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1.  Computational approaches to understanding protein aggregation in neurodegeneration 
The generation of toxic non-native protein conformers has emerged as a unifying thread among disorders such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Atomic-level detail regarding dynamical changes that facilitate protein aggregation, as well as the structural features of large-scale ordered aggregates and soluble non-native oligomers, would contribute significantly to current understanding of these complex phenomena and offer potential strategies for inhibiting formation of cytotoxic species. However, experimental limitations often preclude the acquisition of high-resolution structural and mechanistic information for aggregating systems. Computational methods, particularly those combine both all-atom and coarse-grained simulations to cover a wide range of time and length scales, have thus emerged as crucial tools for investigating protein aggregation. Here we review the current state of computational methodology for the study of protein self-assembly, with a focus on the application of these methods toward understanding of protein aggregates in human neurodegenerative disorders.
PMCID: PMC3995224  PMID: 24620031
protein aggregation; molecular dynamics; protein folding; neurodegeneration
2.  The budding yeast Centromere DNA Element II wraps a stable Cse4 hemisome in either orientation in vivo 
eLife  2014;3:e01861.
In budding yeast, a single cenH3 (Cse4) nucleosome occupies the ∼120-bp functional centromere, however conflicting structural models for the particle have been proposed. To resolve this controversy, we have applied H4S47C-anchored cleavage mapping, which reveals the precise position of histone H4 in every nucleosome in the genome. We find that cleavage patterns at centromeres are unique within the genome and are incompatible with symmetrical structures, including octameric nucleosomes and (Cse4/H4)2 tetrasomes. Centromere cleavage patterns are compatible with a precisely positioned core structure, one in which each of the 16 yeast centromeres is occupied by oppositely oriented Cse4/H4/H2A/H2B hemisomes in two rotational phases within the population. Centromere-specific hemisomes are also inferred from distances observed between closely-spaced H4 cleavages, as predicted from structural modeling. Our results indicate that the orientation and rotational position of the stable hemisome at each yeast centromere is not specified by the functional centromere sequence.
eLife digest
DNA is tightly packaged in cells for a variety of reasons—to allow it to fit inside the nucleus, to protect it from damage, and to help control the production of proteins from genes. The basic unit of packaged DNA is called a nucleosome, which consists of DNA wrapped around a structure formed by two pairs of four different proteins.
These proteins, which are called histones, have a role that extends beyond providing structural support for DNA. When cells divide, for example, pairs of ‘sister chromosomes’ are pulled apart to ensure that the two daughter cells both have the same chromosomes as the original cell. The sister chromosomes are pulled apart from a single position called a centromere, and the nucleosomes at this position contain a histone that is different from the histones found everywhere else in the cell. However, until recently it was not clear if the nucleosomes that contained these special cenH3 histones had the same structure as other nucleosomes.
Now Henikoff et al. have used a method called H4S47C-anchored cleavage mapping to study every nucleosome in the genome of the yeast S. cerevisiae. This mapping technique uses DNA sequencing to measure the precise distances between fixed points on the DNA in the nucleosome. Knowing these distances tells researchers a great deal about the number and position of the histones within each nucleosome in the genome.
Using this approach, Henikoff et al. found that nucleosomes at centromeres are different from other nucleosomes in histone number and arrangement. In particular, the nucleosome at each yeast centromere contains only one each of the four different histones in an asymmetrical orientation, in contrast to all other yeast nucleosomes, which contain two sets of four histones in a symmetrical arrangement. Furthermore, each nucleosome at a centromere can adopt one of two orientations: these orientations are mirror images of each other, and they occur with equal probability. It should also be possible to use the mapping technique developed by Henikoff et al. to study the larger and more complex centromeres found in other organisms, including humans.
PMCID: PMC3983907  PMID: 24737863
centromeres; nucleosome; chemical cleavage mapping; S. cerevisiae
3.  Statistical analysis of SHAPE-directed RNA secondary structure modeling 
Biochemistry  2013;52(4):596-599.
The ability to predict RNA secondary structure is fundamental for understanding and manipulating RNA function. The structural information obtained from selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) experiments greatly improves the accuracy of RNA secondary structure prediction. Recently, Das and colleagues [Kladwang et al., Biochemistry 50:8049 (2011)] proposed a “bootstrapping” approach to estimate the variance and helix-by-helix confidence levels of predicted secondary structures based on resampling (randomizing and summing) the measured SHAPE data. We show that the specific resampling approach described by Kladwang et al. introduces systematic errors and underestimates confidence in secondary structure prediction using SHAPE data. Instead, a leave-data-out jackknife approach better estimates the influence of a given experimental dataset on SHAPE-directed secondary structure modeling. Even when 35% of the data were left out in the jackknife approach, the confidence levels of SHAPE-directed secondary structure prediction were significantly higher than those calculated by Das and colleagues using bootstrapping. Helix confidence levels were thus significantly underestimated in the recent study, and resampling approach implemented by Kladwang et al. is not an appropriate metric for assigning confidences in SHAPE-directed secondary structure modeling.
PMCID: PMC3558531  PMID: 23286327
4.  DMD: An Efficient And Versatile Simulation Method For Fine Protein Characterization 
The Journal of Physical Chemistry. B  2012;116(29):8375-8382.
