Methyltransferases possess a homologous domain that requires both a divalent metal cation and S-adenosyl-L-methionine (SAM) to catalyze its reactions. The kinetics of several methyltransferases has been well characterized; however, the details regarding their structural mechanisms have remained unclear to date. Using catechol O-methyltransferase (COMT) as a model, we perform discrete molecular dynamics and computational docking simulations to elucidate the initial stages of cofactor binding. We find that COMT binds SAM via an induced-fit mechanism, where SAM adopts a different docking pose in the absence of metal and substrate in comparison to the holoenzyme. Flexible modeling of the active site side-chains is essential for observing the lowest energy state in the apoenzyme; rigid docking tools are unable to recapitulate the pose unless the appropriate side-chain conformations are given a priori. From our docking results, we hypothesize that the metal reorients SAM in a conformation suitable for donating its methyl substituent to the recipient ligand. The proposed mechanism enables a general understanding of how divalent metal cations contribute to methyltransferase function.
We describe a computational protocol, called DDMI, for redesigning scaffold proteins to bind to a specified region on a target protein. The DDMI protocol is implemented within the Rosetta molecular modeling program and uses rigid-body docking, sequence design, and gradient-based minimization of backbone and side chain torsion angles to design low energy interfaces between the scaffold and target protein. Iterative rounds of sequence design and conformational optimization were needed to produce models that have calculated binding energies that are similar to binding energies calculated for native complexes. We also show that additional conformation sampling with molecular dynamics can be iterated with sequence design to further lower the computed energy of the designed complexes. To experimentally test the DDMI protocol we redesigned the human hyperplastic discs protein to bind to the kinase domain of p21-activated kinase 1 (PAK1). Six designs were experimentally characterized. Two of the designs aggregated and were not characterized further. Of the remaining four designs, three bound to the PAK1 with affinities tighter than 350 μM. The tightest binding design, named Spider Roll, bound with an affinity of 100 μM. NMR –based structure prediction of Spider Roll based on backbone and 13Cβ chemical shifts using the program CS-ROSETTA indicated that the architecture of human hyperplastic discs protein is preserved. Mutagenesis studies confirmed that Spider Roll binds the target patch on PAK1. Additionally, Spider Roll binds to full length PAK1 in its activated state, but does not bind PAK1 when it forms an auto-inhibited conformation that blocks the Spider Roll target site. Subsequent NMR characterization of the binding of Spider Roll to PAK1 revealed a comparably small binding `on-rate' constant (<< 105 M−1 s−1). The ability to rationally design the site of novel protein-protein interactions is an important step towards creating new proteins that are useful as therapeutics or molecular probes.
Computational protein design; protein-protein interactions; protein docking; Rosetta molecular modeling program; NMR; CS-ROSETTA
RNA function is dependent on its structure, yet three-dimensional folds for most biologically important RNAs are unknown. We develop a generic discrete molecular dynamics (DMD)-based modeling system that uses long-range constraints inferred from diverse biochemical or bioinformatic analyses to create statistically significant (p < 0.01) native-like folds for RNAs of known structure ranging from 45 to 158 nucleotides. We then predict the unknown structure of the hepatitis C virus IRES pseudoknot domain. The resulting RNA model rationalizes independent solvent accessibility and cryo-electron microscopy structure information. The pseudoknot positions the AUG start codon near the mRNA channel and is tRNA-like, suggesting the IRES employs molecular mimicry as a functional strategy.
