In silico assessment of a novel single-molecule protein fingerprinting method employing fragmentation and nanopore detection

Summary: The identification of proteins at the single-molecule level would open exciting new venues in biological research and disease diagnostics. Previously, we proposed a nanopore-based method for protein identification called chop-n-drop fingerprinting, in which the fragmentation pattern induced and measured by a proteasome-nanopore construct is used to identify single proteins. In the simulation study presented here, we show that 97.1% of human proteome constituents are uniquely identified under close to ideal measuring circumstances, using a simple alignment-based classification method. We show that our method is robust against experimental error, as 69.4% can still be identified if the resolution is twice as low as currently attainable, and 10% of proteasome restriction sites and protein fragments are randomly ignored. Based on these results and our experimental proof of concept, we argue that chop-n-drop fingerprinting has the potential to make cost-effective single-molecule protein identification feasible in the near future

Full-length single-molecule protein fingerprinting

Proteins are the primary functional actors of the cell. While proteoform diversity is known to be highly biologically relevant, current protein analysis methods are of limited use for distinguishing proteoforms. Mass spectrometric methods, in particular, often provide only ambiguous information on post-translational modification sites, and sequences of co-existing modifications may not be resolved. Here we demonstrate fluorescence resonance energy transfer (FRET)-based single-molecule protein fingerprinting to map the location of individual amino acids and post-translational modifications within single full-length protein molecules. Our data show that both intrinsically disordered proteins and folded globular proteins can be fingerprinted with a subnanometer resolution, achieved by probing the amino acids one by one using single-molecule FRET via DNA exchange. This capability was demonstrated through the analysis of alpha-synuclein, an intrinsically disordered protein, by accurately quantifying isoforms in mixtures using a machine learning classifier, and by determining the locations of two O-GlcNAc moieties. Furthermore, we demonstrate fingerprinting of the globular proteins Bcl-2-like protein 1, procalcitonin and S100A9. We anticipate that our ability to perform proteoform identification with the ultimate sensitivity may unlock exciting new venues in proteomics research and biomarker-based diagnosis

Full-Length Single-Molecule Protein Fingerprinting

Proteins are the primary functional actors of the cell. Hence, their identification is pivotal to advance our understanding of cell biology and disease. Current protein analysis methods are of limited use for distinguishing proteoforms. In particular, mass spectrometric methods often provide only ambiguous information on post-translational modification sites, and sequences of co-existing modifications may not be resolved. Here we demonstrate FRET-based single-molecule protein fingerprinting to map the location of individual amino acids and a post-translational modification within single full-length protein molecules. Using an approach that relies on transient binding of fluorescently labeled DNA strands to probe the amino acids on a protein one by one we show that we can fingerprint intrinsically disordered proteins as well as folded globular proteins with sub-nanometer resolution. We anticipate that this technology will be used for proteoform identification in biological and translational research with ultimate sensitivity

Effect of angiotensin II-induced changes in perfusion flow rate on chlorothiazide transport in the isolated perfused rat kidney

Angiotensin II was used as a probe to study the effect of changes in perfusate flow rate on the renal clearance parameters of chlorothiazide in the isolated perfused rat kidney. Perfusion studies were performed in five rats with no angiotensin II present in the perfusate and in five rats with a 1–4 ng/min infusion of angiotensin II into the perfusate. Angiotensin II had a dramatic effect on the renal hemodynamics, resulting in a 43% decrease in perfusate flow, a 16% decrease in glomerular filtration rate (GFR), and a 45% increase in filtration fraction. Values for the fractional excretion of glucose were low and consistent, with or without angiotensin II. Although the unbound fraction (fu) of chlorothiazide was unchanged between treatments, the renal (CL r ) and the secretion clearances were reduced by about 50% in the presence of angiotensin II; the excretion ratio [ER=CL r /(fu·GFR)] was reduced by 38% with angiotensin II present in the perfusate. Analysis of the data was complicated by the presence of a capacity-limited transport for renal tubular secretion. Transport parameters (±SD) were obtained and the corrected intrinsic secretory clearance [(V max /GFR)/K m ] of chlorothiazide was 123 ± 18 without angiotensin II vs. 72.8 ± 30.0 with angiotensin II. These results demonstrate that alterations in organ perfusion can significantly reduce the clearance parameters of chlorothiazide in the rat IPK. These flow-induced changes in intrinsic secretory transport may reflect perturbations other than that of perfusion flow rate alone .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45047/1/10928_2005_Article_BF01071001.pd

cvdelannoy/FRET_X_fingerprinting_simulation: v2.0

<p>Added:</p> <ul> <li>New FRET fingerprinting approach: FRET XY fingerprinting (with @jzftran )</li> <li>FRET fingerprint classification for aSyn mutants</li> </ul> <h2>What's Changed</h2> <ul> <li>Helper functions for propka by @jzftran in https://github.com/cvdelannoy/FRET_X_fingerprinting_simulation/pull/1</li> <li>Fretxy by @cvdelannoy in https://github.com/cvdelannoy/FRET_X_fingerprinting_simulation/pull/3</li> <li>filter_mmcif by @jzftran in https://github.com/cvdelannoy/FRET_X_fingerprinting_simulation/pull/2</li> </ul> <h2>New Contributors</h2> <ul> <li>@jzftran made their first contribution in https://github.com/cvdelannoy/FRET_X_fingerprinting_simulation/pull/1</li> <li>@cvdelannoy made their first contribution in https://github.com/cvdelannoy/FRET_X_fingerprinting_simulation/pull/3</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/cvdelannoy/FRET_X_fingerprinting_simulation/compare/v1.0...v2.0</p&gt

