Browsing by Author "Onuchic, Jose N."

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    Folding, Binding, Misfolding and Aggregation with AWSEM
    (2014-04-23) Schafer, Nicholas Peter; Wolynes, Peter G.; Onuchic, Jose N.; Clementi, Cecilia
    This thesis discusses our recent results using the Associative-memory, Water-mediated, Structure and Energy Model (AWSEM), an optimized, coarse-grained molecular dynamics protein folding model, to fold, bind, and predict the misfolding behavior of proteins. AWSEM is capable of performing de novo structure prediction on small alpha-helical protein domains and predict the binding interfaces of homo- and hetero-dimers. More recent work demonstrates how the misfolding behavior of tandem constructs in AWSEM is consistent with crucial aspects of ensemble and single molecule experiments on the aggregation and misfolding of these constructs. The first chapter is a review of the energy landscape theory of protein folding as it applies to the problem of protein structure prediction, and more specifically how energy landscape theory and the principle of minimal frustration can be used to optimize parameters of coarse-grained protein folding simulation models. The subsequent four chapters are reports of novel research performed with one such model.
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    Mesoscale Models for the Study of Emergent Behaviors Arising from Protein Interactions
    (2022-11-28) Bueno Basurco, Carlos Andres; Wolynes, Peter G.; Onuchic, Jose N.; Hazzard, Kaden R. A.
    Proteins are versatile biopolymers in living systems; they exhibit a great diversity of functions depending on the order in which their amino acids are arranged. Most protein functions, like mechanical or regulatory functions, only emerge from the interactions with other proteins and macromolecules. This dissertation describes how we have developed and adapted new computational models to investigate emergent structural and dynamic properties of protein interactions. Chapter 1 presents a review of the two systems of interest to be explored in successive chapters: the regulation of the actin cytoskeleton and the control of DNA transcription by the nuclear factor kappa B (NF-κB). It also introduces some models developed to study the interactions of proteins with actin filaments and with DNA. Chapters 2 and 3 focus on protein interactions in the actin cytoskeleton network. Chapter 2 describes how we have estimated the mechanical and dynamical properties of actin networks using polymer theory. We developed a simplified mathematical mean-field model of F-actin polymerization, cross-linking, and branching based on mass action kinetics. Then we obtained an analytical solution to the connectivity, rigidity, and force percolation transitions using a generalized version of the Flory-Stockmayer theory. Chapter 3 describes how we used a computational mechano-chemical model to simulate the conditions where the actin networks exhibit rare sudden movements. We show that actin networks containing Arp2/3 undergo sudden releases of strain known as “cytoquakes”. Chapters 4 and 5 focus on DNA-protein interactions. Chapter 4 describes a new implementation to simulate protein and DNA dynamics for large systems that we developed. This new procedure retains the accuracy of previous methods our group developed with a 30-fold speedup and eases the introduction of new potential energy terms. Chapter 5 describes how we used this protein a
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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.