Catechol-O-methyltransferase (COMT) is a major enzyme controlling catecholamine levels that plays a central role in cognition, affective mood and pain perception. There are three common COMT haplotypes in the human population reported to have functional effects, divergent in two synonymous and one nonsynonymous position. We demonstrate that one of the haplotypes, carrying the non-synonymous variation known to code for a less stable protein, exhibits increased protein expression in vitro. This increased protein expression, which would compensate for lower protein stability, is solely produced by a synonymous variation (C166T) situated within the haplotype and located in the 5′ region of the RNA transcript. Based on mRNA secondary structure predictions, we suggest that structural destabilization near the start codon caused by the T allele could be related to the observed increase in COMT expression. Our folding simulations of the tertiary mRNA structures demonstrate that destabilization by the T allele lowers the folding transition barrier, thus decreasing the probability of occupying its native state. These data suggest a novel structural mechanism whereby functional synonymous variations near the translation initiation codon affect the translation efficiency via entropy-driven changes in mRNA dynamics and present another example of stable compensatory genetic variations in the human population.
A cell's interior is comprised of macromolecules that can occupy up to 40% of its available volume. Such crowded environments can influence the stability of proteins and their rates of reaction. Using discrete molecular dynamics simulations, we investigate how both the size and number of neighboring crowding reagents affect the thermodynamic and folding properties of structurally diverse proteins. We find that crowding induces higher compaction of proteins. We also find that folding becomes less cooperative with the introduction of crowders into the system. The crowders may induce alternative non-native protein conformations, thus creating barriers for protein folding in highly crowded media.
While various approaches exist to study protein localization, it is still a challenge to predict where proteins localize. Here, we consider a mechanistic viewpoint for membrane localization. Taking into account the steps for the folding pathway of α-helical membrane proteins and relating biophysical parameters to each of these steps, we create a score capable of predicting the propensity for membrane localization and call it FP3mem. This score is driven from the principal component analysis (PCA) of the biophysical parameters related to membrane localization. FP3mem allows us to rationalize the colocalization of a number of channel proteins with the Cav1.2 channel by their fewer propensities for membrane localization.
We present a method to calculate the propensities of regions within a DNA molecule to transition from B-form to Z-form under negative superhelical stresses. We use statistical mechanics to analyze the competition that occurs among all susceptible Z-forming regions at thermodynamic equilibrium in a superhelically stressed DNA of specified sequence. This method, which we call SIBZ, is similar to the SIDD algorithm that was previously developed to analyze superhelical duplex destabilization. A state of the system is determined by assigning to each base pair either the B- or the Z-conformation, accounting for the dinucleotide repeat unit of Z-DNA. The free energy of a state is comprised of the nucleation energy, the sequence-dependent B-Z transition energy, and the energy associated with the residual superhelicity remaining after the change of twist due to transition. Using this information, SIBZ calculates the equilibrium B-Z transition probability of each base pair in the sequence. This can be done at any physiologically reasonable level of negative superhelicity. We use SIBZ to analyze a variety of representative genomic DNA sequences. We show that the dominant Z-DNA forming regions in a sequence can compete in highly complex ways as the superhelicity level changes. Despite having no tunable parameters, the predictions of SIBZ agree precisely with experimental results, both for the onset of transition in plasmids containing introduced Z-forming sequences and for the locations of Z-forming regions in genomic sequences. We calculate the transition profiles of 5 kb regions taken from each of 12,841 mouse genes and centered on the transcription start site (TSS). We find a substantial increase in the frequency of Z-forming regions immediately upstream from the TSS. The approach developed here has the potential to illuminate the occurrence of Z-form regions in vivo, and the possible roles this transition may play in biological processes.
We present the SIBZ algorithm that calculates the equilibrium properties of the transition from right-handed B-form to left-handed Z-form in a DNA sequence that is subjected to imposed stresses. SIBZ calculates the probability of transition of each base pair in a user-defined sequence. By examining illustrative examples, we show that the transition behaviors of all Z-susceptible regions in a sequence are coupled together by the imposed stresses. We show that the results produced by SIBZ agree closely with experimental observations of both the onset of transitions and the locations of Z-form sites in molecules of specified sequence. By analyzing 12,841 mouse genes, we show that sites susceptible to the B-Z transition cluster upstream from gene start sites. As this is where stresses generated by transcription accumulate, these sites may actually experience this transition when the genes involved are being expressed. This suggests that these transitions may serve regulatory functions.