Until now it has been impractical to observe protein folding in silico for proteins larger than 50 residues. Limitations of both force field accuracy and computational efficiency make the folding problem very challenging. Here we employ discrete molecular dynamics (DMD) simulations with an all-atom force field to fold fast-folding proteins. We extend the DMD force field by introducing long-range electrostatic interactions to model salt-bridges and a sequence-dependent semi-empirical potential accounting for natural tendencies of certain amino acid sequences to form specific secondary structures. We enhance the computational performance by parallelizing the DMD algorithm. Using a small number of commodity computers, we achieve sampling quality and folding accuracy comparable to the explicit-solvent simulations performed on high-end hardware. We demonstrate that DMD can be used to observe equilibrium folding of villin headpiece and WW domain, study two-state folding kinetics and sample near-native states in ab initio folding of proteins of ~100 residues.
PMCID: PMC3406226  PMID: 22280505
Conformational dynamics; structure prediction; implicit solvent; parallel event-driven simulation
5.  Gaia: automated quality assessment of protein structure models 
Bioinformatics  2011;27(16):2209-2215.
Motivation: Increasing use of structural modeling for understanding structure–function relationships in proteins has led to the need to ensure that the protein models being used are of acceptable quality. Quality of a given protein structure can be assessed by comparing various intrinsic structural properties of the protein to those observed in high-resolution protein structures.
Results: In this study, we present tools to compare a given structure to high-resolution crystal structures. We assess packing by calculating the total void volume, the percentage of unsatisfied hydrogen bonds, the number of steric clashes and the scaling of the accessible surface area. We assess covalent geometry by determining bond lengths, angles, dihedrals and rotamers. The statistical parameters for the above measures, obtained from high-resolution crystal structures enable us to provide a quality-score that points to specific areas where a given protein structural model needs improvement.
Availability and Implementation: We provide these tools that appraise protein structures in the form of a web server Gaia ( Gaia evaluates the packing and covalent geometry of a given protein structure and provides quantitative comparison of the given structure to high-resolution crystal structures.
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3150034  PMID: 21700672
6.  Automated Minimization of Steric Clashes in Protein Structures 
Proteins  2011;79(1):261-270.
Molecular modeling of proteins including homology modeling, structure determination, and knowledge-based protein design requires tools to evaluate and refine three-dimensional protein structures. Steric clash is one of the artifacts prevalent in low-resolution structures and homology models. Steric clashes arise due to the unnatural overlap of any two non-bonding atoms in a protein structure. Usually, removal of severe steric clashes in some structures is challenging since many existing refinement programs do not accept structures with severe steric clashes. Here, we present a quantitative approach of identifying steric clashes in proteins by defining clashes based on the Van der Waals repulsion energy of the clashing atoms. We also define a metric for quantitative estimation of the severity of clashes in proteins by performing statistical analysis of clashes in high-resolution protein structures. We describe a rapid, automated and robust protocol, Chiron, which efficiently resolves severe clashes in low-resolution structures and homology models with minimal perturbation in the protein backbone. Benchmark studies highlight the efficiency and robustness of Chiron compared to other widely used methods. We provide Chiron as an automated web server to evaluate and resolve clashes in protein structures that can be further used for more accurate protein design.
PMCID: PMC3058769  PMID: 21058396
Homology modeling; refinement; Chiron; Discrete Molecular Dynamics; Protein Design
7.  Flanking Bases Influence the Nature of DNA Distortion by Platinum 1,2-Intrastrand (GG) Cross-Links 
PLoS ONE  2011;6(8):e23582.
The differences in efficacy and molecular mechanisms of platinum anti-cancer drugs cisplatin (CP) and oxaliplatin (OX) are thought to be partially due to the differences in the DNA conformations of the CP and OX adducts that form on adjacent guanines on DNA, which in turn influence the binding of damage-recognition proteins that control downstream effects of the adducts. Here we report a comprehensive comparison of the structural distortion of DNA caused by CP and OX adducts in the TGGT sequence context using nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) simulations. When compared to our previous studies in other sequence contexts, these structural studies help us understand the effect of the sequence context on the conformation of Pt-GG DNA adducts. We find that both the sequence context and the type of Pt-GG DNA adduct (CP vs. OX) play an important role in the conformation and the conformational dynamics of Pt-DNA adducts, possibly explaining their influence on the ability of many damage-recognition proteins to bind to Pt-DNA adducts.
PMCID: PMC3154474  PMID: 21853154
8.  Thermodynamic Stability of Histone H3 Is a Necessary but not Sufficient Driving Force for its Evolutionary Conservation 
PLoS Computational Biology  2011;7(1):e1001042.
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.
Author Summary
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.
PMCID: PMC3017104  PMID: 21253558
9.  A Structural Model of the Pore-Forming Region of the Skeletal Muscle Ryanodine Receptor (RyR1) 
PLoS Computational Biology  2009;5(4):e1000367.
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.
Author Summary
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.
PMCID: PMC2668181  PMID: 19390614
10.  Structural basis for the sequence-dependent effects of platinum–DNA adducts 
Nucleic Acids Research  2009;37(8):2434-2448.
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.
PMCID: PMC2677858  PMID: 19255091
11.  Thermodynamics of calmodulin binding to cardiac and skeletal muscle ryanodine receptor ion channels 
Proteins  2009;74(1):207-211.
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.
PMCID: PMC2605178  PMID: 18618700
Ca2+ release channel; sarcoplasmic reticulum; ryanodine receptor; binding enthalpy; binding entropy

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