This dissertation is focused on the application of Markov State Models on protein folding and designing of small drug-like molecules, as well as application of computational tools on the study of biological processes. The central focus of protein folding is to understand how proteins obtain their unique three-dimensional structure from their aminoacid sequences. The function of protein critically depends on its three- dimensional structure; hence, any internal (such as mutations) or external (such as high temperature) perturbation that obstructs three-dimensional structure of a protein will also interfere with its function. Many diseases are associated with inability of protein to form its unique structure. For example, sickle cell anemia is caused by a single mutation that changes glutamic acid to valine. Molecular dynamics (MD) simulations could be utilized to study protein folding and effects of perturbations on protein energy landscape; however, due to its inherent atomic resolution, MD simulations usually provide enormous amount of data even for small proteins. A thorough analysis and extraction of desired information from MD provided data could be extremely challenging and is well beyond human comprehension. Markov state models (MSMs) are proved to be apt for the analysis of large scale random processes and equilibrium conditions, hence it could be applied for protein folding studies. MSMs can be used to obtain long timescale information from short timescale simulations. In other words, the combination of many short simulations and MSMs is a powerful technique to study the folding mechanism of many proteins, even the ones with folding times over millisecond. This dissertation is centered on the use of MSMs and MD simulation in understanding protein folding and biological processes and is constructed as the following. The first chapter provides a brief introduction into MD simulation and the different techniques that could be used to facilitate simulations. Protein folding and its challenges are also discussed in chapter one. Finally, chapter one ends with describing MSMs and technical aspects of building them for protein folding studies. Chapter two is focused on using MD simulations and MSMs to design small protein like molecules to prevent biofilm propagation by disrupting its lifecycle. The biofilm lifecycle and strategy for its interruption is described first. Then, the designed molecules and their conformational sampling by MD simulations are explained. Next, the application of MSMs in obtaining and comparing equilibrium population of all designs are discussed. At the end of chapter two, the molecular descriptions of best designs are explained. Chapter three is focused on the effects of mutations on the energy landscape of a sixteen residue protein from c-terminal hairpin of protein G, GB1. Three mutations, tz4, tz5, and tz6 are discussed, and their folding rates and folding mechanisms are compared with wild-type GB1 using MSMs built from a significantly large MD simulation data set (aggregating over 9 millisecond). Finally, chapter four is focused on the application of MD simulations on understanding the selectivity of Na,K-ATPase, a biologically critical protein that transports sodium ions outside and potassium ions inside against their concentration gradient in almost all eukaryotic cells. Multiple MD approaches, including metadynamics and free energy perturbation methods are used to describe the origins of selectivity for Na,K-ATPase. / Chemistry

It remains a major challenge in biophysics to understand the conformational dynamics of biomolecules. As powerful tools, molecular dynamics (MD) simulations have become increasingly important in studying the full atomic details of conformational dynamics of biomolecules. In addition, many statistical models have been developed to give insight into the big datasets from MD simulations. In this work, I first describe three statistical models used to analyze MD simulation data: Lifson-Roig Helix-Coil theory, Bayesian inference models, and Markov state models. Then I present the applications of each model in analyzing MD simulations and revealing insight into the conformational dynamics of biomolecules. These statistical models allow us to bridge microscopic and macroscopic mechanisms of biological processes and connect simulations with experiments. / Chemistry