Determining the forces that conserve amino acid positions in proteins across species is a fundamental pursuit of molecular evolution. Evolutionary conservation is driven by either a protein's function or its thermodynamic stability. Highly conserved histone proteins offer a platform to evaluate these driving forces. While the conservation of histone H3 and H4 “tail” domains and surface residues are driven by functional importance, the driving force behind the conservation of buried histone residues has not been examined. Using a computational approach, we determined the thermodynamically preferred amino acids at each buried position in H3 and H4. In agreement with what is normally observed in proteins, we find a significant correlation between thermodynamic stability and evolutionary conservation in the buried residues in H4. In striking contrast, we find that thermodynamic stability of buried H3 residues does not correlate with evolutionary conservation. Given that these H3 residues are not post-translationally modified and only regulate H3-H3 and H3-H4 stabilizing interactions, our data imply an unknown function responsible for driving conservation of these buried H3 residues.
Most proteins fold to a well-defined, three-dimensional structure, which can be delineated into the protein surface and its buried core. When comparing amino acid sequences of the same protein from different organisms, we would expect to find certain residue positions conserved due to the importance of that position in either maintaining the protein's function or its three-dimensional structure. In this study, we looked at residues in the buried core domains of histone proteins H3 and H4, which have no known function other than maintaining the three-dimensional structure of the protein. We find that perturbing protein stability (which is a measure of maintenance of the protein's structure) by mutating these residues compromises survival fitness in yeast. However, the stability conferred by buried amino acids of H3 alone cannot account for their evolutionary conservation, which is in striking contrast to other proteins where stability has been shown to be the driving force for sequence conservation. This conservation of H3 thus points to either new additional functions of H3 that have not been uncovered or a unique conservation mechanism that goes beyond survival pressure. These data therefore reveal a highly conserved domain that is distinct in its evolutionary conservation.
Studies of cellular and tissue dynamics benefit greatly from tools that can control protein activity with specificity and precise timing in living systems. We describe here a new approach to confer allosteric regulation specifically on the catalytic activity of kinases. A highly conserved portion of the kinase catalytic domain is modified with a small protein insert that inactivates catalytic activity, but does not affect other protein interactions. Catalytic activity is restored by addition of rapamycin or non-immunosuppresive analogs (Fig. 1A). We demonstrate the approach by specifically activating focal adhesion kinase (FAK) within minutes in living cells, thereby demonstrating a novel role for FAK in regulation of membrane dynamics. Molecular modeling and mutagenesis indicate that the protein insert reduces activity by increasing the flexibility of the catalytic domain. Drug binding restores activity by increasing rigidity. Successful regulation of Src and p38 suggest that modification of this highly conserved site will be applicable to other kinases.
Mammalian airways are protected from infection by a thin film of airway surface liquid (ASL) which covers airway epithelial surfaces and acts as a lubricant to keep mucus from adhering to the epithelial surface. Precise regulation of ASL volume is essential for efficient mucus clearance and too great a reduction in ASL volume causes mucus dehydration and mucus stasis which contributes to chronic airway infection. The epithelial Na+ channel (ENaC) is the rate-limiting step that governs Na+ absorption in the airways. Recent in vitro and in vivo data have demonstrated that ENaC is a critical determinant of ASL volume and hence mucus clearance. ENaC must be cleaved by either intracellular furin-type proteases or extracellular serine proteases to be active and conduct Na+, and this process can be inhibited by protease inhibitors. ENaC can be regulated by multiple pathways, and once proteolytically cleaved ENaC may then be inhibited by intracellular second messengers such as cAMP and PIP2. In the airways, however, regulation of ENaC by proteases seems to be the predominant mode of regulation since knockdown of either endogenous serine proteases such as prostasin, or inhibitors of ENaC proteolysis such as SPLUNC1, has large effects on ENaC activity in airway epithelia. In this review, we shall discuss how ENaC is proteolytically cleaved, how this process can regulate ASL volume, and how its failure to operate correctly may contribute to chronic airway disease.
