Matching Items (36)
Description
This thesis explores a wide array of topics related to the protein folding problem, ranging from the folding mechanism, ab initio structure prediction and protein design, to the mechanism of protein functional evolution, using multi-scale approaches. To investigate the role of native topology on folding mechanism, the native topology is dissected into non-local and local contacts. The number of non-local contacts and non-local contact orders are both negatively correlated with folding rates, suggesting that the non-local contacts dominate the barrier-crossing process. However, local contact orders show positive correlation with folding rates, indicating the role of a diffusive search in the denatured basin. Additionally, the folding rate distribution of E. coli and Yeast proteomes are predicted from native topology. The distribution is fitted well by a diffusion-drift population model and also directly compared with experimentally measured half life. The results indicate that proteome folding kinetics is limited by protein half life. The crucial role of local contacts in protein folding is further explored by the simulations of WW domains using Zipping and Assembly Method. The correct formation of N-terminal β-turn turns out important for the folding of WW domains. A classification model based on contact probabilities of five critical local contacts is constructed to predict the foldability of WW domains with 81% accuracy. By introducing mutations to stabilize those critical local contacts, a new protein design approach is developed to re-design the unfoldable WW domains and make them foldable. After folding, proteins exhibit inherent conformational dynamics to be functional. Using molecular dynamics simulations in conjunction with Perturbation Response Scanning, it is demonstrated that the divergence of functions can occur through the modification of conformational dynamics within existing fold for β-lactmases and GFP-like proteins: i) the modern TEM-1 lactamase shows a comparatively rigid active-site region, likely reflecting adaptation for efficient degradation of a specific substrate, while the resurrected ancient lactamases indicate enhanced active-site flexibility, which likely allows for the binding and subsequent degradation of different antibiotic molecules; ii) the chromophore and attached peptides of photocoversion-competent GFP-like protein exhibits higher flexibility than the photocoversion-incompetent one, consistent with the evolution of photocoversion capacity.
ContributorsZou, Taisong (Author) / Ozkan, Sefika B (Thesis advisor) / Thorpe, Michael F (Committee member) / Woodbury, Neal W (Committee member) / Vaiana, Sara M (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2014
Description
The continuous random network (CRN) model of network glasses is widely accepted as a model for materials such as vitreous silica and amorphous silicon. Although it
has been more than eighty years since the proposal of the CRN, there has not been conclusive experimental evidence of the structure of glasses and amorphous
materials. This has now changed with the advent of two-dimensional amorphous materials. Now, not only the distribution of rings but the actual atomic ring
structure can be imaged in real space, allowing for greater charicterization of these types of networks. This dissertation reports the first work done
on the modelling of amorphous graphene and vitreous silica bilayers. Models of amorphous graphene have been created using a Monte Carlo bond-switching method
and MD method. Vitreous silica bilayers have been constructed using models of amorphous graphene and the ring statistics of silica bilayers has been studied.
has been more than eighty years since the proposal of the CRN, there has not been conclusive experimental evidence of the structure of glasses and amorphous
materials. This has now changed with the advent of two-dimensional amorphous materials. Now, not only the distribution of rings but the actual atomic ring
structure can be imaged in real space, allowing for greater charicterization of these types of networks. This dissertation reports the first work done
on the modelling of amorphous graphene and vitreous silica bilayers. Models of amorphous graphene have been created using a Monte Carlo bond-switching method
and MD method. Vitreous silica bilayers have been constructed using models of amorphous graphene and the ring statistics of silica bilayers has been studied.
