Transiently Transfected Purine Biosynthetic Enzymes Form Stress Bodies

It has been hypothesized that components of enzymatic pathways might organize into intracellular assemblies to improve their catalytic efficiency or lead to coordinate regulation. Accordingly, de novo purine biosynthesis enzymes may form a purinosome in the absence of purines, and a punctate intracellular body has been identified as the purinosome. We investigated the mechanism by which human de novo purine biosynthetic enzymes might be organized into purinosomes, especially under differing cellular conditions. Irregardless of the activity of bodies formed by endogenous enzymes, we demonstrate that intracellular bodies formed by transiently transfected, fluorescently tagged human purine biosynthesis proteins are best explained as protein aggregation.This work was supported by grants from the United States National Institutes of Health, National Science Foundation, and Welch (F1515) and Packard Foundations to EMM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Cellular and Molecular Biolog

A Self-Assembling Lanthanide Molecular Nanoparticle for Optical Imaging

Chromophores that incorporate f-block elements have considerable potential for use in bioimaging applications because of their advantageous photophysical properties compared to organic dye, which are currently widely used. We are developing new classes of lanthanide-based self-assembling molecular nanoparticles as reporters for imaging and as multi-functional nanoprobes or nanosensors for use with biological samples. One class of these materials, which we call lanthanide "nano-drums", are homogeneous 4d-4f clusters approximately 25 to 30 angstrom in diameter. These are capable of emitting from the visible to near-infrared wavelengths. Here, we present the synthesis, crystal structure, photophysical properties and comparative cytotoxicity data for a 32 metal Eu-Cd nano-drum [Eu8Cd24L12(OAc)(48)] (1). We also explored the imaging capabilities of this nano-drum using epifluorescence, TIRF, and two-photon microscopy platforms.Welch Foundation F-816, F-1018, F1515Ministry of High Education (MOHE), Malaysia under High Impact Research (HIR) - MOHE project UM.C/625/1/HIR/MoE/CHAN/13/6 H-50001-00-A000034NIH/NIAID 1U01AI078008-3Centre for Blast Injury Study at Imperial College LondonCPRIT R1003NIH-NCI CA68682National Institutes of HealthNational Science FoundationCancer Prevention Research Institute of TexasNational Science Foundation CHE-0741973Chemistr

Single molecule peptide sequencing

The proteome is a highly dynamic and complex set of proteins, specific not only to a particular organism, but to cell types and environmental conditions. Understanding proteome changes as they occur is especially important for molecular diagnostics and developing biomarkers. Currently, the primary technology for proteome wide identification and quantification is shotgun mass spectrometry; while powerful, it lacks high sensitivity and coverage. In this dissertation, I discuss my work in the development of a new technology, termed “fluorosequencing”, for sequencing peptides from a complex protein sample at the level of single molecules. The concept is to generate a positional information pattern of an amino acid(s) (such as xKxxK, where K is lysine and x can be any of the other amino acid residues). In order to obtain such a pattern, we proposed a scheme of (i) selectively labeling one or more amino acid(s) in the peptides, (ii) immobilizing millions of these individual fluorescently labeled peptides on a glass surface, (iii) monitoring their changing fluorescent pattern by TIRF microscopy as the (iv) N-terminal amino acid is sequentially cleaved by Edman chemistry and (v) using the resulting fluorescent signature (fluorosequences) to uniquely identify individual single molecule peptides in the mixture. We began by developing a computational framework to justify the feasibility of the concept. By modeling different sources of anticipated errors, we showed that the errors do not greatly affect the identification of proteins in the human proteome. Secondly, after screening fluorophores for their solvent stability, we used fluorescently labeled synthetic peptides covalently immobilized on beads to experimentally demonstrate the ability of the technique to determine the position of the fluorescently labeled residue in peptides. Finally, we translated the bead optimized chemistry procedures to a single molecule setup. We implemented the fluorosequencing method to sequence synthetic peptide molecules and provided evidence for the technique’s utility to discriminate peptides in a peptide mixture with single molecule sensitivity. By establishing the foundational work towards the proof-of-principle for fluorosequencing, we can now scale the method in order to realize the idea of single molecule proteome wide sequencing.Cellular and Molecular Biolog

Raw data for figures used in manuscript - Highly parallelized, single molecule sequencing and identification of proteins

