CovRadar: continuously tracking and filtering SARS-CoV-2 mutations for genomic surveillance.

SUMMARY: The ongoing pandemic caused by SARS-CoV-2 emphasizes the importance of genomic surveillance to understand the evolution of the virus, to monitor the viral population, and plan epidemiological responses. Detailed analysis, easy visualization and intuitive filtering of the latest viral sequences are powerful for this purpose. We present CovRadar, a tool for genomic surveillance of the SARS-CoV-2 Spike protein. CovRadar consists of an analytical pipeline and a web application that enable the analysis and visualization of hundreds of thousand sequences. First, CovRadar extracts the regions of interest using local alignment, then builds a multiple sequence alignment, infers variants and consensus and finally presents the results in an interactive app, making accessing and reporting simple, flexible and fast. AVAILABILITY AND IMPLEMENTATION: CovRadar is freely accessible at https://covradar.net, its open-source code is available at https://gitlab.com/dacs-hpi/covradar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Leveraging Parameter Dependencies in High-Field Asymmetric Waveform Ion-Mobility Spectrometry and Size Exclusion Chromatography for Proteome-wide Cross-Linking Mass Spectrometry.

Ion-mobility spectrometry shows great promise to tackle analytically challenging research questions by adding another separation dimension to liquid chromatography-mass spectrometry. The understanding of how analyte properties influence ion mobility has increased through recent studies, but no clear rationale for the design of customized experimental settings has emerged. Here, we leverage machine learning to deepen our understanding of field asymmetric waveform ion-mobility spectrometry for the analysis of cross-linked peptides. Knowing that predominantly m/z and then the size and charge state of an analyte influence the separation, we found ideal compensation voltages correlating with the size exclusion chromatography fraction number. The effect of this relationship on the analytical depth can be substantial as exploiting it allowed us to almost double unique residue pair detections in a proteome-wide cross-linking experiment. Other applications involving liquid- and gas-phase separation may also benefit from considering such parameter dependencies.

Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.

Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits the numbers of protein-protein interactions that can be confidently identified. Here, we leverage chromatographic retention time information to aid the identification of crosslinked peptides from mass spectra. Our Siamese machine learning model xiRT achieves highly accurate retention time predictions of crosslinked peptides in a multi-dimensional separation of crosslinked E. coli lysate. Importantly, supplementing the search engine score with retention time features leads to a substantial increase in protein-protein interactions without affecting confidence. This approach is not limited to cell lysates and multi-dimensional separation but also improves considerably the analysis of crosslinked multiprotein complexes with a single chromatographic dimension. Retention times are a powerful complement to mass spectrometric information to increase the sensitivity of crosslinking mass spectrometry analyses.

An integrated workflow for crosslinking mass spectrometry.

We present a concise workflow to enhance the mass spectrometric detection of crosslinked peptides by introducing sequential digestion and the crosslink identification software xiSEARCH. Sequential digestion enhances peptide detection by selective shortening of long tryptic peptides. We demonstrate our simple 12-fraction protocol for crosslinked multi-protein complexes and cell lysates, quantitative analysis, and high-density crosslinking, without requiring specific crosslinker 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.

Noncovalently Associated Peptides Observed during Liquid Chromatography-Mass Spectrometry and Their Effect on Cross-Link Analyses.

Cross-linking mass spectrometry draws structural information from covalently linked peptide pairs. When these links do not match to previous structural models, they may indicate changes in protein conformation. Unfortunately, such links can also be the result of experimental error or artifacts. Here, we describe the observation of noncovalently associated peptides during liquid chromatography-mass spectrometry analysis, which can easily be misidentified as cross-linked. Strikingly, they often mismatch to the protein structure. Noncovalently associated peptides presumably form during ionization and can be distinguished from cross-linked peptides by observing coelution of the corresponding linear peptides in MS1 spectra, as well as the presence of the individual (intact) peptide fragments in MS2 spectra. To suppress noncovalent peptide formations, increasingly disruptive ionization settings can be used, such as in-source fragmentation.

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

Cross-linking/mass spectrometry has undergone a maturation process akin to standard proteomics by adapting key methods such as false discovery rate control and quantification. A poorly evaluated 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 the cross-link database search tool 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 data sets. 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.