ContributorsKumar, Avishek (Author) / Thorpe, Michael F (Thesis advisor) / Ozkan, Sefika B (Committee member) / Beckstein, Oliver (Committee member) / Treacy, Michael Mj (Committee member) / Arizona State University (Publisher)
Created2014
Description
Proteins are a fundamental unit in biology. Although proteins have been extensively studied, there is still much to investigate. The mechanism by which proteins fold into their native state, how evolution shapes structural dynamics, and the dynamic mechanisms of many diseases are not well understood. In this thesis, protein folding is explored using a multi-scale modeling method including (i) geometric constraint based simulations that efficiently search for native like topologies and (ii) reservoir replica exchange molecular dynamics, which identify the low free energy structures and refines these structures toward the native conformation. A test set of eight proteins and three ancestral steroid receptor proteins are folded to 2.7Å all-atom RMSD from their experimental crystal structures. Protein evolution and disease associated mutations (DAMs) are most commonly studied by in silico multiple sequence alignment methods. Here, however, the structural dynamics are incorporated to give insight into the evolution of three ancestral proteins and the mechanism of several diseases in human ferritin protein. The differences in conformational dynamics of these evolutionary related, functionally diverged ancestral steroid receptor proteins are investigated by obtaining the most collective motion through essential dynamics. Strikingly, this analysis shows that evolutionary diverged proteins of the same family do not share the same dynamic subspace. Rather, those sharing the same function are simultaneously clustered together and distant from those functionally diverged homologs. This dynamics analysis also identifies 77% of mutations (functional and permissive) necessary to evolve new function. In silico methods for prediction of DAMs rely on differences in evolution rate due to purifying selection and therefore the accuracy of DAM prediction decreases at fast and slow evolvable sites. Here, we investigate structural dynamics through computing the contribution of each residue to the biologically relevant fluctuations and from this define a metric: the dynamic stability index (DSI). Using DSI we study the mechanism for three diseases observed in the human ferritin protein. The T30I and R40G DAMs show a loss of dynamic stability at the C-terminus helix and nearby regulatory loop, agreeing with experimental results implicating the same regulatory loop as a cause in cataracts syndrome.
ContributorsGlembo, Tyler J (Author) / Ozkan, Sefika B (Thesis advisor) / Thorpe, Michael F (Committee member) / Ros, Robert (Committee member) / Kumar, Sudhir (Committee member) / Shumway, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
Molecular dynamics simulations were used to study properties of water at the interface with nanometer-size solutes. We simulated nonpolar attractive Kihara cavities given by a Lennard-Jones potential shifted by a core radius. The dipolar response of the hydration layer to a uniform electric field substantially exceeds that of the bulk. For strongly attractive solutes, the collective dynamics of the hydration layer become slow compared to bulk water, as the solute size is increased. The statistics of electric field fluctuations at the solute center are Gaussian and tend toward the dielectric continuum limit with increasing solute size. A dipolar probe placed at the center of the solute is sensitive neither to the polarity excess nor to the slowed dynamics of the hydration layer. A point dipole was introduced close to the solute-water interface to further study the statistics of electric field fluctuations generated by the water. For small dipole magnitudes, the free energy surface is single-welled, with approximately Gaussian statistics. When the dipole is increased, the free energy surface becomes double-welled, before landing in an excited state, characterized again by a single-welled surface. The intermediate region is fairly broad and is characterized by electrostatic fluctuations significantly in excess of the prediction of linear response. We simulated a solute having the geometry of C180 fullerene, with dipoles introduced on each carbon. For small dipole moments, the solvent response follows the results seen for a single dipole; but for larger dipole magnitudes, the fluctuations of the solute-solvent energy pass through a second maximum. The juxtaposition of the two transitions leads to an approximately cubic scaling of the chemical potential with the dipole strengh. Umbrella sampling techniques were used to generate free energy surfaces of the electric potential fluctuations at the heme iron in Cytochrome B562. The results were unfortunately inconclusive, as the ionic background was not effectively represented in the finite-size system.
