No description available.

No description available.

By using the technology of molecularly imprinted polymer (MIP), we propose to synthesize a protein conformational imprint that also acts as a plastic enzyme, inducing protein structural transitions. The imprint aims at MIP-induced stabilization and / or formation of bound protein secondary structure and the applications associated with analysis and correction of misfolded proteins. The screening of polymeric functional monomers being able to induce the conformational transitions in proteins is investigated in this report. The fluoro-silanes (3-heptafluoroisopropoxy)propalethoxysilane (7F) and 3,3,3-trifluoropropylmethoxysilane (3F) were employed as functional monomers for synthesis of this catalytic protein conformational imprint via sol-gel reactions. 3F was demonstrated superior to 7F for fluoro-modification of tetraethylorthosilicate (TEOS) gel in terms of retaining gel transparency and increasing hydrophobicity while maintaining a uniform distribution of encapsulated protein. Both hydrolyzed 3F and polymerized 3F exhibited strong influences on structure transitions of three template proteins: bovine serum albumin (BSA), beta-lactoglobulin (BLG), and bovine carbonic anhydrase (BCA). The formation of molten globule intermediates that stabilized by increased alpha-helices was induced by the trifluoro-silane in BLG and BCA. Additionally, 3F was effective at a lower concentration than the benchmark fluoro-alcohol 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP), validating the application of 3F as a functional monomer for protein conformational imprinting.

abstract: Proteins are essential for most biological processes that constitute life. The function of a protein is encoded within its 3D folded structure, which is determined by its sequence of amino acids. A variation of a single nucleotide in the DNA during transcription (nSNV) can alter the amino acid sequence (i.e., a mutation in the protein sequence), which can adversely impact protein function and sometimes cause disease. These mutations are the most prevalent form of variations in humans, and each individual genome harbors tens of thousands of nSNVs that can be benign (neutral) or lead to disease. The primary way to assess the impact of nSNVs on function is through evolutionary approaches based on positional amino acid conservation. These approaches are largely inadequate in the regime where positions evolve at a fast rate. We developed a method called dynamic flexibility index (DFI) that measures site-specific conformational dynamics of a protein, which is paramount in exploring mechanisms of the impact of nSNVs on function. In this thesis, we demonstrate that DFI can distinguish the disease-associated and neutral nSNVs, particularly for fast evolving positions where evolutionary approaches lack predictive power. We also describe an additional dynamics-based metric, dynamic coupling index (DCI), which measures the dynamic allosteric residue coupling of distal sites on the protein with the functionally critical (i.e., active) sites. Through DCI, we analyzed 200 disease mutations of a specific enzyme called GCase, and a proteome-wide analysis of 75 human enzymes containing 323 neutral and 362 disease mutations. In both cases we observed that sites with high dynamic allosteric residue coupling with the functional sites (i.e., DARC spots) have an increased susceptibility to harboring disease nSNVs. Overall, our comprehensive proteome-wide analysis suggests that incorporating these novel position-specific conformational dynamics based metrics into genomics can complement current approaches to increase the accuracy of diagnosing disease nSNVs. Furthermore, they provide mechanistic insights about disease development. Lastly, we introduce a new, purely sequence-based model that can estimate the dynamics profile of a protein by only utilizing coevolution information, eliminating the requirement of the 3D structure for determining dynamics. / Dissertation/Thesis / Doctoral Dissertation Physics 2016

Rhomboids are a family of intramembrane serine proteases that cleave transmembrane protein substrates within the lipid membrane. They are involved in a wide range of biological processes, including signal transduction, parasite invasion, bacterial quorum sensing and apoptosis. While previous X-ray crystal structures and functional studies have provided some detailed insights into the mechanism of intramembrane hydrolysis, it is still not clear how the transmembrane substrate can gain access into the active site from the lipid environment. While several modes of action have been suggested, one hypothesis proposes a lateral movement of the fifth transmembrane helix, causing a displacement that would allow transmembrane substrates to enter the rhomboid active site. A powerful method that has the potential to yield insights into rhomboid dynamics is solution NMR; however, the large size of rhomboid protease samples has complicated conventional methods typically used to assess protein structure and dynamics. ¹⁹F NMR could allow the study of rhomboid conformational dynamics by providing a simplified spectrum with high sensitivity to changes in local chemical environments. In this thesis various methods of ¹⁹F incorporation were evaluated for utility in studying rhomboid conformational dynamics, focusing on the GlpG rhomboid from E. coli. First, GlpG samples were prepared with ¹⁹F incorporated into tryptophan sidechains, and 1D ¹⁹F NMR spectra were acquired. While spectra with decent spectral dispersion were obtained, the assignment process was complicated by low signal-to-noise, and multiple changes in the spectrum introduced by the mutation. Chemoselective labelling of cysteine residues with probes containing a trifluoromethyl group was also investigated and found to give rise to well resolved ¹⁹F NMR spectra with promising characteristics. In addition, protocols for incorporation of trifluoromethyl-phenylalanine using unnatural amino acid incorporation at introduced amber codon sites were also explored, since one of the long-term goals of this work is to study ¹⁹F-labelled GlpG in its native lipid environment. For this purpose, some protocol development was also performed to introduce GlpG into lipid nanoparticles using styrene maleic acid co-block polymers. However, low expression yields of trifluoromethyl-phenylalanine-labelled GlpG and the large size of the lipid nanoparticles are not yet compatible with solution NMR. Nonetheless, this thesis lays the groundwork for further development of these samples to allow the future study of conformational exchange of GlpG in native lipid membranes.