Peptide Retention in Hydrophilic Strong Anion Exchange Chromatography Is Driven by Charged and Aromatic Residues.

Hydrophilic strong anion exchange chromatography (hSAX) is becoming a popular method for the prefractionation of proteomic samples. However, the use and further development of this approach is affected by the limited understanding of its retention mechanism and the absence of elution time prediction. Using a set of 59 297 confidentially identified peptides, we performed an explorative analysis and built a predictive deep learning model. As expected, charged residues are the major contributors to the retention time through electrostatic interactions. Aspartic acid and glutamic acid have a strong retaining effect and lysine and arginine have a strong repulsion effect. In addition, we also find the involvement of aromatic amino acids. This suggests a substantial contribution of cation-π interactions to the retention mechanism. The deep learning approach was validated using 5-fold cross-validation (CV) yielding a mean prediction accuracy of 70% during CV and 68% on a hold-out validation set. The results of this study emphasize that not only electrostatic interactions but rather diverse types of interactions must be integrated to build a reliable hSAX retention time predictor.

Comparative phosphoproteomic analysis reveals signaling networks regulating monopolar and bipolar cytokinesis.

The successful completion of cytokinesis requires the coordinated activities of diverse cellular components including membranes, cytoskeletal elements and chromosomes that together form partly redundant pathways, depending on the cell type. The biochemical analysis of this process is challenging due to its dynamic and rapid nature. Here, we systematically compared monopolar and bipolar cytokinesis and demonstrated that monopolar cytokinesis is a good surrogate for cytokinesis and it is a well-suited system for global biochemical analysis in mammalian cells. Based on this, we established a phosphoproteomic signature of cytokinesis. More than 10,000 phosphorylation sites were systematically monitored; around 800 of those were up-regulated during cytokinesis. Reconstructing the kinase-substrate interaction network revealed 31 potentially active kinases during cytokinesis. The kinase-substrate network connects proteins between cytoskeleton, membrane and cell cycle machinery. We also found consensus motifs of phosphorylation sites that can serve as biochemical markers specific to cytokinesis. Beyond the kinase-substrate network, our reconstructed signaling network suggests that combination of sumoylation and phosphorylation may regulate monopolar cytokinesis specific signaling pathways. Our analysis provides a systematic approach to the comparison of different cytokinesis types to reveal alternative ways and a global overview, in which conserved genes work together and organize chromatin and cytoplasm during cytokinesis.

Optimized Fragmentation Regime for Diazirine Photo-Cross-Linked Peptides.

Cross-linking/mass spectrometry has evolved into a robust technology that reveals structural insights into proteins and protein complexes. We leverage a new tribrid instrument with improved fragmentation capacities in a systematic comparison to identify which fragmentation method would be best for the identification of cross-linked peptides. Specifically, we explored three fragmentation methods and two combinations: collision-induced dissociation (CID), beam-type CID (HCD), electron-transfer dissociation (ETD), ETciD, and EThcD. Trypsin-digested, SDA-cross-linked human serum albumin (HSA) served as a test sample, yielding over all methods and in triplicate analysis in total 2602 matched PSMs and 1390 linked residue pairs at 5% false discovery rate, as confirmed by the crystal structure. HCD wins in number of matched peptide-spectrum-matches (958 PSMs) and identified links (446). CID is most complementary, increasing the number of identified links by 13% (58 links). HCD wins together with EThcD in cross-link site calling precision, with approximately 62% of sites having adjacent backbone cleavages that unambiguously locate the link in both peptides, without assuming any cross-linker preference for amino acids. Overall quality of spectra, as judged by sequence coverage of both peptides, is best for EThcD for the majority of peptides. Sequence coverage might be of particular importance for complex samples, for which we propose a data dependent decision tree, else HCD is the method of choice. The mass spectrometric raw data has been deposited in PRIDE (PXD003737).

A Study into the Collision-induced Dissociation (CID) Behavior of Cross-Linked Peptides.

