Physics
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Item Non Traditional Solvent Effect On Protein Behavior(2022) Lee, Pei-Yin; Matysiak, Silvina; Chemical Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Protein preservation has been a long lasting research topic due to its importance in many bio-pharmaceutical applications. A ”cold chain” is a commonplace solution to protein preservation, which stores biochemical products at a refrigerated temperature. A big advantage of cold chain is that the storing process is straightforward, without many further processes before the use of stored bio-products. However, it can also experience malfunction of the cooling system and results in economic lost and health care crisis. Ionic liquids (ILs), as a type of non traditional solvents, consist only of ions and are reported to be a potential candidate to replace the use of cold chain. The advantages of ILs include low flammability, high conductivity and less toxicity compared to some organic solvents. The most interesting feature of ILs is their extremely large number of cation-anion combinations, that can be tailored for specific use according to different needs. This thesis aims to investigate specific mechanism behind how ILs modulate protein behavior, specifically, how ILs affect protein stability, activity, and aggregation. We approach the research questions through the lens of molecular dynamics (MD) simulations and complement with experimental findings. In the first part of the thesis we first investigate the effects of two imidazolium based ILs (1-ethyl-3-methylimidazolium ethylsulfate, [EMIM]+[EtSO4]− and 1-ethyl-3-methylimidazolium diethylphosphate, [EMIM]+[Et2PO4]−) on lysozyme stability and activity. We collaborate with an experiment group at the University of Massachusetts (Bermudez lab) to complement our simulation results. Both ILs are found to destabilize lysozyme stability. In addition, both the cation and anions lower the stability of lysozyme, but in a different fashion. [EMIM]+ interacts with an Arg-Trp-Arg bridge that is critical in lysozyme stability through π–π and cation–π interactions, leading to a local induced destabilization. On the other hand, both anions interact with the whole protein surface through short-range electrostatic interactions, with [Et2PO4]− having a stronger effect than [EtSO4]−. Lysozyme activity is also reduced by the presence of the two ILs, but can be recovered after rehydration. It is found that the protein-ligand complex is less stable in the presence of ILs. In addition, a dense cloud of [EMIM]+ is found in the vicinity of the lysozyme active site residues, possibly leading to a competition with the sugar ligand. A fast leaving of these [EMIM]+ is observed after rehydration, which explains the reappearance of the active site and the recover of lysozyme activity. Although classical all-atom MD simulations can provide us with a great deal of microscopic information, they are often limited by the temporal-spatial scale of the simulated systems. For example, systems with high viscosity solvents or systems involving large number of atoms will be difficult to reach convergence for all-atom MD. In this case, coarse grained (CG) MD can come into play to achieve the desired time- and length- scales. The faster sampling obtained from CG MD is achieved by reducing the degree of freedom of the system and by removing local energetic barriers. In CG MD, similar atoms are grouped to functional groups and thus the free energy landscape is smoothen. We develop a novel CG MD named ”Protein Model with Polarizability and Transferability (ProMPT)”. The novelty of this model is the inclusion of the charged dummies that can result in change of dipoles. These dipoles can reflect the change of environments and thus allow the model to respond to different environmental stimulus. We validate ProMPT with several benchmark proteins: Trp-cage, Trpzip4, villin, ww-domain, and β-α-β. ProMPT is able to simulate folding-unfolding and secondary structure transformation with minimal constraints, which is not feasible with previous CG models. In addition, ProMPT can also reproduce the experimental results for the dimerization of glycophorin A (GpA) with different point mutations. Here we demonstrate the ability of the model to capture the change of conformational space caused by point mutation. In the last part of this thesis, we combine ProMPT and an in-house CG IL model to study the effects of [TEA]+[Ms]− on amyloid beta 16-22 (Aβ16−22) aggregation. Aβ16−22 is the hydrophobic core region and is the smallest fragment of Aβ that can fibrilize. Aβ has been extensively linked to the pathogenesis of the Alzheimer’s disease. [TEA]+[Ms]− is reported to suppress the formation of β-sheets and induce helices at high concentration. From our results, both β-sheet content and the aggregate size decrease with the increase of IL concentration, which are in agreement with experiments. Aggregates can form in both water and IL, but with different morphologies. In water, a nice hydrophobic core involving Phe-Phe interactions