Computational methods to design biophysical experiments for the study of protein dynamics

In recent years, new software and automated instruments have enabled us to imagine autonomous or "self-driving" laboratories of the future. However, ways to design new scientific studies remain unexplored due to challenges such as minimizing associated time, labor, and expense of sample preparation and data acquisition. In the field of protein biophysics, computational simulations such as molecular dynamics and spectroscopy-based experiments such as double electron-electron resonance and Fluorescence resonance energy transfer techniques have emerged as critical experimental tools to capture protein dynamic behavior, a change in protein structure as a function of time which is important for their cellular functions. These techniques can lead to the characterization of key protein conformations and can capture protein motions over a diverse range of timescales. This work addresses the problem of the choice of probe positions in a protein, which residue-pairs should experimentalists choose for spectroscopy experiments. For this purpose, molecular dynamics simulations and Markov state models of protein conformational dynamics are utilized to rank sets of labeled residue-pairs in terms of their ability to capture the conformational dynamics of the protein. The applications of our experimental study design methodology called OptimalProbes on different types of proteins and experimental techniques are examined. In order to utilize this method for a previously uncharacterized protein, atomistic molecular dynamics simulations are performed to study a bacterial di/tri-peptide transporter a typical representative of the Major Facilitator Superfamily of membrane proteins. This was followed by ideal double electron-electron resonance experimental choice predictions based on the simulation data. The predicted choices are superior to the residue-pair choices made by experimentalists which failed to capture the slowest dynamical processes in the conformational ensemble obtained from our long timescale simulations. For molecular dynamics simulations based design of experimental studies to succeed both ensembles need to be comparable. Since this has not been the case for double electron-electron resonance distance distributions and molecular simulations, we explore possible reasons that can lead to mismatches between experiments and simulations in order to reconcile simulated ensembles with experimentally obtained distance traces. This work is one of the first studies towards integrating spectroscopy experiment design into a computational method systematically based on molecular simulations

Computational simulations on membranes and a transmembrane protein

To accurately model the transmembrane proteins, accurate descriptions of its natural environment, i.e., lipids, are critical. The all-atom CHARMM36 lipid force field (C36FF-AA) is tested with molecular dynamics (MD) simulations. Through comparison to experiments, we conclude that the C36FF-AA is accurate for use with bilayers of varying head and chain types over biologically relevant temperatures. The united-atom chain model of the C36FF (C36FF-UA) of common lipids is developed to improve simulation efficiency. It shows good agreement between the simulated bilayer properties obtained by C36FF-UA and experiments, and also between the simulated results from UA and AA lipid models. Besides the single-component membrane, multiple-components 18:2 linoleoyl-containing soybean membrane models have been developed. The structural properties of pure linoleoyl bilayers agree well with experiments, based on which the soybean membrane models also result in reasonable structural properties. Accurate lipid force field greatly facilitates the study of transmembrane proteins. Lactose permease of Escherichia coli (E. coli) belongs to major facilitator superfamily (MFS) which is the largest and most diverse family of transporters and serves as a model for secondary active transporters (SATs) in this dissertation. LacY structures of the cytoplasmic-open, occluded-like, and recently periplasmic-partially-open state have been determined, however, the crystal structure of LacY in the periplasmic-open state is still not available. The periplasmic-open LacY structure is important for understanding the complete proton/sugar transport process of LacY as well as other similar SAT proteins. MD simulations are performed to test the accuracy of the previously developed periplasmic-open LacYIM-EX model (JMB 404:506), and two other periplasmic-open LacY models, LacYSW and LacYFP models (JMB 407:698). The simulated results indicate that LacYIM-EX is the only structure that remains stable in the periplasmic-open state. The MD dummy spin label simulations (MDDS) have also been performed and the results show that the orientation of the spin labels significantly affect the distance measurement so that the proper interpretation of DEER requires the aid of MDDS simulations. Self-guided Langevin dynamics (SGLD) simulations are performed to search periplasmic-open LacY. The results show that no outward-facing is obtained with nanosecond-averaged results, but if we study individual structures, conformational sampling is obtained with certain SGLD parameters that enhance natural helical motions. This SGLD approach might hold promise for studying conformational changes of other SAT proteins

An in-silico study of the type II NADH: Quinone Oxidoreductase (ndh2). A new anti-malaria drug target