ContributorsFriesen, Allan Dwayne (Author) / Matyushov, Dmitry V (Thesis advisor) / Angell, C Austen (Thesis advisor) / Beckstein, Oliver (Committee member) / Mujica, Vladimiro (Committee member) / Arizona State University (Publisher)
Created2012
Description
Sample delivery is an essential component in biological imaging using serial diffraction from X-ray Free Electron Lasers (XFEL) and synchrotrons. Recent developments have made possible the near-atomic resolution structure determination of several important proteins, including one G protein-coupled receptor (GPCR) drug target, whose structure could not easily have been determined otherwise (Appendix A). In this thesis I describe new sample delivery developments that are paramount to advancing this field beyond what has been accomplished to date. Soft Lithography was used to implement sample conservation in the Gas Dynamic Virtual Nozzle (GDVN). A PDMS/glass composite microfluidic injector was created and given the capability of millisecond fluidic switching of a GDVN liquid jet within the divergent section of a 2D Laval-like GDVN nozzle, providing a means of collecting sample between the pulses of current XFELs. An oil/water droplet immersion jet was prototyped that suspends small sample droplets within an oil jet such that the sample droplet frequency may match the XFEL pulse repetition rate. A similar device was designed to use gas bubbles for synchronized “on/off” jet behavior and for active micromixing. 3D printing based on 2-Photon Polymerization (2PP) was used to directly fabricate reproducible GDVN injectors at high resolution, introducing the possibility of systematic nozzle research and highly complex GDVN injectors. Viscous sample delivery using the “LCP injector” was improved with a method for dealing with poorly extruding sample mediums when using full beam transmission from the Linac Coherent Light Source (LCLS), and a new viscous crystal-carrying medium was characterized for use in both vacuum and atmospheric environments: high molecular weight Polyethylene Glycol.
ContributorsNelson, Garrett Charles (Author) / Spence, John C (Thesis advisor) / Weierstall, Uwe J (Thesis advisor) / Schmidt, Kevin E (Committee member) / Beckstein, Oliver (Committee member) / Arizona State University (Publisher)
Created2015
Description
Molecular docking serves as an important tool in modeling protein-ligand interactions. Most of the docking approaches treat the protein receptor as rigid and move the ligand in the binding pocket through an energy minimization, which is an incorrect approach as proteins are flexible and undergo conformational changes upon ligand binding. However, modeling receptor backbone flexibility in docking is challenging and computationally expensive due to the large conformational space that needs to be sampled.
A novel flexible docking approach called BP-Dock (Backbone Perturbation docking) was developed to overcome this challenge. BP-Dock integrates both backbone and side chain conformational changes of a protein through a multi-scale approach. In BP-Dock, the residues along a protein chain are perturbed mimicking the binding induced event, with a small Brownian kick, one at a time. The fluctuation response profile of the chain upon these perturbations is computed by Perturbation Response Scanning (PRS) to generate multiple receptor conformations for ensemble docking. To evaluate the performance of BP-Dock, this approach was applied to a large and diverse dataset of unbound structures as receptors. Furthermore, the protein-peptide docking of PICK1-PDZ proteins was investigated. This study elucidates the determinants of PICK1-PDZ binding that plays crucial roles in numerous neurodegenerative disorders. BP-Dock approach was also extended to the challenging problem of protein-glycan docking and applied to analyze the energetics of glycan recognition in Cyanovirin-N (CVN), a cyanobacterial lectin that inhibits HIV by binding to its highly glycosylated envelope protein gp120. This study provide the energetic contribution of the individual residues lining the binding pocket of CVN and explore the effect of structural flexibility in the hinge region of CVN on glycan binding, which are also verified experimentally. Overall, these successful applications of BP-Dock highlight the importance of modeling backbone flexibility in docking that can have important implications in defining the binding properties of protein-ligand interactions.
Finally, an induced fit docking approach called Adaptive BP-Dock is presented that allows both protein and ligand conformational sampling during the docking. Adaptive BP-Dock can provide a faster and efficient docking approach for the virtual screening of novel targets for rational drug design and aid our understanding of protein-ligand interactions.