Cross-linking/mass spectrometry resolves protein-protein interactions or protein folds by help of distance constraints. Cross-linkers with specific properties such as isotope-labeled or collision-induced dissociation (CID)-cleavable cross-linkers are in frequent use to simplify the identification of cross-linked peptides. Here, we analyzed the mass spectrometric behavior of 910 unique cross-linked peptides in high-resolution MS1 and MS2 from published data and validate the observation by a ninefold larger set from currently unpublished data to explore if detailed understanding of their fragmentation behavior would allow computational delivery of information that otherwise would be obtained via isotope labels or CID cleavage of cross-linkers. Isotope-labeled cross-linkers reveal cross-linked and linear fragments in fragmentation spectra. We show that fragment mass and charge alone provide this information, alleviating the need for isotope-labeling for this purpose. Isotope-labeled cross-linkers also indicate cross-linker-containing, albeit not specifically cross-linked, peptides in MS1. We observed that acquisition can be guided to better than twofold enrich cross-linked peptides with minimal losses based on peptide mass and charge alone. By help of CID-cleavable cross-linkers, individual spectra with only linear fragments can be recorded for each peptide in a cross-link. We show that cross-linked fragments of ordinary cross-linked peptides can be linearized computationally and that a simplified subspectrum can be extracted that is enriched in information on one of the two linked peptides. This allows identifying candidates for this peptide in a simplified database search as we propose in a search strategy here. We conclude that the specific behavior of cross-linked peptides in mass spectrometers can be exploited to relax the requirements on cross-linkers.

Detection of Unknown Amino Acid Substitutions Using Error-Tolerant Database Search.

Recent studies have demonstrated that mass spectrometry-based variant detection is feasible. Typically, either genomic variant databases or transcript data are used to construct customized target databases for the identification of single-amino acid variants in mass spectrometry data. However, both approaches require additional data to perform the identification of SAAVs. Here, we discuss the application of an error-tolerant peptide search engine such as BICEPS for identifying variants exclusively based on standard Uniprot databases. Thereby, unnecessary and redundant extensions of the search space are avoided. The workflow provides an unbiased view on the data; the search space is not limited to known variants and simultaneously does not require additional data. In a subsequent step a second identification search is performed to verify the initially identified variant peptides and aggregate information on the protein level.

Phosphoproteomic Analysis of Aurora Kinase Inhibition in Monopolar Cytokinesis.

Cytokinesis is the last step of the cell cycle that requires coordinated activities of the microtubule cytoskeleton, actin cytoskeleton, and membrane compartments. Aurora B kinase is one of the master regulatory kinases that orchestrate multiple events during cytokinesis. To reveal targets of the Aurora B kinase, we combined quantitative mass spectrometry with chemical genetics. Using the quantitative proteomic approach, SILAC (stable isotope labeling with amino acids in cell culture), we analyzed the phosphoproteome of monopolar cytokinesis upon VX680- or AZD1152-mediated aurora kinase inhibition. In total, our analysis quantified over 20 000 phosphopeptides in response to the Aurora-B kinase inhibition; 246 unique phosphopeptides were significantly down-regulated and 74 were up-regulated. Our data provide a broad analysis of downstream effectors of Aurora kinase and offer insights into how Aurora kinase regulates cytokinesis.

Specificity control for read alignments using an artificial reference genome-guided false discovery rate.

MOTIVATION: Accurate estimation, comparison and evaluation of read mapping error rates is a crucial step in the processing of next-generation sequencing data, as further analysis steps and interpretation assume the correctness of the mapping results. Current approaches are either focused on sensitivity estimation and thereby disregard specificity or are based on read simulations. Although continuously improving, read simulations are still prone to introduce a bias into the mapping error quantitation and cannot capture all characteristics of an individual dataset. RESULTS: We introduce ARDEN (artificial reference driven estimation of false positives in next-generation sequencing data), a novel benchmark method that estimates error rates of read mappers based on real experimental reads, using an additionally generated artificial reference genome. It allows a dataset-specific computation of error rates and the construction of a receiver operating characteristic curve. Thereby, it can be used for optimization of parameters for read mappers, selection of read mappers for a specific problem or for filtering alignments based on quality estimation. The use of ARDEN is demonstrated in a general read mapper comparison, a parameter optimization for one read mapper and an application example in single-nucleotide polymorphism discovery with a significant reduction in the number of false positive identifications. AVAILABILITY: The ARDEN source code is freely available at http://sourceforge.net/projects/arden/.