Malaria is caused by Plasmodium parasites, spread to people through the bites of infected female Anopheles mosquitoes. This study focuses on all 5 (Plasmodium falciparum, Plasmodium knowlesi, Plasmodium malariae, Plasmodium ovale and Plasmodium vivax) parasites that cause malaria in humans. Africa is a developing continent, and it is the most affected with an estimation of 90% of more than 400 000 malaria-related deaths reported by the World Health Organization (WHO) report in 2020, in which 61% of that number are children under the ages of five. Malaria resistance was initially observed in early 1986 and with the progression of time anti-malarial drug resistance has only increased. As a result, there is a need to study the malarial proteins mechanism of action and identify alternative treatment strategies for this disease. Type II NADH: quinone oxidoreductase (NDH2) is a monotopic protein that catalyses the electron transfer from NADH to quinone via FAD without a proton-pumping activity, and functions as an initial enzyme, either in addition to or as an alternative to proton-pumping NADH dehydrogenase (complex I) in the respiratory chain of bacteria, archaea, and fungal and plant mitochondrial. The structures for the Plasmodium knowlesi, Plasmodium malariae, Plasmodium ovale and Plasmodium vivax were modelled from the crystal structure of Plasmodium falciparum (5JWA). Compounds from the South African natural compounds database (SANCDB) were docked against both the NDH2 crystal structure and modelled structures. By performing in silico screening the study aimed to find potential compounds that might interrupt the electron transfer to quinone therefore disturbing the enzyme‟s function and thereby possibly eliminating the plasmodium parasite. CHARMM-GUI was used to create the membrane (since this work is with membrane-bound proteins) and to orient the protein on the membrane using OPM server guidelines, the interface produced GROMACS topology files that were used in molecular dynamics simulations. Molecular dynamics simulations were performed in the Centre for high performance computing (CHPC) cluster under the CHEM0802 project and the trajectories produced were further analysed. In this work not only were hit compounds from SANCDB identified, but also differences in behaviour across species and in the presence or absence of the membrane were described. This highlights the need to include the correct protein environment when studying these systems.Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 202

Molecular Simulation Studies on the Prion Protein Variants: Insights into the Intriguing Effects of Mutations

Prion diseases, or transmissible spongiform encephalopathies (TSE), are a group of rare fatal neurodegenerative maladies that affect humans and animals. The fundamental breakthrough in TSE research was the discovery of the "prion"\u23afproteinaceous infectious particle\u23af and the verification of the \u201cprotein-only\u201d hypothesis, which states that prions could self-propagate by converting the cellular prion protein (PrPC) into the scrapie form, PrPSc (or prions), and lead to neurodegeneration without using any nucleic acids. The concept of prions may unify neurodegenerative diseases under a common pathogenic mechanism. Indeed, growing evidence shows that TSE may share similar pathogenesis with common neurodegenerative syndromes such as Alzheimer\u2019s disease and Parkinson\u2019s disease, for which there are currently no cure. Today, PrP is one of the most studied models for protein misfolding mechanism and TSE serve as an excellent model for studying many other neurodegenerative diseases. Understanding the molecular mechanism of the PrP misfolding process may profoundly influence the development of diagnostics and effective therapies for neurodegenerative diseases in general. Investigating human (Hu) PrP TSE-linked mutations (more than 50 currently identified mutations, linked to ~15% of the cases) may be very instrumental in this respect, as it can provide hints on the molecular basis of the PrPC\u2192PrPSc conversion. These mutations cause spontaneous TSE, which are likely due to modifications in the native structure of PrPC. They are located all over the structure. Polymorphisms (i.e. non-pathogenic, naturally occurring mutations) in the PrP gene have been found to influence the etiology and neuropathology of the disease in both humans and sheep. In transgenic (Tg) mice, artificial mutations can determine the susceptibility to the infection of different prion strains. Intriguingly, mouse (Mo) PrP containing artificial mutations (denoted MoPrP chimera, hereinafter) have very different effects in vitro: some MoPrP chimera were found to resist PrPSc infection, whereas some others did not; some of the resistant MoPrP chimeras even exhibited a protective effect (known as the dominant-negative effect) over the co-expressed endogenous wild-type (WT) MoPrPC. Most mutations are located in the folded globular domain (GD) while fewer are located in the intrinsically disordered N-terminal domain (N-term). The N-term of PrPC has been suggested to serve multiple functions in vivo, which likely relies on the structural flexibility of this domain. Therefore, characterizing the structural features of the N-term is central for investigating not only the mutations in this domain, but also the physiological role of the N-term. Based on previous studies in our lab, in this thesis we first applied molecular dynamics simulations to studying the impact of all the known Hu TSE-linked mutations in HuPrPC GD. We next applied the same approach to study the GD structure of MoPrP chimeras which contain one or two residues from Hu or sheep PrP sequence. By studying these PrP variants, we aim to identify the structural determinants of the mutants that may play a role in the PrPC\u2192PrPSc conversion. Our calculations discovered that these mutants exhibit different structural features from those of the WT PrP GD mainly in two common regions that are likely the \u201chot spots\u201d in the protein misfolding process. These features can be classified into different types that are correlated to the types of mutants (i.e. pathogenic, resistant or dominant-negative), thus hinting to the molecular mechanisms of PrPSc formation and propagation. We have then predicted the structure of the entire PrP N-term and the impact of the Hu TSE-linked mutations in this domain using a novel Monte Carlo-based simulation approach, PROFASI. PROFASI has already shown to provide structural predictions in a disordered protein such as \u3b1-synuclein. Our results are consistent with available experimental data and therefore firmly allow us to provide the first overview on the structural determinants of all Hu TSE-linked mutations in PrP