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- All Subjects: Computational Physics
- Creators: Beckstein, Oliver
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
Secondary active transporters play significant roles in maintaining living cells' homeostasis by utilizing the electrochemical gradient in driving ions or protons as the source of free energy to transport substrate through biological membranes.A broadly recognized molecular framework, the alternating access model, describes the transport mechanism as the transporter undergoes conformational changes between different conformations and alternatingly exposes its binding site to intracellular and extracellular sides and, thus, exchanges ion and substrate in a cyclical manner.
Recent progress in structural biology brought the first-ever structural insights into the mammalian Cation-Proton Antiporters (CPA) family of proteins.
However, the dynamic atomic-level information about the interactions between the newly discovered structures and the bound ion or the corresponding substrate remains unknown.
With Molecular Dynamics (MD), multiple spontaneous ion binding events were observed in the equilibrium simulations, revealing the binding site topology of Horse Sodium-Proton Exchanger 9 (NHE9) and Bison Sodium-Proton Antiporter 2 (NHA2) in their preferred protonation state.
Further investigation into more CPA homologs compared various aspects, including sequence identity, binding site topology, and energetic properties, and obtained general insights into the similarities shared by the binding process of CPA members.
The putative binding site and other conserved residues in their actively ion-bound poses were identified for each model, and their similarities were compared.
The energetic properties accessed by the three-dimensional free energy profile, initially found to be binding unfavorable for the experimental structures, were recalculated based on the simulation data. The updated results show consistency with the correct binding affinity as indicated by the experimental methods.
This work provided a general picture of the structures and the ion-protein interaction of CPA proteins and serves as comprehensive guidance for any related future structural and computational work.
ContributorsZhang, Chenou (Author) / Beckstein, Oliver (Thesis advisor) / Ozkan, Banu (Committee member) / Ros, Robert (Committee member) / Singharoy, Abhishek (Committee member) / Arizona State University (Publisher)
Created2023
Description
Traditionally, allostery is perceived as the response of a catalytic pocket to perturbations induced by binding at another distal site through the interaction network in a protein, usually associated with a conformational change responsible for functional regulation. Here, I utilize dynamics-based metrics, Dynamic Flexibility Index and Dynamic Coupling Index to provide insight into how 3D network of interactions wire communications within a protein and give rise to the long-range dynamic coupling, thus regulating key allosteric interactions. Furthermore, I investigate its role in modulating protein function through mutations in evolution. I use Thioredoxin and β-lactamase enzymes as model systems, and show that nature exploits "hinge-shift'' mechanism, where the loss in rigidity of certain residue positions of a protein is compensated by reduced flexibility of other positions, for functional evolution. I also developed a novel approach based on this principle to computationally engineer new mutants of the promiscuous ancestral β-lactamase (i.e., degrading both penicillin and cephatoxime) to exhibit specificity only towards penicillin with a better catalytic efficiency through population shift in its native ensemble.I investigate how allosteric interactions in a protein can regulate protein interactions in a cell, particularly focusing on E. coli ribosome. I describe how mutations in a ribosome can allosterically change its associating with magnesium ions, which was further shown by my collaborators to distally impact the number of biologically active Adenosine Triphosphate molecules in a cell, thereby, impacting cell growth. This allosteric modulation via magnesium ion concentrations is coined, "ionic allostery''. I also describe, the role played by allosteric interactions to regulate information among proteins using a simplistic toy model of an allosteric enzyme. It shows how allostery can provide a mechanism to efficiently transmit information in a signaling pathway in a cell while up/down regulating an enzyme’s activity.
The results discussed here suggest a deeper embedding of the role of allosteric interactions in a protein’s function at cellular level. Therefore, bridging the molecular impact of allosteric regulation with its role in communication in cellular signaling can provide further mechanistic insights of cellular function and disease development, and allow design of novel drugs regulating cellular functions.
The results discussed here suggest a deeper embedding of the role of allosteric interactions in a protein’s function at cellular level. Therefore, bridging the molecular impact of allosteric regulation with its role in communication in cellular signaling can provide further mechanistic insights of cellular function and disease development, and allow design of novel drugs regulating cellular functions.
ContributorsModi, Tushar (Author) / Ozkan, Sefika (Thesis advisor) / Beckstein, Oliver (Committee member) / Vaiana, Sara (Committee member) / Ros, Robert (Committee member) / Arizona State University (Publisher)
Created2020
Description
I describe the first continuous space nuclear path integral quantum Monte Carlo method, and calculate the ground state properties of light nuclei including Deuteron, Triton, Helium-3 and Helium-4, using both local chiral interaction up to next-to-next-to-leading-order and the Argonne $v_6'$ interaction. Compared with diffusion based quantum Monte Carlo methods such as Green's function Monte Carlo and auxiliary field diffusion Monte Carlo, path integral quantum Monte Carlo has the advantage that it can directly calculate the expectation value of operators without tradeoff, whether they commute with the Hamiltonian or not. For operators that commute with the Hamiltonian, e.g., the Hamiltonian itself, the path integral quantum Monte Carlo light-nuclei results agree with Green's function Monte Carlo and auxiliary field diffusion Monte Carlo results. For other operator expectations which are important to understand nuclear measurements but do not commute with the Hamiltonian and therefore cannot be accurately calculated by diffusion based quantum Monte Carlo methods without tradeoff, the path integral quantum Monte Carlo method gives reliable results. I show root-mean-square radii, one-particle number density distributions, and Euclidean response functions for single-nucleon couplings. I also systematically describe all the sampling algorithms used in this work, the strategies to make the computation efficient, the error estimations, and the details of the implementation of the code to perform calculations. This work can serve as a benchmark test for future calculations of larger nuclei or finite temperature nuclear matter using path integral quantum Monte Carlo.
ContributorsChen, Rong (Author) / Schmidt, Kevin E (Thesis advisor) / Alarcon, Ricardo O (Committee member) / Beckstein, Oliver (Committee member) / Comfort, Joseph R. (Committee member) / Shovkovy, Igor A. (Committee member) / Arizona State University (Publisher)
Created2020