Browsing by Author "Zheng, Wenwei"

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    A comparative analysis of clustering algorithms: O2 migration in truncated hemoglobin I from transition networks
    (AIP Publishing LLC., 2015) Cazade, Pierre-André; Zheng, Wenwei; Prada-Gracia, Diego; Berezovska, Ganna; Rao, Francesco; Clementi, Cecilia; Meuwly, Markus
    The ligand migration network for O2–diffusion in truncated Hemoglobin N is analyzed based on three different clustering schemes. For coordinate-based clustering, the conventional k–means and the kinetics-based Markov Clustering (MCL) methods are employed, whereas the locally scaled diffusion map (LSDMap) method is a collective-variable-based approach. It is found that all three methods agree well in their geometrical definition of the most important docking site, and all experimentally known docking sites are recovered by all three methods. Also, for most of the states, their population coincides quite favourably, whereas the kinetics of and between the states differs. One of the major differences between k–means and MCL clustering on the one hand and LSDMap on the other is that the latter finds one large primary cluster containing the Xe1a, IS1, and ENT states. This is related to the fact that the motion within the state occurs on similar time scales, whereas structurally the state is found to be quite diverse. In agreement with previous explicit atomistic simulations, the Xe3 pocket is found to be a highly dynamical site which points to its potential role as a hub in the network. This is also highlighted in the fact that LSDMap cannot identify this state. First passage time distributions from MCL clusterings using a one- (ligand-position) and two-dimensional (ligand-position and protein-structure) descriptor suggest that ligand- and protein-motions are coupled. The benefits and drawbacks of the three methods are discussed in a comparative fashion and highlight that depending on the questions at hand the best-performing method for a particular data set may differ.
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    Molecular recognition of DNA by ligands: Roughness and complexity of the free energy profile
    (American Institute of Physics, 2013) Zheng, Wenwei; Vargiu, Attilio Vittorio; Rohrdanz, Mary A.; Carloni, Paolo; Clementi, Cecilia
    Understanding the molecular mechanism by which probes and chemotherapeutic agents bind to nucleic acids is a fundamental issue in modern drug design. From a computational perspective, valuable insights are gained by the estimation of free energy landscapes as a function of some collective variables (CVs), which are associated with the molecular recognition event. Unfortunately the choice of CVs is highly non-trivial because of DNA's high flexibility and the presence of multiple association-dissociation events at different locations and/or sliding within the grooves. Here we have applied a modified version of Locally-Scaled Diffusion Map (LSDMap), a nonlinear dimensionality reduction technique for decoupling multiple-timescale dynamics in macromolecular systems, to a metadynamics-based free energy landscape calculated using a set of intuitive CVs. We investigated the binding of the organic drug anthramycin to a DNA 14-mer duplex. By performing an extensive set of metadynamics simulations, we observed sliding of anthramycin along the full-length DNA minor groove, as well as several detachments from multiple sites, including the one identified by X-ray crystallography. As in the case of equilibrium processes, the LSDMap analysis is able to extract the most relevant collective motions, which are associated with the slow processes within the system, i.e., ligand diffusion along the minor groove and dissociation from it. Thus, LSDMap in combination with metadynamics (and possibly every equivalent method) emerges as a powerful method to describe the energetics of ligand binding to DNA without resorting to intuitive ad hoc reaction coordinates.
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    Multiscale Analysis of Macromolecular Systems
    (2013-12-18) Zheng, Wenwei; Clementi, Cecilia; Kolomeisky, Anatoly B.; Pasquali, Matteo; Onuchic, Jose N.
