Combination of Bottom-up 2D-LC-MS and Semi-top-down GelFree-LC-MS Enhances Coverage of Proteome and Low Molecular Weight Short Open Reading Frame Encoded Peptides of the Archaeon <i>Methanosarcina mazei</i>

Cassidy, Liam; Prasse, Daniela; Linke, Dennis; Schmitz, Ruth A.; Tholey, Andreas

doi:10.1021/acs.jproteome.6b00569

Cited by 41 publications

(68 citation statements)

References 30 publications

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“…The other two were acquired from the Thermo Scientific Orbitrap Fusion with the higher-energy collisional dissociation (HCD) technique. The high-resolution set includes nine datasets acquired from the Thermo Scientific Q-Exactive with the HCD technique (48)(49)(50)(51)(52)(53)(54)(55)(56) (Table S2). We chose data from a wide variety of species and research groups to ensure an unbiased evaluation.…”

Section: Resultsmentioning

confidence: 99%

De novo peptide sequencing by deep learning

Tran

Zhang

Lei

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

333

375

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De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7-22.9% higher accuracy at the amino acid level and 38.1-64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5-100% coverage and 97.2-99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming.deep learning | MS | de novo sequencing P roteomics research focuses on large-scale studies to characterize the proteome, the entire set of proteins, in a living organism (1-5). In proteomics, de novo peptide sequencing from tandem MS data plays the key role in the characterization of novel protein sequences. This field has been actively studied over the past 20 y, and many de novo sequencing tools have been proposed, such as PepNovo, PEAKS, NovoHMM, MSNovo, pNovo, UniNovo, and Novor among others (6-19). The recent "gold rush" into mAbs has undoubtedly elevated the application of de novo sequencing to a new horizon (20-23). However, computational challenges still remain, because MS/MS spectra contain much noise and ambiguity that require rigorous global optimization with various forms of dynamic programming that have been developed over the past decade (8-10, 12, 13, 15-19, 24).In this study, we introduce neural networks and deep learning to de novo peptide sequencing and achieve major breakthroughs on this well-studied problem. Deep learning has recently brought about a revolution in many research fields (25), repeatedly breaking state of the art records in image processing (26, 27), speech recognition (28), and natural language processing (29). It now forms the core of the artificial intelligence platforms of several technology giants, such as Google, Facebook, and Microsoft, as well as many startups in the industry. Deep learning has also made its way into biological sciences (30) [for instance, in the field of genomics, where deep neural network models have been developed for predicting the effects of noncoding single-nucleotide ...

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Section: Resultsmentioning

confidence: 99%

De novo peptide sequencing by deep learning

Tran

Zhang

Lei

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

333

375

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“…3). Due to their small size the probability to detect more than one unique peptide for proteins with up to 100 aa is relatively low (see also (21,42). To focus on not yet annotated proteins, we relied on ORFs with the highest coding potential based on different features and quality measures (Tab.…”

Section: Small Protein Identification Using Different Ms-based Approamentioning

confidence: 99%

A proteogenomics workflow to uncover the world of small proteins inStaphylococcus aureus

Fuchs

Kucklick

Lehmann

et al. 2020

Preprint

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Small proteins play diverse and essential roles in bacterial physiology and virulence. Despite their importance, automated genome annotation algorithms still cannot accurately annotate all respective small open reading frames (sORFs), as they usually provide insufficient sequence information for domain and homology searches, tend to be species specific and only a few experimentally validated examples are covered in standard proteomics studies. The accuracy and reliability of genome annotations, particularly for sORFs, can be significantly improved by integrating protein evidence from experimental approaches that enrich for small proteins. Here we present a highly optimized and flexible workflow for bacterial proteogenomics, which covers all steps from (i) creation of protein databases, (ii) database searches, (iii) peptide-to-genome mapping to (iv) result interpretation and whose automated execution is supported by two open source tools (SALT & Pepper). We used the workflow to identify high quality peptide spectrum matches (PSMs) for both annotated and unannotated small proteins (≤ 100 aa; SP100) in Staphylococcus aureus Newman. Proteins isolated from cells at the exponential and stationary growth phase were digested with different endopeptidases (trypsin, Lys-C, AspN), the resulting peptides fractionated by gel-based and gel-free methods and measured with highly sensitive mass spectrometers. PSMs or sORF predictions from sORFfinder were stringently filtered allowing us to detect 185 soluble SP100, 69 of which were missing in the used genome annotation. Most interestingly, almost half of the identified SP100 were basic, suggesting a role in binding to more acidic molecules such as nucleic acids or phospholipids. In addition, phage-related functions were proposed for 30 SP100, based on the localization of their coding sequences in the genome.