A novel flexible docking approach called BP-Dock (Backbone Perturbation docking) was developed to overcome this challenge. BP-Dock integrates both backbone and side chain conformational changes of a protein through a multi-scale approach. In BP-Dock, the residues along a protein chain are perturbed mimicking the binding induced event, with a small Brownian kick, one at a time. The fluctuation response profile of the chain upon these perturbations is computed by Perturbation Response Scanning (PRS) to generate multiple receptor conformations for ensemble docking. To evaluate the performance of BP-Dock, this approach was applied to a large and diverse dataset of unbound structures as receptors. Furthermore, the protein-peptide docking of PICK1-PDZ proteins was investigated. This study elucidates the determinants of PICK1-PDZ binding that plays crucial roles in numerous neurodegenerative disorders. BP-Dock approach was also extended to the challenging problem of protein-glycan docking and applied to analyze the energetics of glycan recognition in Cyanovirin-N (CVN), a cyanobacterial lectin that inhibits HIV by binding to its highly glycosylated envelope protein gp120. This study provide the energetic contribution of the individual residues lining the binding pocket of CVN and explore the effect of structural flexibility in the hinge region of CVN on glycan binding, which are also verified experimentally. Overall, these successful applications of BP-Dock highlight the importance of modeling backbone flexibility in docking that can have important implications in defining the binding properties of protein-ligand interactions.
Finally, an induced fit docking approach called Adaptive BP-Dock is presented that allows both protein and ligand conformational sampling during the docking. Adaptive BP-Dock can provide a faster and efficient docking approach for the virtual screening of novel targets for rational drug design and aid our understanding of protein-ligand interactions.
ContributorsBolia, Ashini (Author) / Ozkan, Sefika Banu (Thesis advisor) / Ghirlanda, Giovanna (Thesis advisor) / Beckstein, Oliver (Committee member) / Wachter, Rebekka (Committee member) / Arizona State University (Publisher)
Created2015
Description
In a typical living cell, millions to billions of proteins—nanomachines that fluctuate and cycle among many conformational states—convert available free energy into mechanochemical work. A fundamental goal of biophysics is to ascertain how 3D protein structures encode specific functions, such as catalyzing chemical reactions or transporting nutrients into a cell. Protein dynamics span femtosecond timescales (i.e., covalent bond oscillations) to large conformational transition timescales in, and beyond, the millisecond regime (e.g., glucose transport across a phospholipid bilayer). Actual transition events are fast but rare, occurring orders of magnitude faster than typical metastable equilibrium waiting times. Equilibrium molecular dynamics (EqMD) can capture atomistic detail and solute-solvent interactions, but even microseconds of sampling attainable nowadays still falls orders of magnitude short of transition timescales, especially for large systems, rendering observations of such "rare events" difficult or effectively impossible.
Advanced path-sampling methods exploit reduced physical models or biasing to produce plausible transitions while balancing accuracy and efficiency, but quantifying their accuracy relative to other numerical and experimental data has been challenging. Indeed, new horizons in elucidating protein function necessitate that present methodologies be revised to more seamlessly and quantitatively integrate a spectrum of methods, both numerical and experimental. In this dissertation, experimental and computational methods are put into perspective using the enzyme adenylate kinase (AdK) as an illustrative example. We introduce Path Similarity Analysis (PSA)—an integrative computational framework developed to quantify transition path similarity. PSA not only reliably distinguished AdK transitions by the originating method, but also traced pathway differences between two methods back to charge-charge interactions (neglected by the stereochemical model, but not the all-atom force field) in several conserved salt bridges. Cryo-electron microscopy maps of the transporter Bor1p are directly incorporated into EqMD simulations using MD flexible fitting to produce viable structural models and infer a plausible transport mechanism. Conforming to the theme of integration, a short compendium of an exploratory project—developing a hybrid atomistic-continuum method—is presented, including initial results and a novel fluctuating hydrodynamics model and corresponding numerical code.
