De novo sequencing of MS/MS spectra

Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field. © 2011 Expert Reviews Ltd.The Turkish Academy of Science (TÜBA

Antilope - A Lagrangian Relaxation Approach to the de novo Peptide Sequencing Problem

Peptide sequencing from mass spectrometry data is a key step in proteome research. Especially de novo sequencing, the identification of a peptide from its spectrum alone, is still a challenge even for state-of-the-art algorithmic approaches. In this paper we present Antilope, a new fast and flexible approach based on mathematical programming. It builds on the spectrum graph model and works with a variety of scoring schemes. Antilope combines Lagrangian relaxation for solving an integer linear programming formulation with an adaptation of Yen's k shortest paths algorithm. It shows a significant improvement in running time compared to mixed integer optimization and performs at the same speed like other state-of-the-art tools. We also implemented a generic probabilistic scoring scheme that can be trained automatically for a dataset of annotated spectra and is independent of the mass spectrometer type. Evaluations on benchmark data show that Antilope is competitive to the popular state-of-the-art programs PepNovo and NovoHMM both in terms of run time and accuracy. Furthermore, it offers increased flexibility in the number of considered ion types. Antilope will be freely available as part of the open source proteomics library OpenMS

De novo sequencing of proteins by mass spectrometry

Introduction Proteins are crucial for every cellular activity and unraveling their sequence and structure is a crucial step to fully understand their biology. Early methods of protein sequencing were mainly based on the use of enzymatic or chemical degradation of peptide chains. With the completion of the human genome project and with the expansion of the information available for each protein, various databases containing this sequence information were formed. Areas covered De novo protein sequencing, shotgun proteomics and other mass-spectrometric techniques, along with the various software are currently available for proteogenomic analysis. Emphasis is placed on the methods for de novo sequencing, together with potential and shortcomings using databases for interpretation of protein sequence data. Expert opinion As mass-spectrometry sequencing performance is improving with better software and hardware optimizations, combined with user-friendly interfaces, de-novo protein sequencing becomes imperative in shotgun proteomic studies. Issues regarding unknown or mutated peptide sequences, as well as, unexpected post-translational modifications (PTMs) and their identification through false discovery rate searches using the target/decoy strategy need to be addressed. Ideally, it should become integrated in standard proteomic workflows as an add-on to conventional database search engines, which then would be able to provide improved identification.publishe

Mapping the proteome with data-driven methods: A cycle of measurement, modeling, hypothesis generation, and engineering

The living cell exhibits emergence of complex behavior and its modeling requires a systemic, integrative approach if we are to thoroughly understand and harness it. The work in this thesis has had the more narrow aim of quantitatively characterizing and mapping the proteome using data-driven methods, as proteins perform most functional and structural roles within the cell. Covered are the different parts of the cycle from improving quantification methods, to deriving protein features relying on their primary structure, predicting the protein content solely from sequence data, and, finally, to developing theoretical protein engineering tools, leading back to experiment.\ua0\ua0\ua0\ua0 High-throughput mass spectrometry platforms provide detailed snapshots of a cell\u27s protein content, which can be mined towards understanding how the phenotype arises from genotype and the interplay between the various properties of the constituent proteins. However, these large and dense data present an increased analysis challenge and current methods capture only a small fraction of signal. The first part of my work has involved tackling these issues with the implementation of a GPU-accelerated and distributed signal decomposition pipeline, making factorization of large proteomics scans feasible and efficient. The pipeline yields individual analyte signals spanning the majority of acquired signal, enabling high precision quantification and further analytical tasks.\ua0\ua0\ua0 Having such detailed snapshots of the proteome enables a multitude of undertakings. One application has been to use a deep neural network model to learn the amino acid sequence determinants of temperature adaptation, in the form of reusable deep model features. More generally, systemic quantities may be predicted from the information encoded in sequence by evolutionary pressure. Two studies taking inspiration from natural language processing have sought to learn the grammars behind the languages of expression, in one case predicting mRNA levels from DNA sequence, and in the other protein abundance from amino acid sequence. These two models helped build a quantitative understanding of the central dogma and, furthermore, in combination yielded an improved predictor of protein amount. Finally, a mathematical framework relying on the embedded space of a deep model has been constructed to assist guided mutation of proteins towards optimizing their abundance

The role of petrimonas mucosa ING2-E5at in mesophilic biogas reactor systems as deduced from multiomics analyses

Members of the genera Proteiniphilum and Petrimonas were speculated to represent indicators reflecting process instability within anaerobic digestion (AD) microbiomes. Therefore, Petrimonas mucosa ING2-E5AT was isolated from a biogas reactor sample and sequenced on the PacBio RSII and Illumina MiSeq sequencers. Phylogenetic classification positioned the strain ING2-E5AT in close proximity to Fermentimonas and Proteiniphilum species (family Dysgonomonadaceae). ING2-E5AT encodes a number of genes for glycosyl-hydrolyses (GH) which are organized in Polysaccharide Utilization Loci (PUL) comprising tandem susCD-like genes for a TonB-dependent outer-membrane transporter and a cell surface glycan-binding protein. Different GHs encoded in PUL are involved in pectin degradation, reflecting a pronounced specialization of the ING2-E5AT PUL systems regarding the decomposition of this polysaccharide. Genes encoding enzymes participating in amino acids fermentation were also identified. Fragment recruitments with the ING2-E5AT genome as a template and publicly available metagenomes of AD microbiomes revealed that Petrimonas species are present in 146 out of 257 datasets supporting their importance in AD microbiomes. Metatranscriptome analyses of AD microbiomes uncovered active sugar and amino acid fermentation pathways for Petrimonas species. Likewise, screening of metaproteome datasets demonstrated expression of the Petrimonas PUL-specific component SusC providing further evidence that PUL play a central role for the lifestyle of Petrimonas species. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

The Role of Petrimonas mucosa ING2-E5AT in Mesophilic Biogas Reactor Systems as Deduced from Multiomics Analyses

Members of the genera Proteiniphilum and Petrimonas were speculated to represent indicators reflecting process instability within anaerobic digestion (AD) microbiomes. Therefore, Petrimonas mucosa ING2-E5AT was isolated from a biogas reactor sample and sequenced on the PacBio RSII and Illumina MiSeq sequencers. Phylogenetic classification positioned the strain ING2-E5AT in close proximity to Fermentimonas and Proteiniphilum species (family Dysgonomonadaceae). ING2-E5AT encodes a number of genes for glycosyl-hydrolyses (GH) which are organized in Polysaccharide Utilization Loci (PUL) comprising tandem susCD-like genes for a TonB-dependent outer-membrane transporter and a cell surface glycan-binding protein. Different GHs encoded in PUL are involved in pectin degradation, reflecting a pronounced specialization of the ING2-E5AT PUL systems regarding the decomposition of this polysaccharide. Genes encoding enzymes participating in amino acids fermentation were also identified. Fragment recruitments with the ING2-E5AT genome as a template and publicly available metagenomes of AD microbiomes revealed that Petrimonas species are present in 146 out of 257 datasets supporting their importance in AD microbiomes. Metatranscriptome analyses of AD microbiomes uncovered active sugar and amino acid fermentation pathways for Petrimonas species. Likewise, screening of metaproteome datasets demonstrated expression of the Petrimonas PUL-specific component SusC providing further evidence that PUL play a central role for the lifestyle of Petrimonas species.Peer Reviewe