22 research outputs found

    Efficient Conformational Sampling in Explicit Solvent Using a Hybrid Replica Exchange Molecular Dynamics Method

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    Temperature-based replica-exchange molecular dynamics (REMD), in which multiple simultaneous simulations, or replicas, are run at a range of temperatures, has become increasingly popular for exploring the energy landscape of biomolecular systems. The practical application of REMD toward systems of biomedical interest is often limited by the rapidly increasing number of replicas needed to model systems of larger size. Continuum solvent models, which replace the explicit modeling of solvent molecules with a mean-field approximation of solvation, decrease system size and correspondingly, the number of replicas, but can sometimes produce distortions of the free energy landscape. We present a hybrid implicit/explicit solvent REMD method in CHARMM in which replicas run in a purely explicit solvent regime while exchanges are implemented with a high-density GBMV2 implicit solvation model. Such a hybrid approach may be able to decrease the number of replicas needed to model larger systems while maintaining the accuracy of explicit solvent simulations. Toward that end, we run REMD using implicit solvent, explicit solvent, and our hybrid method, on three model systems: alanine dipeptide, a zwitterionic tetra-peptide, and a 10-residue β-hairpin peptide. We compare free energy landscape in each system derived from a variety of metrics including dihedral torsion angles, salt-bridge distance, and folding stability, and perform clustering to characterize the resulting structural ensembles. Our results identify discrepancies in the free-energy landscape between implicit and explicit solvent and evaluate the capability of the hybrid approach to decrease the number of replicas needed for REMD while reproducing the energy landscape of explicit solvent simulations

    Dengue virus antibody database: Systematically linking serotype-specificity with epitope mapping in dengue virus

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    <div><p>Background</p><p>A majority infections caused by dengue virus (DENV) are asymptomatic, but a higher incidence of severe illness, such as dengue hemorrhagic fever, is associated with secondary infections, suggesting that pre-existing immunity plays a central role in dengue pathogenesis. Primary infections are typically associated with a largely serotype-specific antibody response, while secondary infections show a shift to a broadly cross-reactive antibody response.</p><p>Methods/Principal findings</p><p>We hypothesized that the basis for the shift in serotype-specificity between primary and secondary infections can be found in a change in the antibody fine-specificity. To investigate the link between epitope- and serotype-specificity, we assembled the Dengue Virus Antibody Database, an online repository containing over 400 DENV-specific mAbs, each annotated with information on <i>1</i>) its origin, including the immunogen, host immune history, and selection methods, <i>2</i>) binding/neutralization data against all four DENV serotypes, and <i>3</i>) epitope mapping at the domain or residue level to the DENV E protein. We combined epitope mapping and activity information to determine a residue-level index of epitope propensity and cross-reactivity and generated detailed composite epitope maps of primary and secondary antibody responses. We found differing patterns of epitope-specificity between primary and secondary infections, where secondary responses target a distinct subset of epitopes found in the primary response. We found that secondary infections were marked with an enhanced response to cross-reactive epitopes, such as the fusion-loop and E-dimer region, as well as increased cross-reactivity in what are typically more serotype-specific epitope regions, such as the domain I-II interface and domain III.</p><p>Conclusions/Significance</p><p>Our results support the theory that pre-existing cross-reactive memory B cells form the basis for the secondary antibody response, resulting in a broadening of the response in terms of cross-reactivity, and a focusing of the response to a subset of epitopes, including some, such as the fusion-loop region, that are implicated in poor neutralization and antibody-dependent enhancement of infection.</p></div

    Differences in epitope fine-specificity between mouse and human.