GLIC är en pH-känslig pentamerisk ligandstyrd jonkanal (pLGIC) som finns i cellmembranet hos prokaryoten Gloeobacter violaceus. GLIC är en bakteriell homolog till flera receptorer som är viktiga i nervsystemet hos de flesta eukaryotiska organismer. Dessa receptorer fungerar som mallar för utvecklingen av målstyrda bedövnings- och stimulerande läkemedel som påverkar nervsystemet. Förståelsen av ett proteins mekanismer har därför hög prioritet inför läkemedelsutvecklingen. Eukaryota pLGICs är dock mycket komplexa eftersom några av de är heteromera, har flera domäner, och de pågår eftertranslationella ändringar. GLIC, å andra sidan, har en enklare struktur och det räcker att analysera strukturen av en subenhet - eftersom alla subenheter är helt lika. Flertalet möjliga grindmekanismer föreslogs av vetenskapen men riktiga öppningsmekanismen av GLIC är fortfarande oklar. Projektets mål är att genomföra maskininlärning (ML) för att upptäcka nya grindmekanismer med hjälp av datormetoder. Urspungsdatan togs från tidigare forskning där andra ML-redskap såsom molekyldynamik (MD), elastisk nätverksstyrd Brownsk dynamik (eBDIMS) och Markovstillståndsmodeller (MSM) användes. Utifrån dessa redskap simulerades proteinet som vildtyp samt med funktionsförstärkt mutation vid två olika pH värden. Fem makrotillstånd byggdes: två öppna, två stängda och ett mellanliggande. I projektet användes ett annat ML redskap: KL-divergens. Detta redskap användes för att hitta skillnader i avståndfördelning mellan öppet och stängt makrotillstånd. Utifrån ursprungsdatan byggdes en tensor som lagrade alla parvisa aminosyrornas avstånd. Varje aminosyrapar hade sin egen metadata som i sin tur användes för att frambringa alla fem avståndsfördelningar fråm MSMs som byggdes i förväg. Sedan bräknades medel-KL-divergens mellan två avståndfördelningar av intresse för att filtrera bort aminosyropar med överlappande avståndsfördelningar. För att se till att aminosyror inom aminosyrapar som låg kvar kan påverka varandra, filtrerades bort alla par vars minsta och medelavstånd var stora. De kvarvarande aminosyroparen utvärderades i förhållande till alla fem makrotillstånd Viktiga nya grindmekanismer som hittades genom både KL-divergens och makrotillståndsfördelningar innefattade loopen mellan M2-M3 helixarna av en subenhet och både loopen mellan sträckor β8 och β9 (Loop F)/N-terminal β9-sträckan och pre-M1/N-terminal M1 av närliggande subenheten. Loopen mellan sträckor β8 och β9 (Loop F) visade höga KL-värden också med loopen mellan sträckor β1 och β2 loop samt med loopen mellan sträckor β6 och β7 (Pro-loop) och avståndet mellan aminosyror minskade vid kanalens grind. Övriga intressanta grindmekanismer innefattade parning av aminosyror från loopen β4-β5 (Loop A) med aminosyror från sträckor β1 och β6 samt böjning av kanalen porangränsande helix. KL-divergens påvisades vara ett viktigt redskap för att filtrera tillgänglig data och de nya grindmekanismer kan bli användbara både för akademin, som vill reda ut GLIC:s fullständiga grindmekanismer, och läkemedelsföretag, som letar efter bindningsställen inom molekylen för att utveckla nya läkemedel. / GLIC is a transmembrane proton-gated pentameric ligand-gated ion channel (pLGIC) that is found in the prokaryote Gloeobacter violaceus. GLIC is the prokaryotic homolog to several receptors that are found in the nervous system of many eukaryotic organisms. These receptors are targets for the development of pharmaceutical drugs that interfere with the gating of these channels - such drugs involve anesthetics and stimulants. Understanding the mechanism of a drug’s target is a high priority for the development of a novel medicine. However, eukaryotic pLGICs are complex to analyse, because some of them are heteromeric, have more domains, and because of their post-translational modifications (PTMs). GLIC, on the other hand, has a simpler structure and it is enough to study the structure of only one subunit - since all subunits are identical. Several possible gating mechanisms have been proposed by the scientific community, but the complete gating of GLIC remains unclear. The goal of this project is to implement machine learning (ML) to discover novel gating mechanisms by computational approaches. The starting data was extracted from a previous research where computational tools like unbiased molecular dynamics (MD), elastic network-driven Brownian Dynamics (eBDIMS), and Markov state models (MSMs) were used. From those tools, the protein was simulated in wild-type and in a gain-of-function mutation at two different pH values. Five macrostates were constructed: two open, two closed, and an intermediate. In this project another ML tool was used: KL divergence. This tool was used to score the difference between the distance distributions of one open and one closed macrostate. The starting data was used to create a tensor that stored all residue-residue distances. Each residue pair had its own metadata, which in turn was used to yield the distance distributions of all five pre-build MSMs. Then the average KL scores between two states of interest were calculated and were used to filter out the residue pairs with overlapping distance distributions. To make sure that the residues within a pair can interact with each other, all residue pairs with very high minimum and average distance were filtered out as well. The residue pairs that remained were later evaluated across all five macrostates for further studies. Important novel mechanisms discovered in this project through both the KL divergence and the macrostate distributions involved the M2-M3 loop of one subunit and both the β8-β9 loop/N-terminal β9 strand and the preM1/N-terminal M1 region of the neighboring subunit. The β8-β9 loop (Loop F) showed high KL scores with the β1-β2 and β6-β7 (Pro-loop) loops as well with decreasing distances upon the channel’s opening. Other notable gating mechanisms involved are the pairing of residues from the β1-β2 loop (Loop A) with residues from the strands β1 and β6, as well as the kink of the pore-lining helix. KL divergence proved a valuable tool to filter available data and the novel mechanisms can prove useful both to the academic community that seeks to unravel the complete gating mechanism of GLIC and to the pharmaceutical companies that search for new binding sites within the molecule for new drugs.

Conformational dynamics are important to the function of biological molecules. While many experimental techniques (e.g. X-ray crystallography and NMR spectroscopy) have been developed for providing the structure of functional conformations, it is exceptionally challenging to understand conformational dynamics from experimental characterization. Molecular dynamics (MD) simulations is a powerful tool for probing conformational dynamics. The timescale resolution of MD simulations enables people to investigate intermediate conformations and transition pathways in atomic detail. Recent advancements in computer hardware have increased the timescales accessible to MD simulations. Meanwhile, more accurate and specific force fields have been developed to accurately model a variety biological system of different sizes. My graduate research has been focused on using MD simulations to study the conformational dynamics of proteins. Markov State Model (MSM) based approaches are extensively applied to investigate a variety of folding and/or binding mechanisms in atomic detail. Another focus of my work has been developing a Bayesian inference-based approach called BICePs to reconcile experimental measurements with simulation data to determine conformational ensembles and to validate force fields. / Chemistry