ANALYSIS AND SIMULATION OF TANDEM MASS SPECTROMETRY DATA

This dissertation focuses on improvements to data analysis in mass spectrometry-based proteomics, which is the study of an organism’s full complement of proteins. One of the biggest surprises from the Human Genome Project was the relatively small number of genes (~20,000) encoded in our DNA. Since genes code for proteins, scientists expected more genes would be necessary to produce a diverse set of proteins to cover the many functions that support the complexity of life. Thus, there is intense interest in studying proteomics, including post-translational modifications (how proteins change after translation from their genes), and their interactions (e.g. proteins binding together to form complex molecular machines) to fill the void in molecular diversity. The goal of mass spectrometry in proteomics is to determine the abundance and amino acid sequence of every protein in a biological sample. A mass spectrometer can determine mass/charge ratios and abundance for fragments of short peptides (which are subsequences of a protein); sequencing algorithms determine which peptides are most likely to have generated the fragmentation patterns observed in the mass spectrum, and protein identity is inferred from the peptides. My work improves the computational tools for mass spectrometry by removing limitations on present algorithms, simulating mass spectroscopy instruments to facilitate algorithm development, and creating algorithms that approximate isotope distributions, deconvolve chimeric spectra, and predict protein-protein interactions. While most sequencing algorithms attempt to identify a single peptide per mass spectrum, multiple peptides are often fragmented together. Here, I present a method to deconvolve these chimeric mass spectra into their individual peptide components by examining the isotopic distributions of their fragments. First, I derived the equation to calculate the theoretical isotope distribution of a peptide fragment. Next, for cases where elemental compositions are not known, I developed methods to approximate the isotope distributions. Ultimately, I created a non-negative least squares model that deconvolved chimeric spectra and increased peptide-spectrum-matches by 15-30%. To improve the operation of mass spectrometer instruments, I developed software that simulates liquid chromatography-mass spectrometry data and the subsequent execution of custom data acquisition algorithms. The software provides an opportunity for researchers to test, refine, and evaluate novel algorithms prior to implementation on a mass spectrometer. Finally, I created a logistic regression classifier for predicting protein-protein interactions defined by affinity purification and mass spectrometry (APMS). The classifier increased the area under the receiver operating characteristic curve by 16% compared to previous methods. Furthermore, I created a web application to facilitate APMS data scoring within the scientific community.Doctor of Philosoph

MSAcquisitionSimulator: data-dependent acquisition simulator for LC-MS shotgun proteomics

Summary: Data-dependent acquisition (DDA) is the most common method used to control the acquisition process of shotgun proteomics experiments. While novel DDA approaches have been proposed, their evaluation is made difficult by the need of programmatic control of a mass spectrometer. An alternative is in silico analysis, for which suitable software has been unavailable. To meet this need, we have developed MSAcquisitionSimulator—a collection of C ++ programs for simulating ground truth LC-MS data and the subsequent application of custom DDA algorithms. It provides an opportunity for researchers to test, refine and evaluate novel DDA algorithms prior to implementation on a mass spectrometer

Enhancing structure relaxations for first-principles codes: an approximate Hessian approach

We present a method for improving the speed of geometry relaxation by using a harmonic approximation for the interaction potential between nearest neighbor atoms to construct an initial Hessian estimate. The model is quite robust, and yields approximately a 30% or better reduction in the number of calculations compared to an optimized diagonal initialization. Convergence with this initializer approaches the speed of a converged BFGS Hessian, therefore it is close to the best that can be achieved. Hessian preconditioning is discussed, and it is found that a compromise between an average condition number and a narrow distribution in eigenvalues produces the best optimization.Comment: 9 pages, 3 figures, added references, expanded optimization sectio

Enabling hotspot detection and public health response to the COVID-19 pandemic

INTRODUCTION: Public-facing maps of COVID-19 cases, hospital admissions, and deaths are commonly displayed at the state, county, and zip code levels, and low case counts are suppressed to protect confidentiality. Public health authorities are tasked with case identification, contact tracing, and canvasing for educational purposes during a pandemic. Given limited resources, authorities would benefit from the ability to tailor their efforts to a particular neighborhood or congregate living facility. METHODS: We describe the methods of building a real-time visualization of patients with COVID-19-positive tests, which facilitates timely public health response to the pandemic. We developed an interactive street-level visualization that shows new cases developing over time and resolving after 14 days of infection. Our source data included patient demographics (ie, age, race and ethnicity, and sex), street address of residence, respiratory test results, and date of test. RESULTS: We used colored dots to represent infections. The resulting animation shows where new cases developed in the region and how patterns changed over the course of the pandemic. Users can enlarge specific areas of the map and see street-level detail on residential location of each case and can select from demographic overlays and contour mapping options to see high-level patterns and associations with demographics and chronic disease prevalence as they emerge. CONCLUSIONS: Before the development of this tool, local public health departments in our region did not have a means to map cases of disease to the street level and gain real-time insights into the underlying population where hotspots had developed. For privacy reasons, this tool is password-protected and not available to the public. We expect this tool to prove useful to public health departments as they navigate not only COVID-19 pandemic outcomes but also other public health threats, including chronic diseases and communicable disease outbreaks

The Dark Kinase Knowledgebase: An online compendium of knowledge and experimental results of understudied kinases