can form as well as intact β-sheet contacts. In addition, a cross β-sandwich structure is also observed, as seen from previous literature. However, the same hydrophobic core can not persist in the presence of IL. Aggregate structures in IL are not stable over time due to the [TEA]+-Phe interaction. Helicity is also computed for Aβ16−22 in water and in IL at different concentrations and a positive correlation is found. The increase in helicity at high [TEA]+[Ms]− concentration can be explained by the reduction of the inter-peptide contacts, which then increases the opportunity for the peptides to form helical structures. Single peptide studies also reveal that [TEA]+[Ms]− increases the helicity, possibly through cation-induced dipole enhancement. In this thesis, a series of detailed investigations on the effects of ILs on protein behavior is performed. Specific interactions between IL functional groups and protein local/global structures are examined. The mechanisms we studied here will help constructing a holistic view for the design of IL-protein pair applications. The construction of the new CG protein/IL model provides another tool for the scientific community to study secondary structure transformation, folding- unfolding, and other biochemical processes that are sensitive to the environment with CG MD.Item Investigating biomolecular rare events with artificial intelligence augmented molecular dynamics(2022) Wang, Yihang; Tiwary, Pratyush; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)To decipher the biomolecular interaction mechanism play an important role in understanding the mystery of life. Understanding the mechanism of receptor-ligand interactions is of great importance both in context of fundamental biology and medical applications. Limited by the spatial or temporal resolution, wet-lab experiments may not be able to capture enough details to unravel the complicated interaction mechanisms. Molecular dynamics (MD) simulation, as computational tool to study many-body systems with atomic resolution, has emerged as a powerful tool to investigate the physical or biochemical properties of biomoleculars. Though MD simulation has its advantage in terms of its high spacial and temporal resolution over experimental methods, a big gap still remains between the time scale that can be reached by MD simulations and the time scale of the biological process that we want to study. In this thesis, I explore two frameworks to utilize the power of statistical mechanics, molecular dynamics, and matching learning to rectify this gap, and thus enable the simulation study of ligand-receptors interaction without overwhelming demand on computational resources. Firstly, I propose the reweighted autoencoded variational Bayes for enhanced sampling (RAVE) method, a new iterative scheme that uses the deep learning framework of information bottleneck to enhance sampling in molecular simulations. RAVE involves iterations between molecular simulations and deep learning in order to produce an increasingly accurate probability distribution along a low- dimensional latent space that captures the key features of the molecular simulation trajectory. RAVE determines an optimum, yet nonetheless physically interpretable, reaction coordinate and optimum probability distribution. Both then directly serve as the biasing protocol for a new biased simulation, which is once again fed into the deep learning module with appropriate weights accounting for the bias, the procedure continuing until estimates of desirable thermodynamic observables are converged. The usefulness and reliability of RAVE is demonstrated by applying it to two test-pieces, studying processes slower than milliseconds, calculating free energies, kinetics and critical mutations. I also systematically study the following questions: (a) the choice of a predictive time-delay in RAVE, or how far into the future should the machine learning model try to predict the state of a given system output from MD, and (b) for short time-delays, how much of an error is made in approximating the biased propagator for the dynamics as the unbiased propagator. I demonstrate through a master equation framework as to why the exact choice of time-delay is irrelevant as long as a small non-zero value is adopted. I also derive a correction to the objective function by reweighting the biased propagator, which better approximates the unbiased objective function without incurring extra computational overhead. To promote our understanding of RNA-ligand interaction at the molecular level, I use RAVE and collaborate with experimentalists to study the interplay between two ligands and PreQ1, which is a widely-studied model for RNA-small molecule recognition. I show that site-specific flexibility profiles from our simulations are in excellent agreement with in vitro measurements of flexibility using Selective 2’ Hydroxyl Acylation analyzed by Primer Extension and Mutational Profiling (SHAPE-MaP). And with orders of magnitude simulation speedup attained by RAVE, I can directly observe ligand dissociation for cognate and