated mode agrees with kinetic measurements in yeast SerRS that threonine will compete with serine for formation of the activated intermediate while alanine and glycine will not compete significantly. In addition, we predict that asparagine will compete with serine for formation of the activated intermediate. Experiments to check the accuracy of this prediction would be useful in further validating the use of HierDock and SCREAM for designing novel amino acids to incorporate into proteins and for determining mutations in aminoacyl-tRNA synthetase design to facilitate the incorporation of amino acid analogs into proteins.

Protein kinases are dynamically regulated signaling proteins that act as switches in the cell by phosphorylating target proteins. To establish a framework for analyzing linkages between structure, function, dynamics, and allostery in protein kinases, we carried out multiple microsecond-scale molecular-dynamics simulations of protein kinase A (PKA), an exemplar active kinase. We identified residue-residue correlated motions based on the concept of mutual information and used the Girvan-Newman method to partition PKA into structurally contiguous "communities." Most of these communities included 40-60 residues and were associated with a particular protein kinase function or a regulatory mechanism, and well-known motifs based on sequence and secondary structure were often split into different communities. The observed community maps were sensitive to the presence of different ligands and provide a new framework for interpreting long-distance allosteric coupling. Communication between different communities was also in agreement with the previously defined architecture of the protein kinase core based on the "hydrophobic spine" network. This finding gives us confidence in suggesting that community analyses can be used for other protein kinases and will provide an efficient tool for structural biologists. The communities also allow us to think about allosteric consequences of mutations that are linked to disease.

Caspases are intracellular cysteine-class proteases with aspartate specificity that is critical for driving processes as diverse as the innate immune response and apoptosis, exemplified by caspase-1 and caspase-3, respectively. Interestingly, caspase-1 cleaves far fewer cellular substrates than caspase-3 and also shows strong positive cooperativity between the two active sites of the homodimer, unlike caspase-3. Biophysical and kinetic studies here present a molecular basis for this difference. Analytical ultracentrifugation experiments show that mature caspase-1 exists predominantly as a monomer under physiological concentrations that undergoes dimerization in the presence of substrate; specifically, substrate binding shifts the KD for dimerization by 20-fold. We have created a hemi-active site-labeled dimer of caspase-1, where one site is blocked with the covalent active site inhibitor, benzyloxycarbonyl-Val-Ala-Asp-fluoromethylketone. This hemi-labeled enzyme is about 9-fold more active than the apo-dimer of caspase-1. These studies suggest that substrate not only drives dimerization but also, once bound to one site in the dimer, promotes an active conformation in the other monomer. Steady-state kinetic analysis and modeling independently support this model, where binding of one substrate molecule not only increases substrate binding in preformed dimers but also drives the formation of heterodimers. Thus, the cooperativity in caspase-1 is driven both by substrate-induced dimerization as well as substrate-induced activation. Substrate-induced dimerization and activation seen in caspase-1 and not in caspase-3 may reflect their biological roles. Whereas caspase-1 cleaves a dramatically smaller number of cellular substrates that need to be concentrated near inflammasomes, caspase-3 is a constitutively active dimer that cleaves many more substrates located diffusely throughout the cell.