<p>Raw Image files used for generating the figures (Fig 2, Fig3, Fig4, Fig5 and Fig6, Supplementary figures, files needed for background subtraction and image processing tutorial (docker image)) in the manuscript - Highly parallelized, single molecule sequencing and identification of proteins</p> <p>Use command tar xfz[v] *.tar.gz to retain the file structure.  </p> <p>Docker image in image processing tutorial works on Linux platforms only.</p> <p>File structure after un-compressing each *.tar.gz is as follows - </p> <p>1. acetylated_background_signalsFiles.tar.gz</p> <p>    - Folders for the different experiments with the name expt[1..30]</p> <p>             - *SIGNALS.pkl - Pickle file (python encoded) containing the information of the histogram of the peptide step-drops</p> <p>     - acetylated_backgroundFiles_list.csv (file formatted for performing iterative_background.py)</p> <p>     - README.txt (information on the contents and the use of the files in the directory)</p> <p>2. fig2.tar.gz</p> <p>     - fig2A/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig2B/ (contains raw image folders, processed_results and README.txt)</p> <p>2. fig3and4.tar.gz</p> <p>     - acPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p>     - bocPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p>     - README.txt</p> <p>3. fig5.tar.gz</p> <p>     - fig5A_panel1/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5A_panel2/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5B_A2/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5B_A3/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5B_B1/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5B_B2/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5C/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5D/ (contains raw image folders, processed_results and README.txt)</p> <p>5. fig6.tar.gz</p> <p>     - fig6B_top/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig6B_bottom/ (contains raw image folders, processed_results and README.txt)</p> <p>6. fig_supplementary09.tar.gz</p> <p>     - (contains raw image folders, processed_results and README.txt)</p> <p>7. fig_supplementart14.tar.gz</p> <p>     - supplementary_fig14A/ (contains raw image folders, processed_results and README.txt)</p> <p>     - supplementary_fig14B/ (contains raw image folders, processed_results and README.txt)</p> <p>8. imageProcessingTutorial.tar.gz</p> <p>     - walkthrough_docker_image.tar.xz (contains the docker image with necessary code pre-installed. Includes small example dataset; Works only in linux docker and not macOS)</p> <p>     - README.txt (information on the image processing tutorial). </p

Raw data for figures used in manuscript - Highly parallelized, single molecule sequencing and identification of proteins

<p>Raw Image files used for generating the figures (Fig 2, Fig3, Fig4, Fig5 and Fig6, Supplementary figures, files needed for background subtraction and image processing tutorial (docker image)) in the manuscript - Highly parallelized, single molecule sequencing and identification of proteins</p> <p>Use command tar xfz[v] *.tar.gz to retain the file structure.  </p> <p>Docker image in image processing tutorial works on Linux platforms only.</p> <p>File structure after un-compressing each *.tar.gz is as follows - </p> <p>1. acetylated_background_signalsFiles.tar.gz</p> <p>    - Folders for the different experiments with the name expt[1..30]</p> <p>             - *SIGNALS.pkl - Pickle file (python encoded) containing the information of the histogram of the peptide step-drops</p> <p>     - acetylated_backgroundFiles_list.csv (file formatted for performing iterative_background.py)</p> <p>     - README.txt (information on the contents and the use of the files in the directory)</p> <p>2. fig2.tar.gz</p> <p>     - fig2A/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig2B/ (contains raw image folders, processed_results and README.txt)</p> <p>2. fig3and4.tar.gz</p> <p>     - acPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p>     - bocPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p>     - README.txt</p> <p>3. fig5.tar.gz</p> <p>     - fig5A_panel1/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5A_panel2/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5B_A2/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5B_A3/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5B_B1/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5B_B2/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5C/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig5D/ (contains raw image folders, processed_results and README.txt)</p> <p>5. fig6.tar.gz</p> <p>     - fig6B_top/ (contains raw image folders, processed_results and README.txt)</p> <p>     - fig6B_bottom/ (contains raw image folders, processed_results and README.txt)</p> <p>6. fig_supplementary09.tar.gz</p> <p>     - (contains raw image folders, processed_results and README.txt)</p> <p>7. fig_supplementart14.tar.gz</p> <p>     - supplementary_fig14A/ (contains raw image folders, processed_results and README.txt)</p> <p>     - supplementary_fig14B/ (contains raw image folders, processed_results and README.txt)</p> <p>8. imageProcessingTutorial.tar.gz</p> <p>     - walkthrough_docker_image.tar.xz (contains the docker image with necessary code pre-installed. Includes small example dataset; Works only in linux docker and not macOS)</p> <p>     - README.txt (information on the image processing tutorial). </p

Raw data for figures used in manuscript - Highly parallelized, single molecule sequencing and identification of proteins