synthetic ligands from a PreQ1 riboswitch system. The artificial intelligence-argumented simulations reproduce known binding affinity profiles for the cognate and synthetic ligands, and pinpoint how both ligands make use of different aspects of riboswitch flexibility. On the basis of the dissociation trajectories, I also make and validate predictions of pairs of mutations for both the ligand systems that would show differing binding affinities. These mutations are distal to the binding site and could not have been predicted solely on the basis of structure. Secondly, I develop a framework based on statistical mechanics and generative Artificial Intelligence to use simulations or experiments performed at some set of temperatures to learn about the physics or chemistry at some other arbitrary temperature. Specifically, I use denoising diffusion probabilistic models, and show how these models in combination with replica exchange molecular dynamics achieve superior sampling of the biomolecular energy landscape at temperatures that were never even simulated without assuming any particular slow degrees of freedom. The key idea is to treat the temperature as a fluctuating random variable and not a control parameter as is usually done. This allows us to directly sample from the joint probability distribution in configuration and temperature space. The results are demonstrated for a chirally symmetric peptide and single-strand ribonucleic acid undergoing conformational transitions in all-atom water. I demonstrate how we can discover transition states and metastable states that were previously unseen at the temperature of interest, and even bypass the need to perform further simulations for wide range of temperatures. At the same time, any unphysical states are easily identifiable through very low Boltzmann weights. The procedure while shown here for a class of molecular simulations should be more generally applicable to mixing information across simulations and experiments with varying control parameters.Item THEORETICAL AND COMPUTATIONAL STUDIES OF HUMAN INTERPHASE CHROMOSOMES(2019) Shi, Guang; Thirumalai, Devarajan; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this thesis, various aspects of dynamical and structural properties of human interphase chromosomes are studied using both theoretical and computational tools. In addition, the cooperative transport by the multi-motor system was investigated using a stochastic kinetic model. First, I create the Chromosome Copolymer Model (CCM) by representing chromosomes as a copolymer. I first showed that the model is consistent with current experimental data. Using the CCM, I further investigated the dynamics of human interphase chromosomes. The model suggested that human interphase chromosome exhibit glassy-like dynamics characterized by sluggish movement, large loci-to-loci variations, and dynamical heterogeneity. Furthermore, I predicted that human interphase chromosomes also display extensive structural heterogeneity. Using a theoretical framework I developed based on polymer physics, I am able to identify that the existence of subpopulations is the reason for the Hi-C-FISH paradox. As an application of the theory, the information of subpopulations of cells can be readily extracted from experimental FISH data. The results suggest that heterogeneity is pervasive in genome organization at all length scales, reflecting large cell-to-cell variations. Then I proceed to develop a method to reconstruct the three-dimensional genome structure directly from Hi-C data. By applying the theory combined with various manifold embedding methods to experimental Hi-C data, I am able to visualize the averaged global 3D organization of a single chromosome and also local structures such as Topological Associated Domains. The method provides a fast and simple way to help experimentalist visualize the genome organization from the measured Hi-C data. Finally, I propose a kinetic model for the multi-motor system. I investigate the effect of mechanical coupling between multiple motors on their velocity and force-velocity behavior. Reduction of velocity is observed for coupled motor system especially when the coupling strength is strong. The model also shows that the multi-motors system is more efficient for transporting large cargo but is less efficient for transporting small cargo compared to a single motor.Item LIPID FORCE FIELD PARAMETERIZATION FOR IMPROVED MODELING OF ION-LIPID INTERACTIONS AND ETHER LIPIDS, AND EVALUATION OF THE EFFECTS OF LONG-RANGE LENNARD-JONES INTERACTIONS ON ALKANES(2019) Leonard, Alison N; Klauda, Jeffery B; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Chemical specificity of lipid models used in molecular dynamics simulations is essential to accurately represent the complexity and diversity of biological membranes. This dissertation discusses contributions to the CHARMM36 (C36) family of lipid force fields, including a revised model for the glycerol-ether linkage found in plasmalogens and archaeal membranes; interaction parameters between ions and lipid oxygens; and