Can Förster Theory Describe Stereoselective Energy Transfer Dynamics in a Protein-Ligand Complex?

Förster resonance energy transfer (FRET) reactions involving ligands and aromatic amino acids can substantially impact the fluorescence properties of a protein-ligand complex, an impact intimately related to the corresponding binding mode. Structural characterization of such binding events in terms of intermolecular distances can be done through the well-known R-6 distance-dependent Förster rate expression. However, such interpretation suffers from uncertainties underlying Förster theory in the description of the electronic coupling that promotes FRET, mostly related to the dipole-dipole orientation factor, dielectric screening effects and deviations from the ideal dipole approximation. Here, we investigate how Förster approximations impact the prediction of energy transfer dynamics in the complex between flurbiprofen and human serum albumin (HSA), as well as a model flurbiprofen-Trp dyad, in which recent observations of enantioselective fluorescence quenching has been ascribed to energy transfer from flurbiprofen to Trp. To this aim, we combine classical molecular dynamics simulations with polarizable quantum mechanics/molecular mechanics (QM/MM) calculations that allow overcoming Förster approximations. On the basis of our results, we discuss the potential of structure-based simulations in the characterization of drug-binding events through fluorescence techniques. Overall, we find an excellent agreement among theory and experiment both in terms of enantioselectivity and FRET times, thus strongly supporting the reliability of the binding modes proposed for the (S)- and (R)- enantiomers of flurbiprofen. In particular, we show that the dynamic quenching arises from a small fraction of drug bound to the secondary site of HSA at the interface between subdomains IIA and IIB, whereas the enantioselectivity arises from the larger flexibility of the (S)-flurbiprofen enantiomer in the binding pocket

Faculty of Mathematics and Science 1st Graduate Research Day Conference, 2022

FMS Graduate Research Day (FMS GRaD) is an academic conference open to all FMS students with a mandate to celebrate and communicate Brock University research and teaching. The FMS GRaD 2022 conference was hosted by the Dean’s office of the Faculty of Mathematics and Science and Graduate Mathematics and Science Society at Brock University. With 57 presenters and over 300 attendees this first FMS GRaD held on September 16th 2022 strengthened the STEM research community and highlight the research and profile of FMS graduate student research programs

Enhanced sampling in molecular dynamics using metadynamics, replica-exchange, and temperature-acceleration

We review a selection of methods for performing enhanced sampling in molecular dynamics simulations. We consider methods based on collective variable biasing and on tempering, and offer both historical and contemporary perspectives. In collective-variable biasing, we first discuss methods stemming from thermodynamic integration that use mean force biasing, including the adaptive biasing force algorithm and temperature acceleration. We then turn to methods that use bias potentials, including umbrella sampling and metadynamics. We next consider parallel tempering and replica-exchange methods. We conclude with a brief presentation of some combination methods. \ua9 2013 by the author; licensee MDPI, Basel, Switzerland

The Influence of Aspen Chemistry and the Nutritional Context on Aspen Herbivory

Herbivory is one major force accelerating aspen decline in North America, but it is unclear why herbivores prefer certain aspen stands over others, or over other plant species in the understory. In this dissertation, I determined the influence of nutrients and plant secondary compounds (PSC), physiological state, chemical composition, and prior experience on aspen preference by sheep in controlled pen experiments. In addition, I explored the relationship between herbivory, regeneration, recruitment, and other landscape elements for specific aspen stands within Wolf Creek Ranch in northern Utah using biomass and chemical composition of the understory and chemical defenses of juvenile aspen trees (i.e., the foodscape). Aspen intake was enhanced when lamb diets contained a high crude protein to energy ratio or when the basal diet contained a low density of energy. Intake was depressed as concentrations of PG (phenolic glycosides) increased in aspen leaves or when lambs were fed a high energy to protein ratio. The effects of nutrients on aspen intake were greater when phenolic glycosides in aspen were present at low concentrations. However, when given a choice between aspen leaves of high or low PG content, lamb preference depended more on aspen nutrient and mineral availability, or on prior diet, than on defense chemistry. On the landscape, I found that stands at low elevations with low abundance of nutrients in the understory are more likely to experience less regeneration and recruitment than those growing within nutrient-rich sites. Aspen browsing was negatively correlated with PG content in aspen stands, and elk presence (measured via fecal pellets) was negatively correlated with abundance of understory protein. In conclusion, aspen herbivory appears to be controlled by the interplay between types and amounts of nutrients offered by the landscape and the chemical composition of aspen stands. A clear assessment of these variables on the landscape, i.e., the foodscape, will aid in the development of novel management strategies aimed at providing nutrients (e.g., through supplements, introduced forages) at strategic locations in order to reduce aspen herbivory within at-risk aspen stands

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field

Atomistic Simulations of Model Amyloid Beta Aggregates, Water Networks and their Optical Properties

Based on the amyloid hypothesis, amyloid oligomers and the fibrils that they aggregate into, have been implicated in neurodegenerative diseases. Most of these amyloid proteins live in a solvent environment. The role of solvent in modulating the structural and dynamical properties of amyloid proteins remains poorly understood. In this thesis, computer simulations are used to reveal the structural properties of the amyloid protein and the coupling between protein and water using model systems. After assessing the validity of the force fields by comparison with high-level quantum chemistry calculations, we examine further the conformational free energy landscape of an amyloid protein. Different conformations characterized in the free energy surface are driven by internal protein interactions as well as interactions between protein and water, resulting in the collective reorganization of protein and water hydrogen bond networks. We show that these proteins are surrounded by water wires that add a roughness to the free energy surface. To better understand the water hydrogen bond network and particularly the water wires around protein, we used data-science algorithms allowing for the dimensionality and free energy landscape of different water coordinates to be determined. These results confirm that using water wire coordinates encodes more information on the underlying secondary structure of the protein. Finally, ab initio calculations are used to investigate the optical properties of amyloid proteins to help rationalize recent experiments suggesting the intrinsic fluorescence in fibrils that can occur without aromatic residues

