Biology subjects

de Lannoy, C.

Publications and source records attributed to de Lannoy, C..

5 recordsLinked to original sources

bioRxiv2023

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.

biophysics↗

bioRxiv2021

Inferring kinetic rate constants from single-molecule FRET trajectories - a blind benchmark of kinetic analysis tools

Single-molecule FRET (smFRET) is a versatile technique to study the dynamics and function of biomolecules since it makes nanoscale movements detectable as fluorescence signals. The powerful ability to infer quantitative kinetic information from smFRET data is, however, complicated by experimental limitations. Diverse analysis tools have been developed to overcome these hurdles but a systematic comparison is lacking. Here, we report the results of a blind benchmark study assessing eleven analysis tools used to infer kinetic rate constants from smFRET trajectories. We tested them against simulated and experimental data containing the most prominent difficulties encountered in analyzing smFRET experiments: different noise levels, varied model complexity, non-equilibrium dynamics, and kinetic heterogeneity. Our results highlight the current strengths and limitations in inferring kinetic information from smFRET trajectories. In addition, we formulate concrete recommendations and identify key targets for future developments, aimed to advance our understanding of biomolecular dynamics through quantitative experiment-derived models.

biophysics↗

bioRxiv2021

Evaluation of FRET X for Single-Molecule Protein Fingerprinting

Single-molecule protein identification is a novel, as of yet unrealized concept with potentially groundbreaking applications in biological research. We propose a method called FRET X (Forster Resonance Energy Transfer via DNA eXchange) fingerprinting, in which the FRET efficiency is read out between exchangeable dyes on protein-bound DNA docking strands, and accumulated FRET efficiency values constitute the fingerprint for a protein. To evaluate the feasibility of this approach, we simulated fingerprints for hundreds of proteins using a coarse-grained lattice model and experimentally demonstrated FRET X fingerprinting on a system of model peptides. Measured fingerprints are in agreement with our simulations, corroborating the validity of our modeling approach. In a simulated complex mixture of >300 human proteins of which only cysteines, lysines and arginines were labeled, a support vector machine was able to identify constituents with 95% accuracy. We anticipate that our FRET X fingerprinting approach will form the basis of an analysis tool for targeted proteomics.

biophysics↗

bioRxiv2021

Chop-n-drop: in silico assessment of a novel single-molecule protein fingerprinting method employing fragmentation and nanopore detection

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. However whether such fragmentation patterns are sufficiently characteristic of proteins to identify them in complex samples remained unclear. In the simulation study presented here, we show that 97.9% 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 78.8% 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.

bioinformatics↗

bioRxiv2018

poreTally: run and publish de novo Nanopore assembler benchmarks

Nanopore sequencing is a novel approach to nucleic acid analysis that generates long, error-prone reads. Since device components, base calling software and best practices for sample preparation are updated frequently and extensively, the nature of the produced data also changes frequently. As a result, peer-reviewed publications on de novo assembly pipeline benchmarking efforts are quickly rendered outdated by the next major improvement to the sequencing platforms. To provide the user community with a faster, more flexible alternative to peer-reviewed benchmark papers for de novo assembly tool performance we constructed poreTally, a comprehensive benchmarking 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. Results can immediately be shared with peers in a Github/Gitlab repository. Furthermore, we aim to give a more inclusive overview of assembly pipeline performance than any individual research group can, by offering users the possibility to submit their results to a collective benchmarking effort. poreTally is available on Github.

bioinformatics↗