Boosting MS1-only Proteomics with Machine Learning Allows 2000 Protein Identifications in Single-Shot Human Proteome Analysis Using 5 min HPLC Gradient

Ivanov, Mark V.; Bubis, Julia A.; Gorshkov, Vladimir; Abdrakhimov, Daniil A.; Kjeldsen, Frank; Горшков, М. В.

doi:10.1021/acs.jproteome.0c00863

Cited by 30 publications

(48 citation statements)

References 52 publications

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“…The Hela digest is a well-known standard and is commonly used by the proteomics community to evaluate the performance of instruments, new sample preparations, or data acquisition methodologies [ 27 , 60 , 61 ]. It has also been applied in the evaluation of other MS1 transfer methodologies and single-cell proteomics [ 62 , 63 ]. In addition, most of the proteins identified in different types of cancer cell lines can be found in Hela, which means that this standard provides a qualitative representation of different cell proteomes [ 64 ].…”

Section: Resultsmentioning

confidence: 99%

Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database

Almeida

Rodriguez

Pla

et al. 2021

Cancers

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Plasma analysis by mass spectrometry-based proteomics remains a challenge due to its large dynamic range of 10 orders in magnitude. We created a methodology for protein identification known as Wise MS Transfer (WiMT). Melanoma plasma samples from biobank archives were directly analyzed using simple sample preparation. WiMT is based on MS1 features between several MS runs together with custom protein databases for ID generation. This entails a multi-level dynamic protein database with different immunodepletion strategies by applying single-shot proteomics. The highest number of melanoma plasma proteins from undepleted and unfractionated plasma was reported, mapping >1200 proteins from >10,000 protein sequences with confirmed significance scoring. Of these, more than 660 proteins were annotated by WiMT from the resulting ~5800 protein sequences. We could verify 4000 proteins by MS1t analysis from HeLA extracts. The WiMT platform provided an output in which 12 previously well-known candidate markers were identified. We also identified low-abundant proteins with functions related to (i) cell signaling, (ii) immune system regulators, and (iii) proteins regulating folding, sorting, and degradation, as well as (iv) vesicular transport proteins. WiMT holds the potential for use in large-scale screening studies with simple sample preparation, and can lead to the discovery of novel proteins with key melanoma disease functions.

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Section: Resultsmentioning

confidence: 99%

Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database

Almeida

Rodriguez

Pla

et al. 2021

Cancers

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“…HEK-293 data were additionally analyzed using DirectMS1 (ms1searchpy v. 2.1.7) approach [33]. Search parameters were default except missed cleavages were set to 0, 0 and 2 for trypsin, LysC and GluC, respectively.…”

Section: Ms1 Only Search To Identity Variants In Hek-293 Cellsmentioning

confidence: 99%

“…An approach of express proteomic analysis which uses only precursor m/z values without MS/MS, called DirectMS1, was recently elaborated by authors of this work [33]. In this method, peptide-level FDR is usually uncontrolled and high, which makes variant identification tricky.…”

Section: Ms1 Approach To Confirm Amino Acid Sequence Variants In Hek-293 Proteomementioning

confidence: 99%

Validating amino acid variants in proteogenomics using sequence coverage by multiple reads

Levitsky

Kuznetsova

Kliuchnikova

et al. 2022

Preprint

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Mass spectrometry-based proteome analysis usually implies matching mass spectra of proteolytic peptides to amino acid sequences predicted from nucleic acid sequences. At the same time, due to the stochastic nature of the method when it comes to proteome-wide analysis, in which only a fraction of peptides are selected for sequencing, the completeness of protein sequence identification is undermined. Likewise, the reliability of peptide variant identification in proteogenomic studies is suffering. We propose a way to interpret shotgun proteomics results, specifically in data-dependent acquisition mode, as protein sequence coverage by multiple reads, just as it is done in the field of nucleic acid sequencing for the calling of single nucleotide variants. Multiple reads for each position in a sequence could be provided by overlapping distinct peptides, thus, confirming the presence of certain amino acid residues in the overlapping stretch with much lower false discovery rate than conventional 1%. The source of overlapping distinct peptides are, first, miscleaved tryptic peptides in combination with their properly cleaved counterparts, and, second, peptides generated by several proteases with different specificities after the same specimen is subject to parallel digestion and analyzed separately. We illustrate this approach using publicly available multiprotease proteomic datasets and our own data generated for HEK-293 cell line digests obtained using trypsin, LysC and GluC proteases. From 5000 to 8000 protein groups are identified for each digest corresponding to up to 30% of the whole proteome coverage. Most of this coverage was provided by a single read, while up to 7% of the observed protein sequences were covered two-fold and more. The proteogenomic analysis of HEK-293 cell line revealed 36 peptide variants associated with SNP, seven of which were supported by multiple reads. The efficiency of the multiple reads approach depends strongly on the depth of proteome analysis, the digesting features such as the level of miscleavages, and will increase with the number of different proteases used in parallel proteome digestion.

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“…It should also be noted that by using accurate predictions of RT and CCS, it is possible to use one-dimensional MS (MS1) for proteome analysis instead of MS2 [ 11 ]. This allows for using much shorter HPLC gradients and determining proteins much faster [ 12 ]. The typical HPLC analysis of the digested mixture of peptides (complete proteome analysis) using MS2 takes 30–120 min per sample (duration of chromatographic method).…”

Section: Introductionmentioning

confidence: 99%

“…The typical HPLC analysis of the digested mixture of peptides (complete proteome analysis) using MS2 takes 30–120 min per sample (duration of chromatographic method). The use of MS1 allows conducting such an analysis in 5–10 min [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry

et al. 2021

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Most frequently, the identification of peptides in mass spectrometry-based proteomics is carried out using high-resolution tandem mass spectrometry. In order to increase the accuracy of analysis, additional information on the peptides such as chromatographic retention time and collision cross section in ion mobility spectrometry can be used. An accurate prediction of the collision cross section values allows erroneous candidates to be rejected using a comparison of the observed values and the predictions based on the amino acids sequence. Recently, a massive high-quality data set of peptide collision cross sections was released. This opens up an opportunity to apply the most sophisticated deep learning techniques for this task. Previously, it was shown that a recurrent neural network allows for predicting these values accurately. In this work, we present a deep convolutional neural network that enables us to predict these values more accurately compared with previous studies. We use a neural network with complex architecture that contains both convolutional and fully connected layers and comprehensive methods of converting a peptide to multi-channel 1D spatial data and vector. The source code and pre-trained model are available online.

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Boosting MS1-only Proteomics with Machine Learning Allows 2000 Protein Identifications in Single-Shot Human Proteome Analysis Using 5 min HPLC Gradient

Cited by 30 publications

References 52 publications

Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database

Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database

Validating amino acid variants in proteogenomics using sequence coverage by multiple reads

A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry

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