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    <p>Mapping of epitope propensity on to the structure of the DENV E protein dimer for mAbs from mouse (A) and human (B). Spheres correspond to epitope residues. The size of the sphere corresponds to its epitope propensity, as described previously. C. Difference in epitope propensity between mouse and human across the E protein. Residue color corresponds to domain—purple, cyan, and magenta, for DI, DII, and DIII, respectively.</p

    QSAR Classification Model for Antibacterial Compounds and Its Use in Virtual Screening

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    As novel and drug-resistant bacterial strains continue to present an emerging health threat, the development of new antibacterial agents is critical. This includes making improvements to existing antibacterial scaffolds as well as identifying novel ones. The aim of this study is to apply a Bayesian classification QSAR approach to rapidly screen chemical libraries for compounds predicted to have antibacterial activity. Toward this end we assembled a data set of 317 known antibacterial compounds as well as a second data set of diverse, well-validated, non-antibacterial compounds from 215 PubChem Bioassays against various bacterial species. We constructed a Bayesian classification model using structural fingerprints and physicochemical property descriptors and achieved an accuracy of 84% and precision of 86% on an independent test set in identifying antibacterial compounds. To demonstrate the practical applicability of the model in virtual screening, we screened an independent data set of ∼200k compounds. The results show that the model can screen top hits of PubChem Bioassay actives with accuracy up to ∼76%, representing a 1.5–2-fold enrichment. The top screened hits represented a mixture of both known antibacterial scaffolds as well as novel scaffolds. Our study suggests that a well-validated Bayesian classification QSAR approach could compliment other screening approaches in identifying novel and promising hits. The data sets used in constructing and validating this model have been made publicly available

    Sequence variation and serotype specificity in DENV mAbs.

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    <p>A) Histogram of sequence conservation among DENV 1–4 E protein epitope residues for type-specific and complex mAbs from primary and secondary infections, compared with all residues in the E protein. B) Sequence variation between consensus sequences of DENV1-4, at the epitope level measured by the average <i>p</i><sub><i>epitope</i></sub> for type-specific mAbs from primary infection (<i>N</i> = 45), complex mAbs from primary infection (<i>N</i> = 24), and complex mAbs from secondary infections (<i>N</i> = 25). Only mAbs with epitope definitions consisting of at least five residues were considered. Error bars correspond to the standard deviation of <i>p</i><sub><i>epitope</i></sub> values. A statistically significant difference is indicated by ‘*’, corresponding to p < 0.001.</p

    Dengue virus antibody database.

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    <p>Each monoclonal antibody in the database is annotated with information about its origin and selection, activity against all four dengue serotypes, epitope mapping, and relevant references.</p

    Serotype-specificity and epitope mapping in primary and secondary dengue infections.

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    <p>A) Histograms of human mAbs in the primary (left) and secondary infection (right) data sets. B) Epitope propensity in primary (left) and secondary (right) infections across E protein amino acid sequence. Points are colored with respect to epitope cross-reactivity, or the proportion of mAbs that are associated with a given epitope residue that are classified as ‘complex’: red (>60%), orange (30–60%), or yellow (< 30%). C) A composite map of epitope propensity and cross-reactivity on the structure of the DENV E protein dimer for mAbs from primary (left) and secondary (right) infections. Epitope regions DI/DII interface, dimer interface, fusion loop, DII/DIII interface, and DIII-lateral ridge (LR) are highlighted. Spheres correspond to epitope residues. The size of the sphere corresponds to its epitope propensity: low propensity (<5%, small spheres), medium propensity (>5% and <10%; medium spheres), and high propensity (>10%, large spheres). The color of the sphere corresponds to the epitope cross-reactivity as described above.</p

    Representative structures for zanamivir (A, B and C) and oseltamivir (D, E and F) bound to WT and mutant NAs from the SRSM/HREX simulations.

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    <p>Salt-bridges and hydrogen bonds are depicted as magenta and orange dashed lines, respectively. Positively charged, negatively charged, and uncharged polar groups are noted as blue, red, and purple circles, respectively, and residues of interest are labeled. Mutated residues are underlined.</p

    Alchemical thermodynamic paths using SRMM (A) and SRSM (B) in the bound state between wild type (<i>wt</i>) and a mutant (<i>mut<sup>1</sup></i>).

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    <p>The paths (arrows) between end states (squares) going through nonphysical reference states (ovals) are shown. The SRMM uses a common reference state ‘hub’ for all mutations and ligands (RS<sup>1*2*3*</sup> L<sup>*</sup>); SRSM uses a mutation and ligand-specific reference state (RS<sup>1*</sup> L<sup>X</sup>). Decoupled residues and ligands are noted by an ‘*’. Thermodynamic paths in the unbound state have a similar form but without the ligand.</p
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