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“…by sophisticated bioinformatics predictions, peptidomics, ribosome profiling and combinations of those (e.g. Cassidy et al (2019), Cassidy et al (2016), Lago et al (2017), Saghatelian and Couso (2015), Chu et al (2015), Cardon et al (2020), Ma et al (2014), Ingolia (2014), Meydan et al (2019), Weaver et al (2019), Miravet-Verde et al (2019)). Due to their small size the small proteins are frequently predicted to modify the activity of larger proteins or complexes via physical interactions or interact with the membranes.…”

Section: Introductionmentioning

confidence: 99%

“…In archaea a few small proteins have been reported, the majority of which is regulated in response to a specific stress. However, for most of them verified functional analysis is scarce or missing (Prasse et al (2015), Cassidy et al (2016), Cassidy et al (2018), Nagel et al (2019), Kubatova et al (2019), Jevtic et al (2019 Methanosarcina mazei strain Gö1 belongs to the methylotrophic methanogens of the order Methanosarcinales, which have the most versatile substrate spectrum within the methanogenic archaea and significantly contribute to the production of the greenhouse gas (Ferry (1999)). M. mazei is able to fix molecular nitrogen under nitrogen limitation.…”

Section: Introductionmentioning

confidence: 99%

Small protein 26 interacts and enhances glutamine synthetase activity inMethanosarcina mazei

Gutt

Jordan

Weidenbach

et al. 2020

Preprint

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Small ORFs (sORF) encoded small proteins have been overlooked for a long time due to challenges in prediction and distinguishing between coding and non-coding predicted sORFs and in their biochemical detection and characterization. We report on the first biochemical and functional characterization of a small protein (sP26) in the archaeal model organism Methanosarcina mazei, comprising 23 amino acids. The corresponding encoding leaderless mRNA (spRNA26) is highly conserved within numerous Methanosarcina strains on the amino acid as well as on nucleotide level strongly arguing for a cellular function of the small protein. spRNA26 is significantly enhanced under nitrogen limitation, but also under oxygen and salt stress conditions. His-tagged sP26 was heterologously expressed and purified by fractionated ammonium sulfate precipitation, affinity chromatography and size exclusion centrifugation. Using independent biochemical approaches (pull-down by affinity chromatography followed by MS analysis, revers pull-down, microscale thermophoresis and size exclusion chromatography) we observed that sP26 interacts and forms complexes with M. mazei glutamine synthetase (GlnA 1 ) with high affinity (app. KD = 45 +/-14 µM). Upon interaction with sP26, GlnA 1 activity was significantly stimulated independently and in addition to the known activation by the metabolite 2-oxoglutarate. Besides strong interaction of sP26 with the PII-like protein GlnK 1 was demonstrated (KD= 1.4 µM +/-0.9 µM). On the basis of these findings, we hypothesize that in addition to 2-oxoglutarate, sP26activates GlnA 1 activity under nitrogen limitation most likely by stabilizing the dodecameric structure of GlnA 1 .

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Combination of Bottom-up 2D-LC-MS and Semi-top-down GelFree-LC-MS Enhances Coverage of Proteome and Low Molecular Weight Short Open Reading Frame Encoded Peptides of the Archaeon Methanosarcina mazei

Cited by 41 publications

References 30 publications

De novo peptide sequencing by deep learning

De novo peptide sequencing by deep learning

A proteogenomics workflow to uncover the world of small proteins inStaphylococcus aureus

Small protein 26 interacts and enhances glutamine synthetase activity inMethanosarcina mazei

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