    Molecular dynamics (MD) simulation serves as both a supplement to experiments and a predictive tool by revealing details inaccessible to current state-of-the-art experimental techniques. The relevant dynamics in complex macromolecular systems correspond to timescales longer than what can be sampled using MD with standard computational resources. In addition, even if Boltzmann-distributed sampling can be achieved, the definition of good reaction coordinates quantifying the progress of the reaction is non-trivial because of the high degrees of freedom of the system. My doctoral dissertation focuses on these two interrelated issues: the determination of good reaction coordinates and enhanced sampling techniques in the theoretical understanding of macromolecular systems. A new multiscale method, Locally Scaled Diffusion Map (LSDMap), has been introduced to extract the optimal collective reaction coordinates from MD data without a priori knowledge of the system. The method decouples motions with different timescales into a set of reaction coordinates, named diffusion coordinates (DCs). For systems with a seperation of timescales, the first few DCs are sufficient to characterize the slow processes of the system. Reaction rates computed along the 1st DC are in remarkable agreement with the rates measured directly from simulation. LSDMap has been applied to a number of systems, including Alanine Dipeptide, Alanine-12, polymer reversal inside a nanopore, Beta3s and DNA-Anthramycin binding. Based on LSDMap, a new enhanced sampling method, Diffusion Map-directed MD has been introduced by periodically calculating DCs on the fly and restarting the dynamics from the boundary along the 1st DC. The system is more likely to visit new regions of the configuration space instead of being trapped in a local minimum. In particular, the method achieves 3 orders of magnitude speedup over standard MD in the exploration of the configurational space of alanine-12 at 300K. The method is reaction coordinate free and minimally dependent on a priori knowledge of the system. Wide applicability of both LSDMap and its enhanced sampling extension is expected in larger systems, to the extent to allow a comparison with the experimental results, and to make predictions not yet accessible to experiment.
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    Multiscale Approach to the Determination of the Photoactive Yellow Protein Signaling State Ensemble
    (Public Library of Science, 2014) Rohrdanz, Mary A.; Zheng, Wenwei; Lambeth, Bradley; Vreede, Jocelyne; Clementi, Cecilia; Center for Theoretical Biological Physics
    The nature of the optical cycle of photoactive yellow protein (PYP) makes its elucidation challenging for both experiment and theory. The long transition times render conventional simulation methods ineffective, and yet the short signaling-state lifetime makes experimental data difficult to obtain and interpret. Here, through an innovative combination of computational methods, a prediction and analysis of the biological signaling state of PYP is presented. Coarse-grained modeling and locally scaled diffusion map are first used to obtain a rough bird's-eye view of the free energy landscape of photo-activated PYP. Then all-atom reconstruction, followed by an enhanced sampling scheme; diffusion map-directed-molecular dynamics are used to focus in on the signaling-state region of configuration space and obtain an ensemble of signaling state structures. To the best of our knowledge, this is the first time an all-atom reconstruction from a coarse grained model has been performed in a relatively unexplored region of molecular configuration space. We compare our signaling state prediction with previous computational and more recent experimental results, and the comparison is favorable, which validates the method presented. This approach provides additional insight to understand the PYP photo cycle, and can be applied to other systems for which more direct methods are impractical.
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    Rapid Exploration of Configuration Space with Diffusion-Map-Directed Molecular Dynamics
    (American Chemical Society, 2013) Zheng, Wenwei; Rohrdanz, Mary A.; Clementi, Cecilia
    The gap between the time scale of interesting behavior in macromolecular systems and that which our computational resources can afford often limits molecular dynamics (MD) from understanding experimental results and predicting what is inaccessible in experiments. In this paper, we introduce a new sampling scheme, named diffusion-map-directed MD (DM-d-MD), to rapidly explore molecular configuration space. The method uses a diffusion map to guide MD on the fly. DM-d-MD can be combined with other methods to reconstruct the equilibrium free energy, and here, we used umbrella sampling as an example. We present results from two systems: alanine dipeptide and alanine-12. In both systems, we gain tremendous speedup with respect to standard MD both in exploring the configuration space and reconstructing the equilibrium distribution. In particular, we obtain 3 orders of magnitude of speedup over standard MD in the exploration of the configurational space of alanine-12 at 300 K with DM-d-MD. The method is reaction coordinate free and minimally dependent on a priori knowledge of the system. We expect wide applications of DM-d-MD to other macromolecular systems in which equilibrium sampling is not affordable by standard MD.