No description available.

In structural biology, immense effort has been put into discovering functionally relevant atomic resolution protein structures. Still, most experimental, computational and machine learning-based methods alone struggle to capture all the functionally relevant states of many proteins without very involved and system-specific techniques. In this thesis, I propose a new broadly applicable method for determining an ensemble of functionally relevant protein structures. The method consists of (1) generating multiple protein structures from AlphaFold2 by stochastic subsampling of the multiple sequence alignment (MSA) depth, (2) screening these structures using small-angle X-ray scattering (SAXS) data and a structure validation scoring tool, (3) simulating the screened conformers using short molecular dynamics (MD) simulations and (4) refining the ensemble of simulated structures by reweighting it against SAXS data using a bayesian maximum entropy (BME) approach. I apply the method to the T-cell intracellular antigen-1 (TIA-1) protein and find that the generated ensemble is in good agreement with the SAXS data it is fitted to, in contrast to the original set of conformations from AF2. Additionally, the predicted radius of gyration is much more consistent with the experimental value than what is predicted from a 450 ns long MD simulation starting from a single structure. Finally, I cross-validate my findings against small-angle neutron scattering (SANS) data and find that the method-generated ensemble, although not in a perfect way, fits some of the SANS data much better than the ensemble from the long MD simulation. Since the method is fairly automatic, I argue that it could be used by non-experts in MD simulations and also in combination with more advanced methods for more accurate results. I also propose generalisations of the method by tuning it to different biological systems, by using other AI-based methods or a different type of experimental data. / Inom strukturbiologi har ett stort arbete lagts på att bestämma funktionellt relevanta proteinstrukturer på atomnivå. Dock så har de flesta experimentella, simuleringsbaserade och maskinlärningsbaserade metoderna svårigheter med att ensamma bestämma alla funktionellt relevanta strukturer utan väldigt involverade och system-specifika tekniker. I den här masteruppsatsen föreslår jag en ny allmänt applicerbar metod för att bestämma ensembler av funktionellt relevanta proteinstrukturer. Metoden består utav (1) generering av ett flertal proteinkonformationer från AlphaFold2 (AF2) genom att stokastiskt subsampla djupet för multisekvenslinjering, (2) välja ut en delmängd av dessa konformationer med hjälp av small angle X-ray scattering (SAXS) och ett strukturvalideringsverktyg, (3) simulera de utvlada konformationerna med hjälp av korta molekyldynamiksimuleringar (MD-simuleringar) och (4) förfina ensemblen av simulerade konformationer genom att vikta om dem utgående från SAXS-data med en Bayesian Maximum Entropy-metod. Jag applicerar min föreslagna metod på proteinet T-cell intracellular antigen-1 och finner att den genererade ensemblen har en god anpassning till den SAXS-profil den är anpassad till, till skillnad från ensemblen av konformationer direkt genererade av AF2. Dessutom är den förutspådda tröghetsradien mycket mer konsekvent med den experimentellt förutspådda radien än vad som förutspås utifrån en 450 ns lång MD-simulering utgående från en ensam struktur. Slutgiltigen korsvaliderar jag mina upptäckter mot data från small-angle neutron scattering (SANS) och finner att den metod-genererade ensemblen, om än inte på ett perfekt sätt, passar en del av SANS-datan mycket bättre än ensemblen från den långa MD simulationen. Då metoden är ganska automatisk så argumenterar jag för att den med fördel kan användas av icke-experter inom MD simuleringar och dessutom kombineras med mer avancerade metoder för ännu bättre resultat. Jag föreslår också generaliseringar av metoden genom att kunna anpassa den till olika biologiska system, genom att använda andra AI-baserade metoder eller att använda andra typer av experimentell data.