evaluation of the effects of long-range Lennard-Jones parameters on alkanes. Long-range Lennard-Jones interactions have a significant impact on structural and thermodynamic properties of systems with nonpolar regions such as membranes. Effects of these interactions on properties of alkanes are investigated. Implementation of the Lennard-Jones particle-mesh Ewald (LJ-PME) method with the C36 additive and Drude polarizable force fields improves agreement with experiment for thermodynamic and kinetic properties of alkanes, with Drude outperforming the additive FF for nearly all quantities. Trends in the temperature dependence of the density and isothermal compressibility are also improved. Phospholipids containing an ether linkage between the glycerol backbone and hydrophobic tails are prevalent in human red blood cells and nerve tissue. Ab initio results are used to revise linear ether parameters and develop new parameters for the glycerol-ether linkage in lipids. The new force field, called C36e, more accurately represents the dihedral potential energy landscape and improves solution properties of linear ethers. C36e allows more water to penetrate an ether-linked lipid bilayer, increasing the surface area per lipid compared to simulations carried out with the original C36 parameters and improving structural properties. In addition to modulating membrane structure, lipid-ion interactions influence protein-ligand binding and conformations of membrane-bound proteins. Interaction parameters are introduced describing Be2+ affinity for binding sites on lipids. Experimental binding affinities reveal that Be2+ strongly binds to phosphoryl groups. Revised interaction parameters reproduce binding affinities in solution simulations. In a separate effort, experimental results for the radius of gyration (R_g) of polyethylene glycol (PEG) in various concentrations of KCl reveal that, while C36e parameters reproduce experimental R_g of PEG in the absence of KCl, adding salt results in underestimation of〖 R〗_g. It is found that the water shell around PEG affects R_g calculated from neutron scattering experiments, and K+-PEG interactions increase the gauche character of PEG.Item Effect of electrostatic interactions on biomolecular self-assembly processes(2018) Xu, Hongcheng; Matysiak, Silvina; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Molecular level self-assembly processes are not only ubiquitous in living cells, but also widely applied in industry to synthesize and fabricate a variety of nanoscale biomaterials. The emergence of ordered aggregates from disordered components typically requires driving forces from electrostatic interactions to hydrophobic-hydrophilic effects. This thesis aims to elucidate the effect of electrostatic interactions, and the intricate balance between electrostatic and hydrophobic interactions in dictating spontaneous self-assembly processes with three case studies covering various types of biomolecules. For the first case study, we have examined the pH-induced polysaccharide hydrogel network formation. The polysaccharide molecule chitosan forms hydrogels composed of water-filled cross-linking polymer chains. The pH-responsive selfassembly behavior of chitosan hydrogel has been utilized in fabricating nanomaterials with a wide range of applications. To investigate the role of electrostatic interactions in the chitosan hydrogel network formation, we have developed a novel coarse-grained (CG) chitosan polymer model that captures the pH-dependent self-assembly behavior. The structural, mechanical, and thermodynamical properties of chitosan polymer hydrogel have been characterized well in the simulations and agree very well with experimental observations. For the second case study, the anticancer peptide folding induced by phospholipid membrane was investigated. Peptide folding in an aqueous environment is a self-assembly process that has been well studied over the years. However, the folding in a membranous environment is complicated by the heterogeneity in phospholipid distributions and membrane-peptide interactions. To provide information about the driving forces behind membrane peptide folding and the effect of lipid composition on folding behavior, my work has combined our recently developed Water-Explicit Polarizable Protein (WEPPRO) and Membrane (WEPMEM) model to explore the driving forces behind model anticancer peptide SVS-1 folding and how they can be affected by changing the membrane composition. For the third case study, we have studied the formation of nanodomains in mixed lipid bilayers. Phospholipid membranes are essential components in animal cells. The heterogeneous distribution of phospholipids on the membrane bilayer plays an important role in cellular structure and function such as signal transduction and membrane fusion. Interactions between a mixture of lipids and different ligands give rise to interesting patterns that are yet to be understood. Model lipid bilayers with a content of anionic lipids have been shown experimentally to be sensitive to the presence of certain ions. Monovalent