Na+ channel; Cystic fibrosis; Epithelial transport; Fluid absorption; Lung liquid
A didactic model is presented to illustrate how the effect of macromolecular crowding on protein folding and association is modeled using current analytical theory and discrete molecular dynamics. While analytical treatments of crowding may consider the effect as a potential of average force acting to compress a polypeptide chain into a compact state, the use of simulations enables the presence of crowding reagents to be treated explicitly. Using an analytically solvable toy model for protein folding, an approximate statistical thermodynamic method is directly compared to simulation in order to gauge the effectiveness of current analytical crowding descriptions. Both methodologies are in quantitative agreement under most conditions, indication that both current theory and simulation methods are capable of recapitulating aspects of protein folding even by utilizing a simplistic protein model.
Cytoplasmic dynein is an important motor that drives all minus-end directed movement along microtubules. Dynein is a complex motor whose processive motion is driven by ATP-hydrolysis. Dynein's run length has been measured to be several millimetres with typical velocities in the order of a few nanometres per second. Therefore, the average time between steps is a fraction of a second. When this time scale is compared with typical time scales for protein side chain and backbone movements (~10−9 s and ~10−5 s, respectively), it becomes clear that a multi-timescale modelling approach is required to understand energy transduction in this protein. Here, we review recent efforts to use computational and mathematical modelling to understand various aspects of dynein's chemomechanical cycle. First, we describe a structural model of dynein's motor unit showing a heptameric organization of the motor subunits. Second, we describe our molecular dynamics simulations of the motor unit that are used to investigate the dynamics of the various motor domains. Third, we present a kinetic model of the coordination between the two dynein heads. Lastly, we investigate the various potential geometries of the dimer during its hydrolytic and stepping cycle.
Polyglutamine (polyQ) expansion in exon1 (XN1) of the huntingtin protein is linked to Huntington's disease. When the number of glutamines exceeds a threshold of approximately 36–40 repeats, XN1 can readily form amyloid aggregates similar to those associated with disease. Many experiments suggest that misfolding of monomeric XN1 plays an important role in the length-dependent aggregation. Elucidating the misfolding of a XN1 monomer can help determine the molecular mechanism of XN1 aggregation and potentially help develop strategies to inhibit XN1 aggregation. The flanking sequences surrounding the polyQ region can play a critical role in determining the structural rearrangement and aggregation mechanism of XN1. Few experiments have studied XN1 in its entirety, with all flanking regions. To obtain structural insights into the misfolding of XN1 toward amyloid aggregation, we perform molecular dynamics simulations on monomeric XN1 with full flanking regions, a variant missing the polyproline regions, which are hypothesized to prevent aggregation, and an isolated polyQ peptide (Qn). For each of these three constructs, we study glutamine repeat lengths of 23, 36, 40 and 47. We find that polyQ peptides have a positive correlation between their probability to form a β-rich misfolded state and their expansion length. We also find that the flanking regions of XN1 affect its probability to^x_page_count=28 form a β-rich state compared to the isolated polyQ. Particularly, the polyproline regions form polyproline type II helices and decrease the probability of the polyQ region to form a β-rich state. Additionally, by lengthening polyQ, the first N-terminal 17 residues are more likely to adopt a β-sheet conformation rather than an α-helix conformation. Therefore, our molecular dynamics study provides a structural insight of XN1 misfolding and elucidates the possible role of the flanking sequences in XN1 aggregation.