Control and analysis of protein aggregation is an increasing challenge to biopharmaceutical research and development. Therefore it is important to understand the interactions, causes and analysis of particles in order to control protein aggregation to enable successful biopharmaceutical formulations. This work investigates the role of different non-ionic surfactants on protein conformational stability, as assessed by HSDSC, and on protein size stability as assessed by Dynamic Light Scattering (DLS), HIAC and MFI. BSA and IgG2 were used as model proteins. Thermal unfolding experiments indicated a very weak surfactant-immunoglobulin IgG2 interaction, compared to much stronger interactions for the BSA surfactant systems. The DLS results showed that BSA and IgG2 with different surfactants and concentration produced different levels of particle size growth. The heat treatment and aging of samples in the presence of Tween 20, Tween 80, Brij 35 and Pluronic F-68 surfactants led to an increase in the populations of larger particles for BSA samples, whereas IgG2 systems did not notably aggregate under storage conditions MFI was shown to be more sensitive than HIAC technique for measuring sub-visible particles in protein surfactant systems. Heat treatment and storage stress showed a significant effect on BSA and IgG2 protein sub-visible particle size stability. This work has demonstrated that both proteins with different Tween 20, Tween 80, Brij 35 and Pluronic F-68 concentrations, have different level of conformational and size stability. Also aging samples and heating stress bears the potential to generate particles, but this depends on surfactant type. Poor predictive correlations between the analytical methods were determined.

The goal of this thesis has been to throw light on a selection of open problems in chemical and biological physics using the general principles of statistical mechanics. These problems are all broadly concerned with the role of fluctuations in the dynamics of macromolecular systems. More specifically, they are concerned with identifying the microscopic roots of a number of interesting and unusual effects, including fractional viscoelasticity, anomalous chain cyclization dynamics in crowded environments, subdifffusion in hair bundles, symmetries in the work distributions of stretched polymers, heterogeneities in the geometries of reptation channels in polymer melts, and non-Gaussianity in the distributions of the end products of gene expression. I have shown here that all these effects are expressions of essentially the same underlying process of stochasticity, which can be described in terms of the dynamics of a point particle or a continuous curve that evolves in simple potentials under the action of white or colored Gaussian noise [8]. I have also shown that this minimal model of time-dependent behavior in condensed phases is amenable to analysis, often exactly, by path integral methods [13-15], which are naturally suited to the treatment of random processes in many-body physics. The results of such analyses are theoretical expressions for various experimentally measured quantities, comparisons with which form the basis for developing physical intuition about the phenomena under study. The general success of this approach to the study of stochasticity in biophysics and molecular biology holds out hopes of its application to other unsolved problems in these fields. These include electrical transport in DNA [143], quantum coherence in photosynthesis [144], power generation in molecular motors [145], cell signaling and chemotaxis [146], space dependent diffusion [147], and self-organization of active matter [148], to name a few. Most of these problems are characterized by non-linearities of one kind or another, so they add a new layer of complexity to the problems considered in this thesis. Although path integral and related field theoretic methods are equipped to handle such complexities, the attendant calculations are expected to be non-trivial, and the challenge to theory will be to devise effective approximation schemes for these methods, or to develop new and more sophisticated methods altogether.

Control and analysis of protein aggregation is an increasing challenge to biopharmaceutical research and development. Therefore it is important to understand the interactions, causes and analysis of particles in order to control protein aggregation to enable successful biopharmaceutical formulations. This work investigates the role of different non-ionic surfactants on protein conformational stability, as assessed by HSDSC, and on protein size stability as assessed by Dynamic Light Scattering (DLS), HIAC and MFI. BSA and IgG2 were used as model proteins. Thermal unfolding experiments indicated a very weak surfactant-immunoglobulin IgG2 interaction, compared to much stronger interactions for the BSA surfactant systems. The DLS results showed that BSA and IgG2 with different surfactants and concentration produced different levels of particle size growth. The heat treatment and aging of samples in the presence of Tween 20, Tween 80, Brij 35 and Pluronic F-68 surfactants led to an increase in the populations of larger particles for BSA samples, whereas IgG2 systems did not notably aggregate under storage conditions MFI was shown to be more sensitive than HIAC technique for measuring sub-visible particles in protein surfactant systems. Heat treatment and storage stress showed a significant effect on BSA and IgG2 protein sub-visible particle size stability. This work has demonstrated that both proteins with different Tween 20, Tween 80, Brij 35 and Pluronic F-68 concentrations, have different level of conformational and size stability. Also aging samples and heating stress bears the potential to generate particles, but this depends on surfactant type. Poor predictive correlations between the analytical methods were determined.