cation Li+ induces membrane phase transition similarly as Ca2+ and Mg2+, while distinctive from other monovalent cations like Na+ and K+. We have evaluated the role of electrostatics interactions in the sizedependent cation-induced lipid nanodomain formation with binary mixed bilayers composed of zwitterionic and anionic lipids.Item Applications of Multiphoton Imaging Techniques To The Study Of Protein Interactions(2009) Rosales, Tilman J.; Walker, Robert A; Chemical Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Several recent improvements in microscopy have been driven by advances in ultrafast laser technology. The goal of the research described in this dissertation was to develop noninvasive, optically based methods to measure the mobility of macromolecules in biologically relevant systems. These methods exploit advances in ultrafast laser science and recent developments in multiphoton spectroscopy techniques. Each of the techniques described in this dissertation is validated and standardized using well characterized systems. We have explored the following techniques: First, 2-photon 2-color Fluorescence Cross Correlation Spectroscopy (FCCS) a powerful technique to measure dilute protein interactions in living cells. We have used FCCS to probe AR-Tif2 (Androgen Receptor - activating cofactor) interactions in the presence of casodex, an antagonist yielding decreased binding affinity. On a much faster timescale, exploring rotational rather than translational diffusion, we used molecular dynamics simulations of the model probe perylene to show that there is `room to wiggle' (sub-ps libration) within pure hexadecane. Third, combining picosecond and microsecond scales, we built a system to measure both rotational and translational motion in one experiment, using advanced Time Correlated Single Photon Counting (TCSPC) techniques. We have tested our ability to measure and link simultaneously the translational rates and decay rates of Alexa488 dye and other biologically relevant fluorophores. Next, exploiting non-linear vibrational spectroscopy, we have imaged the non-fluorescent molecule NAD+ in DPPC vesicles, the C-H stretch of lipids in vesicles and polystyrene beads, and the O-H stretch of water inside living cells (vs. O-D) to demonstrate the chemically selective imaging capabilities of Coherent Anti-Stokes Raman Spectroscopy (CARS) Microscopy. Most recently, we have built a STED (STimulated Emission Depletion) Microscope capable of extracting fluorescent images well below the diffraction limit. The STED microscope was tested using both 170nm fluorescent beads and a novel photochromic dye.Item Ultra-fast Dynamics of Small Molecules in Strong Fields(2006-04-24) Zhao, Kun; Hill, Wendell T; Chemical Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Correlation detection techniques (image labeling, coincidence imaging, and joint variance) are developed with an image spectrometer capable of collecting charges ejected over 4\pi sr and a digital camera synchronized with the laser repetition rate at up to 735 Hz. With these techniques, molecular decay channels ejecting atomic fragments with different momenta (energies) can be isolated; thus the initial molecular configurations (bond lengths and/or bond angles) and orientations as well as their distributions can be extracted. These techniques are applied to study strong-field induced dynamics of diatomic and triatomic molecules. Specific studies included the measurements of the Coulomb explosion energy as a function of bond angle in linear (CO_2) and bent (NO_2) triatomics and the ejection anisotropy relative to the laser polarization axis during Coulomb explosions in both triatomic (CO_2 and NO_2) and diatomic (H_2, N_2 and O_2) systems. The experiments were performed with 100 fs, 800 nm laser pulses focused to 0.1 ~ 5 \times 10^15 W/cm^2. The explosion energy of NO_2 decreases monotonically by more than 25% from the smallest to the largest bond angle. By contrast, the CO_2 explosion energies are nearly independent of bond angle. The enhanced-ionization and static-screening models in two-dimension with three charge centers were developed to simulate the explosion energies as a function of bond angle. The predictions are consistent with the measurements of CO_2 and NO_2. The observed explosion signals as a function of bond angle for both triatomics show large-amplitude vibrations. The ejection angular distributions in triatomic (CO_2 and NO_2) and diatomic (H_2, N_2, and O_2) Coulomb explosions were measured; the contribution made to the ejection anisotropy by dynamic alignment was studied by comparing the images obtained with linearly and circularly polarized fields. Different angular distributions of the molecules are consistent with different ionization stages, induced dipole moments and rotational constants. The dynamic alignment of H_2 is found to be nearly complete. A larger dynamic alignment of CO_2 than that of N_2 or O_2 is consistent with that more electrons have been removed from CO_2 and the precursor molecular ion spends more time in the field prior to the explosion.