The Janus Kinase 2 (JAK2) plays essential roles in transmitting signals from multiple cytokine receptors, and constitutive activation of JAK2 results in hematopoietic disorders and oncogenesis. JAK2 kinase activity is negatively regulated by its pseudokinase domain (JH2), where the gain-of-function mutation V617F that causes myeloproliferative neoplasms resides. In the absence of a crystal structure of full-length JAK2, how JH2 inhibits the kinase domain (JH1), and how V617F hyperactivates JAK2 remain elusive. We modeled the JAK2 JH1–JH2 complex structure using a novel informatics-guided protein-protein docking strategy. A detailed JAK2 JH2-mediated auto-inhibition mechanism is proposed, where JH2 traps the activation loop of JH1 in an inactive conformation and blocks the movement of kinase αC helix through critical hydrophobic contacts and extensive electrostatic interactions. These stabilizing interactions are less favorable in JAK2-V617F. Notably, several predicted binding interfacial residues in JH2 were confirmed to hyperactivate JAK2 kinase activity in site-directed mutagenesis and BaF3/EpoR cell transformation studies. Although there may exist other JH2-mediated mechanisms to control JH1, our JH1–JH2 structural model represents a verifiable working hypothesis for further experimental studies to elucidate the role of JH2 in regulating JAK2 in both normal and pathological settings., Author Summary Protein-protein interactions (PPIs) are essential to cellular signal transduction, and structural information about PPIs is crucial for understanding of how cellular machinery functions at the atomistic level. However, both experimental structural determination and computational prediction of PPI are challenging. In the cytoplasmic tyrosine kinase JAK2, a pseudokinase domain (JH2) negatively regulates kinase activity of its adjacent catalytic kinase domain (JH1). A gain-of-function mutation within JH2 is found in the majority of patients with myeloproliferative neoplasms, and is sufficient to cause similar diseases in murine models. Here we combined an informatics-guided protein-protein docking method with molecular dynamics simulation to construct and refine the JAK2 JH1–JH2 complex, and validated our model with mutational studies. Our modeled structure suggests that JH2 auto-inhibits JAK2 kinase activities by blocking the movements of the activation loop and the αC helix of JH1, but awaits further validation by a detailed structure of the full-length JAK2 protein.

The dynamics of proteins is essential for the quantification of various cellular processes like rates of enzymatic reactions, signal transduction and protein association reactions. However, understanding the structure and dynamics of macromolecules in a cell is complicated by the highly crowded nature of the cell. It is likely that properties of macromolecules in cell may differ significantly to that measured in dilute solution. Diffusion plays important roles in many processes occurring inside the cell. The estimation of diffusion coefficient of macromolecules in a cell can be considered as a first step in understanding the complex nature of the heterogeneous environment of the cell.In this current work we developed a computational model of E. coli cytoplasm and performed extensive Brownian dynamics simulation to calculate diffusivity of proteins. Our model differs from some of the previous models of E. coli cytoplasm in the following way; (1) The proteins modeled as flexible units by considering them as a collection of spheres. (2) hydrodynamic interaction (HI), which is essential to get accurate diffusion coefficient, was considered using a mean field approach.The model predicts accurately the diffusion coefficient of Green Fluorescent Protein (GFP) in E.coli cell. We have found that HI is essential to get correct diffusion coefficient for this highly crowded system. The presence of anomalous diffusion has also been observed for short time (∼1 micro sec), which was identified using fractional Brownian motion (FBM) analysis. It was found that repulsive interaction between different proteins is the main reason for the anomalous diffusion. To understand the anomalous diffusion observed in simulations, we also formulated a one dimensional random walk model in which successive steps are biased and correlated. This analytical model can explain some of the findings from our simulation.

There is significant interest in identifying and characterizing allosteric sites in enzymes such as protein kinases both for understanding allosteric mechanisms as well as for drug discovery. Here, we apply a site-directed technology, disulfide trapping, to interrogate structurally and functionally how an allosteric site on the Ser/Thr kinase, 3-phosphoinositide-dependent kinase 1 (PDK1)—the PDK1-interacting-fragment (PIF) pocket—is engaged by an activating peptide motif on downstream substrate kinases (PIFtides) and by small molecule fragments. By monitoring pairwise disulfide conjugation between PIFtide and PDK1 cysteine mutants, we defined the PIFtide binding orientation in the PIF pocket of PDK1 and assessed subtle relationships between PIFtide positioning and kinase activation. We also discovered a variety of small molecule fragment disulfides (or inhibit PDK1 by conjugation to the PIF pocket, thus displaying greater functional diversity than is displayed by PIFtides conjugated to the same sites. Biochemical data and three crystal structures provided insight into the mechanism of action of the best fragment activators and inhibitors. These studies show that disulfide trapping is useful for characterizing allosteric sites on kinases and that a single allosteric site on a protein kinase can be exploited for both activation and inhibition by small molecules.