Advanced path-sampling methods exploit reduced physical models or biasing to produce plausible transitions while balancing accuracy and efficiency, but quantifying their accuracy relative to other numerical and experimental data has been challenging. Indeed, new horizons in elucidating protein function necessitate that present methodologies be revised to more seamlessly and quantitatively integrate a spectrum of methods, both numerical and experimental. In this dissertation, experimental and computational methods are put into perspective using the enzyme adenylate kinase (AdK) as an illustrative example. We introduce Path Similarity Analysis (PSA)—an integrative computational framework developed to quantify transition path similarity. PSA not only reliably distinguished AdK transitions by the originating method, but also traced pathway differences between two methods back to charge-charge interactions (neglected by the stereochemical model, but not the all-atom force field) in several conserved salt bridges. Cryo-electron microscopy maps of the transporter Bor1p are directly incorporated into EqMD simulations using MD flexible fitting to produce viable structural models and infer a plausible transport mechanism. Conforming to the theme of integration, a short compendium of an exploratory project—developing a hybrid atomistic-continuum method—is presented, including initial results and a novel fluctuating hydrodynamics model and corresponding numerical code.
ContributorsSeyler, Sean L (Author) / Beckstein, Oliver (Thesis advisor) / Chamberlin, Ralph (Committee member) / Matyushov, Dmitry (Committee member) / Thorpe, Michael F (Committee member) / Vaiana, Sara (Committee member) / Arizona State University (Publisher)
Created2017
Description
In this dissertation two kinds of strongly interacting fermionic systems were studied: cold atomic gases and nucleon systems. In the first part I report T=0 diffusion Monte Carlo results for the ground-state and vortex excitation of unpolarized spin-1/2 fermions in a two-dimensional disk. I investigate how vortex core structure properties behave over the BEC-BCS crossover. The vortex excitation energy, density profiles, and vortex core properties related to the current are calculated. A density suppression at the vortex core on the BCS side of the crossover and a depleted core on the BEC limit is found. Size-effect dependencies in the disk geometry were carefully studied. In the second part of this dissertation I turn my attention to a very interesting problem in nuclear physics. In most simulations of nonrelativistic nuclear systems, the wave functions are found by solving the many-body Schrödinger equations, and they describe the quantum-mechanical amplitudes of the nucleonic degrees of freedom. In those simulations the pionic contributions are encoded in nuclear potentials and electroweak currents, and they determine the low-momentum behavior. By contrast, in this work I present a novel quantum Monte Carlo formalism in which both relativistic pions and nonrelativistic nucleons are explicitly included in the quantum-mechanical states of the system. I report the renormalization of the nucleon mass as a function of the momentum cutoff, an Euclidean time density correlation function that deals with the short-time nucleon diffusion, and the pion cloud density and momentum distributions. In the two nucleon sector the interaction of two static nucleons at large distances reduces to the one-pion exchange potential, and I fit the low-energy constants of the contact interactions to reproduce the binding energy of the deuteron and two neutrons in finite volumes. I conclude by showing that the method can be readily applied to light-nuclei.
ContributorsMadeira, Lucas (Author) / Schmidt, Kevin E (Thesis advisor) / Alarcon, Ricardo (Committee member) / Beckstein, Oliver (Committee member) / Erten, Onur (Committee member) / Arizona State University (Publisher)
Created2018
Description
The structure of glass has been the subject of many studies, however some
details remained to be resolved. With the advancement of microscopic
imaging techniques and the successful synthesis of two-dimensional materials,
images of two-dimensional glasses (bilayers of silica) are now available,
confirming that this glass structure closely follows the continuous random
network model. These images provide complete in-plane structural information
such as ring correlations, and intermediate range order and with computer
refinement contain indirect information such as angular distributions, and
tilting.
This dissertation reports the first work that integrates the actual atomic
coordinates obtained from such images with structural refinement to enhance
the extracted information from the experimental data.
The correlations in the ring structure of silica bilayers are studied
and it is shown that short-range and intermediate-range order exist in such networks.
Special boundary conditions for finite experimental samples are designed so atoms
in the bulk sense they are part of an infinite network.
It is shown that bilayers consist of two identical layers separated by a
symmetry plane and the tilted tetrahedra, two examples of
added value through the structural refinement.