<p>Raw Image files used for generating the figures (Fig 2, Fig3, Fig4, Supplementary figure, files needed for background subtraction and image processing tutorial (docker image)) in the manuscript - Highly parallelized, single molecule sequencing and identification of proteins</p> <p>Use command tar xfz[v] *.tar.gz to retain the file structure.  </p> <p>File structure after un-compressing each *.tar.gz is as follows - </p> <p>1. acetylated_background_signalsFiles.tar.gz</p> <p>    - Folders for the different experiments with the name expt[1..30]</p> <p>             - *SIGNALS.pkl - Pickle file (python encoded) containing the information of the histogram of the peptide step-drops</p> <p>     - acetylated_backgroundFiles_list.csv (file formatted for performing iterative_background.py)</p> <p>     - README.txt (information on the contents and the use of the files in the directory)</p> <p>2. fig2.tar.gz</p> <p>     - fig2B.tar.gz (contains raw image folders, processed_results and README.txt)</p> <p>     - fig2C.tar.gz (contains raw image folders, processed_results and README.txt)</p> <p>2. fig3.tar.gz</p> <p>     - fig3_acPeptide_label-2-5.tar.gz (contains raw image folders, processed_results)</p> <p>     - fig3_bocPeptide_label-2-5.tar.gz ((contains raw image folders, processed_results)</p> <p>     - README.txt</p> <p>3. fig4.tar.gz</p> <p>     - fig4A_panel1.tar.gz (contains raw image folders, processed_results and README.txt)</p> <p>     - fig4A_panel2.tar.gz (contains raw image folders, processed_results and README.txt)</p> <p>     - fig4B_A2.tar.gz (contains raw image folders, processed_results and README.txt)</p> <p>     - fig4B_A3.tar.gz (contains raw image folders, processed_results and README.txt)</p> <p>     - fig4B_B1.tar.gz (contains raw image folders, processed_results and README.txt)</p> <p>     - fig4B_B2.tar.gz (contains raw image folders, processed_results and README.txt)</p> <p>     - fig4C.tar.gz (contains raw image folders, processed_results and README.txt)</p> <p>4. imageProcessingTutorial.tar.gz</p> <p>     - walkthrough_docker_image.tar.xz (contains the docker image with necessary code pre-installed. Includes small example dataset)</p> <p>     - README.txt (information on the image processing tutorial). </p

Peptide Fragment Ion Analyser (PFIA): a simple and versatile tool for the interpretation of tandem mass spectrometric data and de novo sequencing of peptides

The software Peptide Fragment Ion Analyser (PFIA) aids in the analysis and interpretation of tandem mass spectrometric data of peptides. The software package has been designed to facilitate the analysis of product ions derived from acyclic and cyclic peptide natural products that possess unusual amino acid residues and are heavily post-translationally modified. The software consists of two programmes: (a) PFIA-I lists the amino acid compositions and their corresponding product ion types for a queried m/z value' (z = +1) and (b) PFIA-II displays fragmentation pattern iagram(s) and lists all sequence-specific product ion types for' the protonated adduct of 'a queried sequence'. The unique feature of PFIA-II is its ability to handle cyclic peptides. The two programmes used in combination can prove helpful for deriving peak assignments in the de novo sequencing of novel peptides

A theoretical justification for single molecule peptide sequencing.

The proteomes of cells, tissues, and organisms reflect active cellular processes and change continuously in response to intracellular and extracellular cues. Deep, quantitative profiling of the proteome, especially if combined with mRNA and metabolite measurements, should provide an unprecedented view of cell state, better revealing functions and interactions of cell components. Molecular diagnostics and biomarker discovery should benefit particularly from the accurate quantification of proteomes, since complex diseases like cancer change protein abundances and modifications. Currently, shotgun mass spectrometry is the primary technology for high-throughput protein identification and quantification; while powerful, it lacks high sensitivity and coverage. We draw parallels with next-generation DNA sequencing and propose a strategy, termed fluorosequencing, for sequencing peptides in a complex protein sample at the level of single molecules. In the proposed approach, millions of individual fluorescently labeled peptides are visualized in parallel, monitoring changing patterns of fluorescence intensity as N-terminal amino acids are sequentially removed, and using the resulting fluorescence signatures (fluorosequences) to uniquely identify individual peptides. We introduce a theoretical foundation for fluorosequencing and, by using Monte Carlo computer simulations, we explore its feasibility, anticipate the most likely experimental errors, quantify their potential impact, and discuss the broad potential utility offered by a high-throughput peptide sequencing technology

Identifying peptides at the single molecule level

The present invention relates to methods for identifying amino acids in peptides. In one embodiment, the present invention contemplates labeling the N-terminal amino acid with a first label and labeling an internal amino acid with a second label. In some embodiments, the labels are fluorescent labels. In other embodiments, the internal amino acid is lysine. In other embodiments, amino acids in peptides are identified based on the fluorescent signature for each peptide at the single molecule level.Board of Regents, University of Texas Syste

Surface plots illustrate the consequences of differing rates of Edman efficiency, photobleaching, and fluorophore failure rates.

<p>Each panel summarizes the consequences of varying rates of photobleaching and Edman failures for a different fixed fluorophore failure rate, ranging from 0% to 25%, as calculated after simulating 30 experimental cycles on the complete human proteome at a simulation depth of 10,000 copies per protein. Photobleaching shows the strongest negative impact on proteome coverage when compared to other errors; increasing the number of distinguishable labels strongly increases proteome coverage. Labeling and immobilization schemes are denoted as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004080#pcbi.1004080.g002" target="_blank">Fig. 2</a>. For comparison, literature evidence suggests that common failure rates of fluorophores may be about 15–20% [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004080#pcbi.1004080.ref018" target="_blank">18</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004080#pcbi.1004080.ref032" target="_blank">32</a>], Edman degradation proceeds with about 94% efficiency [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004080#pcbi.1004080.ref033" target="_blank">33</a>], and the mean photobleaching lifetime of a typical Atto680 dye is about 30 minutes [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004080#pcbi.1004080.ref023" target="_blank">23</a>], corresponding to 1800 Edman cycles, assuming 1 sec exposure per Edman cycle. Thus, we expect error rates to be sufficiently low for effective fluorosequencing.</p