Kinases form the backbone of numerous cell signaling pathways, with their dysfunction similarly implicated in multiple pathologies. Further facilitated by their druggability, kinases are a major focus of therapeutic development efforts in diseases such as cancer, infectious disease and autoimmune disorders. While their importance is clear, the role or biological function of nearly one-third of kinases is largely unknown. Here, we describe a data resource, the Dark Kinase Knowledgebase (DKK; https://darkkinome.org), that is specifically focused on providing data and reagents for these understudied kinases to the broader research community. Supported through NIH\u27s Illuminating the Druggable Genome (IDG) Program, the DKK is focused on data and knowledge generation for 162 poorly studied or \u27dark\u27 kinases. Types of data provided through the DKK include parallel reaction monitoring (PRM) peptides for quantitative proteomics, protein interactions, NanoBRET reagents, and kinase-specific compounds. Higher-level data is similarly being generated and consolidated such as tissue gene expression profiles and, longer-term, functional relationships derived through perturbation studies. Associated web tools that help investigators interrogate both internal and external data are also provided through the site. As an evolving resource, the DKK seeks to continually support and enhance knowledge on these potentially high-impact druggable targets

TRIM67 regulates exocytic mode and neuronal morphogenesis via SNAP47

Neuronal morphogenesis involves dramatic plasma membrane expansion, fueled by soluble N-ethylmaleimide-sensitive factor attachment protein eceptors (SNARE)-mediated exocytosis. Distinct fusion modes described at synapses include full-vesicle fusion (FVF) and kiss-and-run fusion (KNR). During FVF, lumenal cargo is secreted and vesicle membrane incorporates into the plasma membrane. During KNR, a transient fusion pore secretes cargo but closes without membrane addition. In contrast, fusion modes are not described in developing neurons. Here, we resolve individual exocytic events in developing murine cortical neurons and use classification tools to identify four distinguishable fusion modes: two FVF-like modes that insert membrane material and two KNR-like modes that do not. Discrete fluorescence profiles suggest distinct behavior of the fusion pore. Simulations and experiments agree that FVF-like exocytosis provides sufficient membrane material for morphogenesis. We find the E3 ubiquitin ligase TRIM67 promotes FVF-like exocytosis in part by limiting incorporation of the Qb/Qc SNARE SNAP47 into SNARE complexes and, thus, SNAP47 involvement in exocytosis

Spotlite: Web Application and Augmented Algorithms for Predicting Co-Complexed Proteins from Affinity Purification – Mass Spectrometry Data

Protein-protein interactions defined by affinity purification and mass spectrometry (APMS) approaches suffer from high false discovery rates. Consequently, the candidate interaction lists must be pruned of contaminants before network construction and interpretation, historically an expensive and time-intensive task. In recent years, numerous computational methods have been developed to identify genuine interactions from hundreds revealed by APMS experiments. Here, comparative analysis of several popular algorithms revealed complementarity in their classification accuracies, which is supported by their divergent scoring strategies. As such, we used two accurate and computationally efficient methods as features for machine learning using the Random Forest algorithm. Additionally, we developed novel mathematical models to include a variety of indirect data, such as mRNA co-expression, gene ontologies and homologous protein interactions as features within the classification problem. We show that our method, which we call Spotlite, outperforms existing methods on four diverse and public APMS datasets. Because implementation of existing APMS scoring methods requires computational expertise beyond many laboratories, we created a user-friendly and fast web application for APMS data scoring, analysis, annotation and network visualization, for use on new and existing data (http://152.19.87.94:8080/spotlite). The utility of Spotlite and its visualization platform for revealing physical, functional and disease-relevant characteristics within APMS data is established through a focused analysis of the KEAP1 E3 ubiquitin ligase

Wnt regulation: Exploring Axin-Disheveled interactions and defining mechanisms by which the SCF E3 ubiquitin ligase is recruited to the destruction complex

Wnt signaling plays key roles in embryonic development and adult stem cell homeostasis and is altered in human cancer. Signaling is turned on and off by regulating stability of the effector β-catenin (β-cat). The multiprotein destruction complex binds and phosphorylates β-cat and transfers it to the SCF-TrCP E3-ubiquitin ligase for ubiquitination and destruction. Wnt signals act though Dishevelled to turn down the destruction complex, stabilizing β-cat. Recent work clarified underlying mechanisms, but important questions remain. We explore β-cat transfer from the destruction complex to the E3 ligase, and test models suggesting Dishevelled and APC2 compete for association with Axin. We find that Slimb/TrCP is a dynamic component of the destruction complex biomolecular condensate, while other E3 proteins are not. Recruitment requires Axin and not APC, and Axin\u27s RGS domain plays an important role. We find that elevating Dishevelled levels i

Ligands and media impact interactions between engineered nanomaterials and clay minerals

The exponential growth in technologies incorporating engineered nanomaterials (ENMs) requires plans to handle waste ENM disposal and accidental environmental release throughout the material life cycle. These scenarios motivate efforts to quantify and model ENM interactions with diverse background particles and solubilized chemical species in a variety of environmental systems. In this study, quantum dot (QD) nanoparticles and clay minerals were mixed in a range of water chemistries in order to develop simple assays to predict aggregation trends. CdSe QDs were used as a model ENM functionalized with either negatively charged or zwitterionic small molecule ligand coatings, while clays were chosen as an environmentally relevant sorbent given their potential as an economical water treatment technology and ubiquitous presence in nature. In our unbuffered experimental systems, clay type impacted pH, which resulted in a change in zwitterionic ligand speciation that favored aggregation with kaolinite more than with montmorillonite. With kaolinite, the zwitterionic ligand-coated QD exhibited greater than ten times the relative attachment efficiency for QD-clay heteroaggregation compared to the negatively charged ligand coated QD. Under some conditions, particle oxidative dissolution and dynamic sorption of ions and QDs to surfaces complicated the interpretation of the removal kinetics. This work demonstrates that QDs stabilized by small molecule ligands and electrostatic surface charges are highly sensitive to changes in water chemistry in complex media. Natural environments enable rapid dynamic physicochemical changes that will influence the fate and mobility of ENMs, as seen by the differential adsorption of water-soluble QDs to our clay media.Accepted manuscrip