Understanding and Exploiting Protein Allostery and Dynamics Using Molecular Simulations

Protein conformational landscapes contain much of the functionally relevant information that is useful for understanding biological processes at the chemical scale. Understanding and mapping out these conformational landscapescan provide valuable insight into protein behaviors and biological phenomena, and has relevance to the process of therapeutic design. While structural biology methods have been transformative in studying protein dynamics, they are limited by technicallimitations and have inherent resolution limits. Molecular dynamics (MD) simulations are a powerful tool for exploring conformational landscapes, and provide atomic-scale information that is useful in understanding protein behaviors. With recent advances in generating datasets of large timescale simulations (using Folding@home) and powerful methods to interpret conformational landscapes such as Markov State Models (MSMs), it is now possible to study complex biological phenomena and long-timescale processes. However, inferring communication between residues across long distances, referred to as allosteric communication, remains a challenge. Allostery is a ubiquitious biological phenomena by which two distant regions of a protein are coupled to one anotherover large distances. Allosteric coupling is the mechanism through which events in one region (such as ligand binding) alter the conformation or dynamics of another region (ie. large conformational domain motions). For example, allostery plays a critical role in cellular signaling, such as in the transfer of a signal from outside the cell to cytosolic proteins for generating a cellular response. While many methods have made tremendous progress in inferring and measuring allosteric communication usingstructures or molecular simulations, they rely on a structural view of allostery and do not account for the role of conformational entropy. Furthermore, it remains a challenge to interpret allosteric coupling in large, complex biomolecules relevant to physiology and disease. In this thesis, I present a method to measure the Correlation of All Rotameric and Dynamical States (CARDS) whichis used to construct and interpret allosteric networks in biological systems. CARDS allows us to infer allostery both via concerted changes in protein structure and in correlated changes in conformational entropy (dynamic allostery). CARDS does so by parsing trajectories into dynamical states which reflect whether a residue is locally ordered (ie. stable in a single rotameric basin) or disordered (ie. rapidly hopping between rotamers). Here I explain the CARDS methodology (chapter 2) and demonstrate applications to a variety of disease-relevantsystems. In particular, I apply CARDS and other sophisticated computational methods to understand the process of G protein activation (chapter 3), a protein whose mutations are linked to cancers such as uveal melanoma. I further demonstrate the utility of CARDS in the study a potentially druggable pocket in the ebolavirus protein VP35 (chapter 4). The analyses and models constructed in this work are supported by experimental testing. Lastly, I demonstrate how integrating MD with experiments, sometimes with the help of citizen-scientists around the world, can provide unique insight into biological systems and identify potentially useful targets. In particular, I highlight our recent effort converting Folding@home into an exascale computer platform to hunt for potentially druggable pockets in the proteome of SARS-CoV-2 (chapter 7) (the cause of the COVID19 pandemic)

Understanding fluorescent amyloid biomarkers by computational chemistry

Protein misfolding diseases, including neurodegenerative disorders like Alzheimer’s disease, are characterized by the involvement of amyloid aggregation, which emphasizes the need for molecular biomarkers for effective disease diagnosis. The thesis addresses two aspects of biomarker development: firstly, the computation of vibrationally resolved spectra of small fluorescent dyes to detect amyloid aggregation, and secondly, the binding and unbinding processes of a novel ligand to the target protein. In relation to the first aspect, a hybrid model for vibrational line shapes of optical spectra, called VCI-in-IMDHO, is introduced. This model enables the treatment of selected modes using highly accurate and anharmonic vibrational wave function methods while treating the remaining modes using the approximate IMDHO model. This model reduces the computational cost and allows for the calculation of emission line shapes of organic dyes with anharmonicity in both involved electronic states. The interaction between the dyes and their environment is also explored to predict the photophysical properties of the oxazine molecules in the condensed phase. The position and the choice of the solvent molecule have a significant impact on the spectra of the studied systems as they altered the spectral band shape. However, further studies are necessary to confirm the findings. In addition to neurodegenerative diseases, the systemic amyloidoses represent another group of disorders caused by misfolded or misassembled proteins. In the cardiac domain, the accumulation of amyloid fibrils formed by the transthyretin (TTR) protein leads to cardiac dysfunction and restrictive cardiomyopathy. The investigation of binding and unbinding pathways between the TTR protein and its ligands is crucial for gaining a comprehensive understanding and enabling early detection of systemic amyloidoses and related disorders. Hence, exploring the different binding modes and the dissociation pathways of TTR-ligand complex is the primary objective of the second aspect of this thesis. The experimental study provides evidence of binding and X-ray crystallographic structure data on TTR complex formation with the fluorescent salicylic acid-based pyrene amyloid ligand (Py1SA). However, the electron density from X-ray diffraction did not allow confident placement of Py1SA, possibly due to partial ligand occupancy. Molecular dynamics and umbrella sampling approaches were used to determine the preferred orientation of the Py1SA ligand in the binding pocket, with a distinct preference for the binding modes with the salicylic acid group pointing into the pocket.Deutsche Forschungs-gemeinschaft (DFG)/Emmy Noether/KO 5423/1- 1/E