The long reads ahead : De novo genome assembly using the MinION

Nanopore technology provides a novel approach to DNA sequencing that yields long, label-free reads of constant quality. The first commercial implementation of this approach, the MinION, has shown promise in various sequencing applications. This review gives an up-to-date overview of the MinION's utility as a de novo sequencing device. It is argued that the MinION may allow for portable and affordable de novo sequencing of even complex genomes in the near future, despite the currently error-prone nature of its reads. Through continuous updates to the MinION hardware and the development of new assembly pipelines, both sequencing accuracy and assembly quality have already risen rapidly. However, this fast pace of development has also lead to a lack of overview of the expanding landscape of analysis tools, as performance evaluations are outdated quickly. As the MinION is approaching a state of maturity, its user community would benefit from a thorough comparative benchmarking effort of de novo assembly pipelines in the near future. An earlier version of this article can be found on bioRxiv

poreTally: run and publish de novo nanopore assembler benchmarks

Summary: Nanopore sequencing is a novel development in nucleic acid analysis. As such, nanopore-sequencing hardware and software are updated frequently and extensively, which quickly renders peer-reviewed publications on analysis pipeline benchmarking efforts outdated. To provide the user community with a faster, more flexible alternative to peer-reviewed benchmark papers forde novo assembly tool performance we constructed poreTally, a comprehensivebenchmarking tool. poreTally automatically assembles a given read set using several often-used assembly pipelines, analyzes the resulting assemblies for correctness and continuity, and finally generates a quality report, which can immediately be published on Github/Gitlab.Availability and implementation: poreTally is available on Github at https://github.com/cvdelannoy/poreTally, under an MIT license

Single-molecule fret for protein characterization

The invention provides an analysis method (100) for characterization of a tagged protein (10) using FRET donor-acceptor pair chromophores (20), wherein the FRET donor- acceptor pair chromophores (20) comprise a first chromophore (21) and a second chromophore (22), wherein the FRET donor-acceptor pair chromophores (20) have a donor excitation radiation range, a donor emission radiation range and an acceptor emission radiation range, wherein one of the FRET donor-acceptor pair chromophores (23) is excitable by donor excitation radiation (51) in the donor excitation radiation range, wherein the other of the FRET donor-acceptor pair chromophores (24) is configured to provide acceptor emission radiation (54) in the acceptor emission radiation range upon excitation with donor excitation radiation (51) in the donor excitation radiation range of the one of the FRET donor-acceptor pair chromophores (23) when the first chromophore (21) and the second chromophore (22) are configured within a predetermined distance, wherein the tagged protein (10) comprises a first amino acid (11) tagged with a first tag (31) and a second amino acid (12) tagged with a second tag (32), wherein the first tag (31) comprises the first chromophore (21) or is associated to the first chromophore (21), wherein the second tag (32) comprises an oligonucleotide, and wherein the analysis method (100) comprises: a barcode exposure stage (110) comprising: (i) exposing the tagged protein (10) to a barcode (42), wherein the barcode (42) is configured to hybridize with the second tag (32), and wherein the barcode (42) comprises the second chromophore (22), (ii) providing radiation having a wavelength selected from the donor excitation radiation range to the tagged protein (10), and (iii) measuring emission in the donor emission radiation range and the acceptor emission radiation range to provide an emission signal.</p

iMAX FRET (Information Maximized FRET) for Multipoint Single-Molecule Structural Analysis.

Understanding the structure of biomolecules is vital for deciphering their roles in biological systems. Single-molecule techniques have emerged as alternatives to conventional ensemble structure analysis methods for uncovering new biology in molecular dynamics and interaction studies, yet only limited structural information could be obtained experimentally. Here, we address this challenge by introducing iMAX FRET, a one-pot method that allows ab initio 3D profiling of individual molecules using two-color FRET measurements. Through the stochastic exchange of fluorescent weak binders, iMAX FRET simultaneously assesses multiple distances on a biomolecule within a few minutes, which can then be used to reconstruct the coordinates of up to four points in each molecule, allowing structure-based inference. We demonstrate the 3D reconstruction of DNA nanostructures, protein quaternary structures, and conformational changes in proteins. With iMAX FRET, we provide a powerful approach to advance the understanding of biomolecular structure by expanding conventional FRET analysis to three dimensions

baseLess: lightweight detection of sequences in raw MinION data

With its candybar form factor and low initial investment cost, the MinION brought affordable portable nucleic acid analysis within reach. However, translating the electrical signal it outputs into a sequence of bases still requires mid-tier computer hardware, which remains a caveat when aiming for deployment of many devices at once or usage in remote areas. For applications focusing on detection of a target sequence, such as infectious disease monitoring or species identification, the computational cost of analysis may be reduced by directly detecting the target sequence in the electrical signal instead. Here, we present baseLess, a computational tool that enables such target-detection-only analysis. BaseLess makes use of an array of small neural networks, each of which efficiently detects a fixed-size subsequence of the target sequence directly from the electrical signal. We show that baseLess can accurately determine the identity of reads between three closely related fish species and can classify sequences in mixtures of 20 bacterial species, on an inexpensive single-board computer