Finally, the low-temperature properties of glasses in two dimensions
are studied. This dissertation presents a new approach to find possible
two-level systems in silica bilayers employing the tools of rigidity theory
in isostatic systems.
details remained to be resolved. With the advancement of microscopic
imaging techniques and the successful synthesis of two-dimensional materials,
images of two-dimensional glasses (bilayers of silica) are now available,
confirming that this glass structure closely follows the continuous random
network model. These images provide complete in-plane structural information
such as ring correlations, and intermediate range order and with computer
refinement contain indirect information such as angular distributions, and
tilting.
This dissertation reports the first work that integrates the actual atomic
coordinates obtained from such images with structural refinement to enhance
the extracted information from the experimental data.
The correlations in the ring structure of silica bilayers are studied
and it is shown that short-range and intermediate-range order exist in such networks.
Special boundary conditions for finite experimental samples are designed so atoms
in the bulk sense they are part of an infinite network.
It is shown that bilayers consist of two identical layers separated by a
symmetry plane and the tilted tetrahedra, two examples of
added value through the structural refinement.
Finally, the low-temperature properties of glasses in two dimensions
are studied. This dissertation presents a new approach to find possible
two-level systems in silica bilayers employing the tools of rigidity theory
in isostatic systems.
ContributorsSadjadi, Seyed Mahdi (Author) / Thorpe, Michael F (Thesis advisor) / Beckstein, Oliver (Committee member) / Schmidt, Kevin E (Committee member) / Treacy, Michael Mj (Committee member) / Arizona State University (Publisher)
Created2018
Description
The rigidity of a material is the property that enables it to preserve its structure when deformed. In a rigid body, no internal motion is possible since the degrees of freedom of the system are limited to translations and rotations only. In the macroscopic scale, the rigidity and response of a material to external load can be studied using continuum elasticity theory. But when it comes to the microscopic scale, a simple yet powerful approach is to model the structure of the material and its interparticle interactions as a ball$-$and$-$spring network. This model allows a full description of rigidity in terms of the vibrational modes and the balance between degrees of freedom and constraints in the system.
In the present work, we aim to establish a microscopic description of rigidity in \emph{disordered} networks. The studied networks can be designed to have a specific number of degrees of freedom and/or elastic properties. We first look into the rigidity transition in three types of networks including randomly diluted triangular networks, stress diluted triangular networks and jammed networks. It appears that the rigidity and linear response of these three types of systems are significantly different. In particular, jammed networks display higher levels of self-organization and a non-zero bulk modulus near the transition point. This is a unique set of properties that have not been observed in any other types of disordered networks. We incorporate these properties into a new definition of jamming that requires a network to hold one extra constraint in excess of isostaticity and have a finite non-zero bulk modulus. We then follow this definition by using a tuning by pruning algorithm to build spring networks that have both these properties and show that they behave exactly like jammed networks. We finally step into designing new disordered materials with desired elastic properties and show how disordered auxetic materials with a fully convex geometry can be produced.
In the present work, we aim to establish a microscopic description of rigidity in \emph{disordered} networks. The studied networks can be designed to have a specific number of degrees of freedom and/or elastic properties. We first look into the rigidity transition in three types of networks including randomly diluted triangular networks, stress diluted triangular networks and jammed networks. It appears that the rigidity and linear response of these three types of systems are significantly different. In particular, jammed networks display higher levels of self-organization and a non-zero bulk modulus near the transition point. This is a unique set of properties that have not been observed in any other types of disordered networks. We incorporate these properties into a new definition of jamming that requires a network to hold one extra constraint in excess of isostaticity and have a finite non-zero bulk modulus. We then follow this definition by using a tuning by pruning algorithm to build spring networks that have both these properties and show that they behave exactly like jammed networks. We finally step into designing new disordered materials with desired elastic properties and show how disordered auxetic materials with a fully convex geometry can be produced.
ContributorsFaghir Hagh, Varda (Author) / Thorpe, Michael F. (Thesis advisor) / Beckstein, Oliver (Committee member) / Chamberlin, Ralph V. (Committee member) / Schmidt, kevin E. (Committee member) / Arizona State University (Publisher)
Created2018