Ubiquitin conjugating proteins (E2s) are an important component of the ubiquitin proteasome pathway. E2s interact with ubiquitin activating enzyme (E1) and ubiquitin ligases (E3s) to transfer ubiquitin to target proteins to mark them for degradation. In some E3s, RING domains act as scaffolds to bind E2s and target proteins so that the ubiquitin can be directly transferred from the E2 to the target protein. This ubiquitin transfer has been shown to be allosterically regulated: E3 RING domain binding to E2 at one site promotes ubiquitin release from the active site cysteine (∼15A distance) without substantial conformational change in E2. Previous studies used statistical coupling analysis (SCA) to identify clusters of residues that might transmit information. Here, we use a novel information-theory approach to identify residues with statistically significant correlated conformations in a set of equilibrium molecular dynamics simulations. From the matrix of correlations between residues, we observed substantial coupling between an E2's active site and its E3 RING domain binding site. However, in the I88A mutant, the pattern of correlations is disrupted, consistent with the experimental observation that this I88A mutation abrogates the allostery in the E2s. Thus, our approach is sensitive enough to identify effects of single point mutations in the protein. Unlike SCA, which infers couplings from many protein sequences, our approach identifies couplings between residues in individual proteins, some of which coincide with residues identified by SCA. As our approach is general and sensitive to small physical-chemical differences in sequence, structure, and dynamics, we can apply our approach to study similarities and differences in the allosteric networks of different E2s in order to better understand how protein degradation is regulated, also providing a mechanistic insight of the process.

Allostery describes altered protein function at one site due to a perturbation at another site. One mechanism of allostery involves correlated motions, which can occur even in the absence of substantial conformational change. We present a novel method, “MutInf”, to identify statistically significant correlated motions from equilibrium molecular dynamics simulations. Our approach analyzes both backbone and sidechain motions using internal coordinates to account for the gear-like twists that can take place even in the absence of the large conformational changes typical of traditional allosteric proteins. We quantify correlated motions using a mutual information metric, which we extend to incorporate data from multiple short simulations and to filter out correlations that are not statistically significant. Applying our approach to uncover mechanisms of cooperative small molecule binding in human interleukin-2, we identify clusters of correlated residues from 50 ns of molecular dynamics simulations. Interestingly, two of the clusters with the strongest correlations highlight known cooperative small-molecule binding sites and show substantial correlations between these sites. These cooperative binding sites on interleukin-2 are correlated not only through the hydrophobic core of the protein but also through a dynamic polar network of hydrogen bonding and electrostatic interactions. Since this approach identifies correlated conformations in an unbiased, statistically robust manner, it should be a useful tool for finding novel or “orphan” allosteric sites in proteins of biological and therapeutic importance.

Targeting the interfaces between proteins has huge therapeutic potential, but discovering small-molecule drugs that disrupt protein-protein interactions is an enormous challenge. Several recent success stories, however, indicate that protein-protein interfaces might be more tractable than has been thought. These studies discovered small molecules that bind with drug-like potencies to 'hotspots' on the contact surfaces involved in protein-protein interactions. Remarkably, these small molecules bind deeper within the contact surface of the target protein, and bind with much higher efficiencies, than do the contact atoms of the natural protein partner. Some of these small molecules are now making their way through clinical trials, so this high-hanging fruit might not be far out of reach.

Soon, a new cluster of parallel Pentium 4 machines will be set up at JLAB to run Lattice QCD calculations. I discuss the rationale for optimized Lattice QCD routines, and how the features of the Pentium 4 enable new optimized routines to run much faster than normal C routines. I describe the optimization strategies used in SU(3) linear algebra routines, and in both single-node and parallel implementations of the Wilson-Dirac Operator. Finally, I show single node performance timings for the parallel version of the Wilson-Dirac operator.

Allostery describes altered protein function at one site due to a perturbation at another site. One mechanism of allostery involves correlated motions, which can occur even in the absence of substantial conformational change. We present a novel method, “MutInf”, to identify statistically significant correlated motions from equilibrium molecular dynamics simulations. Our approach analyzes both backbone and side chain motions using internal coordinates to account for the gear-like twists that can take place even in the absence of the large conformational changes typical of traditional allosteric proteins. We quantify correlated motions using a mutual information metric, which we extend to incorporate data from multiple short simulations and to filter out correlations that are not statistically significant. Applying our approach to uncover mechanisms of cooperative small molecule binding in human interleukin-2, we identify clusters of correlated residues from 50 ns of molecular dynamics simulations. Interest...