Huntington's Disease is a neurodegenerative disorder associated with protein aggregation in neurons. The aggregates formed are thought to lead to neurotoxicity and cell death. Understanding the molecular structure of these aggregates may lead to strategies to inhibit aggregation. Exon 1 (XN1) of the huntingtin protein is critical for aggregate formation. This polypeptide has a naturally occurring polyglutamine sequence (polyQ), which is elongated in patients afflicted with the disease. The polyQ region in XN1 has several flanking sequences with distinct physicochemical properties, including the N-terminal 17 residues, two polyproline regions, and C-terminal sequences, that may affect its overall structure and aggregation. What is the overall structure of XN1, and what structural effects do the neighboring sequences have on each other and polyQ? We address these questions by studying computational models of various polypeptides, including XN1 and three mutant forms associated with Huntington's Disease. Certain neighboring sequences are found to inhibit aggregation, while others may be recruited by polyQ to form aggregates. Our results suggest the role that the flanking sequences may play in XN1 aggregation and may subsequently guide future structural models of XN1 aggregation.
The difficulty of analyzing higher order RNA structure, especially for folding intermediates and for RNAs whose functions require domains that are conformationally flexible, emphasizes the need for new approaches for modeling RNA tertiary structure accurately. Here, we report a concise approach that makes use of facile RNA structure probing experiments that are then interpreted using a computational algorithm, carefully tailored to optimize both the resolution and refinement speed for the resulting structures, without requiring user intervention. The RNA secondary structure is first established using SHAPE chemistry. We then use a sequence-directed cleavage agent, that can be placed arbitrarily in many helical motifs, to obtain high quality inter-residue distances. We interpret this in-solution chemical information using a fast, coarse grained, discrete molecular dynamics engine in which each RNA nucleotide is represented by pseudoatoms for the phosphate, ribose and nucleobase groups. By this approach, we refine base paired positions in yeast tRNAAsp to 4 Å RMSD without any preexisting information or assumptions about secondary or tertiary structures. This blended experimental and computational approach has the potential to yield native-like models for the diverse universe of functionally important RNAs whose structures cannot be characterized by conventional structural methods.
We describe the proceedings and conclusions from a “Workshop on Applications of Protein Models in Biomedical Research” that was held at University of California at San Francisco on 11 and 12 July, 2008. At the workshop, international scientists involved with structure modeling explored (i) how models are currently used in biomedical research, (ii) what the requirements and challenges for different applications are, and (iii) how the interaction between the computational and experimental research communities could be strengthened to advance the field.
Summary: Three-dimensional RNA structure prediction and folding is of significant interest in the biological research community. Here, we present iFoldRNA, a novel web-based methodology for RNA structure prediction with near atomic resolution accuracy and analysis of RNA folding thermodynamics. iFoldRNA rapidly explores RNA conformations using discrete molecular dynamics simulations of input RNA sequences. Starting from simplified linear-chain conformations, RNA molecules (<50 nt) fold to native-like structures within half an hour of simulation, facilitating rapid RNA structure prediction. All-atom reconstruction of energetically stable conformations generates iFoldRNA predicted RNA structures. The predicted RNA structures are within 2–5 Å root mean squre deviations (RMSDs) from corresponding experimentally derived structures. RNA folding parameters including specific heat, contact maps, simulation trajectories, gyration radii, RMSDs from native state, fraction of native-like contacts are accessible from iFoldRNA. We expect iFoldRNA will serve as a useful resource for RNA structure prediction and folding thermodynamic analyses.
Supplementary information: Supplementary data are available at Bioinformatics online.
Protein aggregation is the phenomenon of protein self-association potentially leading to detrimental effects on physiology, which is closely related to numerous human diseases such as Alzheimer's and Parkinson's disease. Despite progress in understanding the mechanism of protein aggregation, how natural selection against protein aggregation acts on subunits of protein complexes and on proteins with different contributions to organism fitness remains largely unknown. Here, we perform a proteome-wide analysis by using an experimentally validated algorithm TANGO and utilizing sequence, interactomic and phenotype-based functional genomic data from yeast, fly, and nematode. We find that proteins that are capable of forming homooligomeric complex have lower aggregation propensity compared with proteins that do not function as homooligomer. Further, proteins that are essential to the fitness of an organism have lower aggregation propensity compared with nonessential ones. Our finding suggests that the selection force against protein aggregation acts across different hierarchies of biological system.
