Biology subjects

Fischer, L.

Publications and source records attributed to Fischer, L..

5 recordsLinked to original sources

bioRxiv2018

Defining the RNA Interactome by Total RNA-Associated Protein Purification

UV crosslinking can be used to identify precise RNA targets for individual proteins, transcriptome-wide. We sought to develop a technique to generate reciprocal data, identifying precise sites of RNA-binding proteome-wide. The resulting technique, total RNA-associated protein purification (TRAPP), was applied to yeast (S. cerevisiae) and bacteria (E. coli). In all analyses, SILAC labelling was used to quantify protein recovery in the presence and absence of irradiation. For S. cerevisiae, we also compared crosslinking using 254 nm (UVC) irradiation (TRAPP) with 4-thiouracil (4tU) labelling combined with ~350 nm (UVA) irradiation (PAR-TRAPP). Recovery of proteins not anticipated to show RNA-binding activity was substantially higher in TRAPP compared to PAR-TRAPP. As an example of preferential TRAPP-crosslinking, we tested enolase (Eno1) and demonstrated its binding to tRNA loops in vivo. We speculate that many protein-RNA interactions have biophysical effects on localization and/or accessibility, by opposing or promoting phase separation for highly abundant protein. Homologous metabolic enzymes showed RNA crosslinking in S. cerevisiae and E. coli, indicating conservation of this property. TRAPP allows alterations in RNA interactions to be followed and we initially analyzed the effects of weak acid stress. This revealed specific alterations in RNA-protein interactions; for example, during late 60S ribosome subunit maturation. Precise sites of crosslinking at the level of individual amino acids (iTRAPP) were identified in 395 peptides from 155 unique proteins, following phospho-peptide enrichment combined with a bioinformatics pipeline (Xi). TRAPP is quick, simple and scalable, allowing rapid characterization of the RNA-bound proteome in many systems.

cell biology↗

bioRxiv2018

An integrated workflow for cross-linking/mass spectrometry

We present a concise workflow to enhance the mass spectrometric detection of cross-linked peptides by introducing sequential digestion and the cross-link identification software Xi. Sequential digestion enhances peptide detection by shortening long tryptic peptides while avoiding over-digestion. We demonstrate our simple 12-fraction protocol for cross-linked multi-protein complexes and cell lysates, quantitative analysis, and high-density cross-linking, without requiring specific cross-linker features. This overall approach reveals dynamic protein-protein interaction sites, which are accessible, have fundamental functional relevance and are therefore ideally suited for the development of small molecule inhibitors.

biochemistry↗

bioRxiv2018

Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data

Diagnosis and risk stratification of cancer and many other diseases require the detection of genomic breakpoints as a prerequisite of calling copy number alterations (CNA). This, however, is still challenging and requires time-consuming manual curation. As deep-learning methods outperformed classical state-of-the-art algorithms in various domains and have also been successfully applied to life science problems including medicine and biology, we here propose Deep SNP, a novel Deep Neural Network to learn from genomic data. Specifically, we used a manually curated dataset from 12 genomic single nucleotide polymorphism array (SNPa) profiles as truth-set and aimed at predicting the presence or absence of genomic breakpoints, an indicator of structural chromosomal variations, in windows of 40,000 probes. We compare our results with well-known neural network models as well as Rawcopy though this tool is designed to predict breakpoints and in addition genomic segments with high sensitivity. We show, that Deep SNP is capable of successfully predicting the presence or absence of a breakpoint in large genomic windows and outperforms state-of-the-art neural network models. Qualitative examples suggest that integration of a localization unit may enable breakpoint detection and prediction of genomic segments, even if the breakpoint coordinates were not provided for network training. These results warrant further evaluation of DeepSNP for breakpoint localization and subsequent calling of genomic segments.

genomics↗

bioRxiv2018

In-Search Selection of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides

Cross-linking/mass spectrometry (CLMS) has undergone a maturation process akin to standard proteomics by adapting key methods such as false discovery rate control and quantification. A seldom-used search setting in proteomics is the consideration of multiple (lighter) alternative values for the monoisotopic precursor mass to compensate for possible misassignments of the monoisotopic peak. Here, we show that monoisotopic peak assignment is a major weakness of current data handling approaches in cross-linking. Cross-linked peptides often have high precursor masses, which reduces the presence of the monoisotopic peak in the isotope envelope. Paired with generally low peak intensity, this generates a challenge that may not be completely solvable by precursor mass assignment routines. We therefore took an alternative route by in-search assignment of the monoisotopic peak in Xi (Xi-MPA), which considers multiple precursor masses during database search. We compare and evaluate the performance of established preprocessing workflows that partly correct the monoisotopic peak and Xi-MPA on three publicly available datasets. Xi-MPA always delivered the highest number of identifications with ~2 to 4-fold increase of PSMs without compromising identification accuracy as determined by FDR estimation and comparison to crystallographic models.

bioinformatics↗

bioRxiv2017

False discovery rate estimation and heterobifunctional cross-linkers

False discovery rate (FDR) estimation is a cornerstone of proteomics that has recently been adapted to cross-linking/mass spectrometry. Here we demonstrate that heterobifunctional cross-linkers, while theoretically different from homobifunctional cross-linkers, need not be considered separately in practice. We develop and then evaluate the impact of applying a correct FDR formula for use of heterobifunctional cross-linkers and conclude that there are minimal practical advantages. Hence a single formula can be applied to data generated from the many different non-cleavable cross-linkers.

bioinformatics↗