natural selection; protein aggregation; functional genomics; proteome; Drosophila melanogaster; Caenorhabditis elegans
Virtual screening is becoming an important tool for drug discovery. However, the application of virtual screening has been limited by the lack of accurate scoring functions. Here, we present a novel scoring function, MedusaScore, for evaluating protein-ligand binding. MedusaScore is based on models of physical interactions that include van der Waals, solvation and hydrogen bonding energies. To ensure the best transferability of the scoring function, we do not use any protein-ligand experimental data for parameter training. We then test the MedusaScore for docking decoy recognition and binding affinity prediction and find superior performance compared to other widely used scoring functions. Statistical analysis indicates that one source of inaccuracy of MedusaScore may arise from the unaccounted entropic loss upon ligand binding, which suggests avenues of approach for further MedusaScore improvement.
Computer generated trajectories can, in principle, reveal the folding pathways of a protein at atomic resolution and possibly suggest general and simple rules for predicting the folded structure of a given sequence. While such reversible folding trajectories can only be determined ab initio using all-atom transferable force-fields for a few small proteins, they can be determined for a large number of proteins using coarse-grained and structure-based force-fields, in which a known folded structure is by construction the absolute energy and free-energy minimum. Here we use a model of the fast folding helical λ-repressor protein to generate trajectories in which native and non-native states are in equilibrium and transitions are accurately sampled. Yet, representation of the free-energy surface, which underlies the thermodynamic and dynamic properties of the protein model, from such a trajectory remains a challenge. Projections over one or a small number of arbitrarily chosen progress variables often hide the most important features of such surfaces. The results unequivocally show that an unprojected representation of the free-energy surface provides important and unbiased information and allows a simple and meaningful description of many-dimensional, heterogeneous trajectories, providing new insight into the possible mechanisms of fast-folding proteins.
The process of protein folding is a complex transition from a disordered to an ordered state. Here, we simulate a specific fast-folding protein at the point at which the native and denatured states are at equilibrium and show that obtaining an accurate description of the mechanisms of folding and unfolding is far from trivial. Using simple quantities which quantify the degree of native order is, in the case of this protein, clearly misleading. We show that an unbiased representation of the free-energy surface can be obtained; using such a representation we are able to redesign the landscape and thus modify, upon site-specific “mutations”, the folding and unfolding rates. This leads us to formulate a hypothesis to explain the very fast folding of many proteins.
Discrete molecular dynamics (DMD) is a rapid sampling method used in protein folding and aggregation studies. Until now, DMD was used to perform simulations of simplified protein models in conjunction with structure-based force fields. Here, we develop an all-atom protein model and a transferable force field featuring packing, solvation, and environment-dependent hydrogen bond interactions. Using the replica exchange method, we perform folding simulations of six small proteins (20–60 residues) with distinct native structures. In all cases, native or near-native states are reached in simulations. For three small proteins, multiple folding transitions are observed and the computationally-characterized thermodynamics are in quantitative agreement with experiments. The predictive power of all-atom DMD highlights the importance of environment-dependent hydrogen bond interactions in modeling protein folding. The developed approach can be used for accurate and rapid sampling of conformational spaces of proteins and protein-protein complexes, and applied to protein engineering and design of protein-protein interactions.
ab initio protein folding; environment-dependent hydrogen bond; replica exchange; free energy landscape; conformational sampling
The Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a cAMP dependent chloride channel that is mutated in cystic fibrosis, an inherited disease of high morbidity and mortality. The phosphorylation of its ∼200 amino acid R domain by protein kinase A is obligatory for channel gating under normal conditions. The R domain contains more than ten PKA phosphorylation sites. No individual site is essential but phosphorylation of increasing numbers of sites enables progressively greater channel activity. In spite of numerous studies of the role of the R domain in CFTR regulation, its mechanism of action remains largely unknown. This is because neither its structure nor its interactions with other parts of CFTR have been completely elucidated. Studies have shown that the R domain lacks well-defined secondary structural elements and is an intrinsically disordered region of the channel protein. Here, we have analyzed the disorder pattern and employed computational methods to explore low energy conformations of the R domain. Specific disorder and secondary structure patterns detected suggest the presence of Molecular Recognition Elements (MoREs) that may mediate phosphorylation regulated intra- and inter-domain interactions. Simulations were performed to generate an ensemble of accessible R domain conformations. Although the calculated structures may represent more compact conformers than occur in vivo, their secondary structure propensities are consistent with predictions and published experimental data. Equilibrium simulations of a mimic of a phosphorylated R domain showed that it exhibited an increased radius of gyration. In one possible interpretation of these findings, by changing its size, the globally unstructured R domain may act as an entropic spring to perturb the packing of membrane-spanning sequences that constitute the ion permeability pathway and thereby activate channel gating.
CFTR; R domain; phosphorylation; disordered protein; molecular dynamics
Ryanodine receptors (RyRs) are ion channels that regulate muscle contraction by releasing calcium ions from intracellular stores into the cytoplasm. Mutations in skeletal muscle RyR (RyR1) give rise to congenital diseases such as central core disease. The absence of high-resolution structures of RyR1 has limited our understanding of channel function and disease mechanisms at the molecular level. Here, we report a structural model of the pore-forming region of RyR1. Molecular dynamics simulations show high ion binding to putative pore residues D4899, E4900, D4938, and D4945, which are experimentally known to be critical for channel conductance and selectivity. We also observe preferential localization of Ca2+ over K+ in the selectivity filter of RyR1. Simulations of RyR1-D4899Q mutant show a loss of preference to Ca2+ in the selectivity filter as seen experimentally. Electrophysiological experiments on a central core disease mutant, RyR1-G4898R, show constitutively open channels that conduct K+ but not Ca2+. Our simulations with G4898R likewise show a decrease in the preference of Ca2+ over K+ in the selectivity filter. Together, the computational and experimental results shed light on ion conductance and selectivity of RyR1 at an atomistic level.
Ryanodine receptors (RyRs) are ion channels present in the membranes of an intracellular calcium storage organelle, the sarcoplasmic reticulum. Nerve impulse triggers the opening of RyR channels, thus increasing the cytoplasmic calcium levels, which subsequently leads to muscle contraction. Congenital mutations in a specific type of RyR that is present in skeletal muscles, RyR1, lead to central core disease (CCD), which leads to weakened muscle. RyR1 mutations also render patients to be highly susceptible to malignant hyperthermia, an adverse reaction to general anesthesia. Although it is generally known that CCD mutations abort RyR1 function, the molecular basis of RyR1 dysfunction remains largely unknown because of the lack of atomic-level structure. Here, we present a structural model of the RyR1 pore region, where many of the CCD mutations are located. Molecular dynamics simulations of the pore region confirm the positions of residues experimentally known to be relevant for function. Furthermore, electrophysiological experiments and simulations shed light on the loss of function of CCD mutant channels. The combined theoretical and experimental studies on RyR1 elucidate the ion conduction pathway of RyR1 and a potential molecular origin of muscle diseases.
The differences in efficacy and molecular mechanisms of platinum based anti-cancer drugs cisplatin (CP) and oxaliplatin (OX) have been hypothesized to be in part due to the differential binding affinity of cellular and damage recognition proteins to CP and OX adducts formed on adjacent guanines in genomic DNA. HMGB1a in particular exhibits higher binding affinity to CP-GG adducts, and the extent of discrimination between CP- and OX-GG adducts is dependent on the bases flanking the adducts. However, the structural basis for this differential binding is not known. Here, we show that the conformational dynamics of CP- and OX-GG adducts are distinct and depend on the sequence context of the adduct. Molecular dynamics simulations of the Pt-GG adducts in the TGGA sequence context revealed that even though the major conformations of CP- and OX-GG adducts were similar, the minor conformations were distinct. Using the pattern of hydrogen bond formation between the Pt–ammines and the adjacent DNA bases, we identified the major and minor conformations sampled by Pt–DNA. We found that the minor conformations sampled exclusively by the CP-GG adduct exhibit structural properties that favor binding by HMGB1a, which may explain its higher binding affinity to CP-GG adducts, while these conformations are not sampled by OX-GG adducts because of the constraints imposed by its cyclohexane ring, which may explain the negligible binding affinity of HMGB1a for OX-GG adducts in the TGGA sequence context. Based on these results, we postulate that the constraints imposed by the cyclohexane ring of OX affect the DNA conformations explored by OX-GG adduct compared to those of CP-GG adduct, which may influence the binding affinities of HMG-domain proteins for Pt-GG adducts, and that these conformations are further influenced by the DNA sequence context of the Pt-GG adduct.
The skeletal muscle (RyR1) and cardiac muscle (RyR2) ryanodine receptor calcium release channels contain a single, conserved calmodulin (CaM) binding domain, yet are differentially regulated by CaM. Here, we report that high-affinity [35S]CaM binding to RyR1 is driven by favorable enthalpic and entropic contributions at Ca2+ concentrations from <0.01 to 100 μM. At 0.15 μM Ca2+, [35S]CaM bound to RyR2 with decreased affinity and binding enthalpy compared with RyR1. The rates of [35S]CaM dissociation from RyR1 increased as the temperature was raised, whereas at 0.15 μM Ca2+ the rate from RyR2 was little affected. The results suggest major differences in the energetics of CaM binding to and dissociation from RyR1 and RyR2.
Ca2+ release channel; sarcoplasmic reticulum; ryanodine receptor; binding enthalpy; binding entropy
Mismatch repair proteins, DNA damage-recognition proteins and translesion DNA polymerases discriminate between Pt-GG adducts containing cis-diammine ligands (formed by cisplatin (CP) and carboplatin) and trans-RR-diaminocyclohexane ligands (formed by oxaliplatin (OX)) and this discrimination is thought to be important in determining differences in the efficacy, toxicity and mutagenicity of these platinum anticancer agents. We have postulated that these proteins recognize differences in conformation and/or conformational dynamics of the DNA containing the adducts. We have previously determined the NMR solution structure of OX-DNA, CP-DNA and undamaged duplex DNA in the 5'-d(CCTCAGGCCTCC)-3' sequence context and have shown the existence of several conformational differences in the vicinity of the Pt-GG adduct. In this study we have used molecular dynamics simulations to explore differences in the conformational dynamics between OX-DNA, CP-DNA and undamaged DNA in the same sequence context. Twenty-five 10 ns unrestrained fully solvated molecular dynamics simulations were performed starting from two different DNA conformations using AMBER v8.0. All twenty-five simulations reached equilibrium within 4 ns, were independent of the starting structure and were in close agreement with previous crystal and NMR structures. Our data show that the cis-diammine (CP) ligand preferentially forms hydrogen bonds on the 5' side of the Pt-GG adduct, while the trans-RR-diaminocyclohexane (OX) ligand preferentially forms hydrogen bonds on the 3' side of the adduct. In addition, our data show that these differences in hydrogen bond formation are strongly correlated with differences in conformational dynamics, specifically the fraction of time spent in different DNA conformations in the vicinity of the adduct, for CP- and OX-DNA adducts. We postulate that differential recognition of CP- and OX-GG adducts by mismatch repair proteins, DNA damage-recognition proteins and DNA polymerases may be due, in part, to differences in the fraction of time that the adducts spend in a conformation favorable for protein binding.
Cisplatin; Oxaliplatin; Molecular Dynamics; Simulations; Conformation