EuPA Open Proteomics最新文献

The 2020 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers’ meeting was held from January 13^th to January 17^th 2020 in Nyborg, Denmark. Among the participants were scientists as well as developers working in the field of computational mass spectrometry (MS) and proteomics. The 4-day program was split between introductory keynote lectures and parallel hackathon sessions. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts, and to actively contribute to highly relevant research projects. We successfully produced several new tools that will be useful to the proteomics community by improving data analysis as well as facilitating future research. All keynote recordings are available on https://doi.org/10.5281/zenodo.3890181.

In the recent year, we felt like we were not truly showing our full potential in our PhD projects, and so we were very happy and excited when YPIC announced the ultimate proteomics challenge. This gave us the opportunity of showing off and procrastinating at the same time:) The challenge was to identify the amino acid sequence of 19 synthetic peptides made up from an English text and then find the book that it came from. For this task we chose to run on an Orbitrap Fusion™ Lumos™ Tribrid™ Mass Spectrometer with two different sensitive MS2 resolutions, each with both HCD and CID fragmentation consecutively. This strategy was chosen because we speculated that multiple MS2 scans at high quality would be beneficial over lower resolution, speed and quantity in the relatively sparse sample. The resulting chromatogram did not reveal 19 sharp distinct peaks and it was not clear to us where to start a manual spectra interpretation. We instead used the de novo option in the MaxQuant software and the resulting output gave us two phrases with words that were specific enough to be searched in the magic Google search engine. Google gave us the name of a very famous physicist, namely Sir Joseph John Thomson, and a reference to his book “Rays of positive electricity” from 1913. We then converted the paragraph we believed to be the right one into a FASTA format and used it with MaxQuant to do a database search. This resulted in 16 perfectly FASTA search-identified peptide sequences, one with a missing PTM and one found as a truncated version. The remaining one was identified within the MaxQuant de novo sequencing results. We thus show in this study that our workflow combining de novo spectra analysis algorithms with an online search engine is ideally suited for all applications where users want to decipher peptide-encoded prefaces of 20th century science books.

The 2019 European Bioinformatics Community (EuBIC) Winter School was held from January 15th to January 18th 2019 in Zakopane, Poland. This year’s meeting was the third of its kind and gathered international researchers in the field of (computational) proteomics to discuss (mainly) challenges in proteomics quantification and data independent acquisition (DIA). Here, we present an overview of the scientific program of the 2019 EuBIC Winter School. Furthermore, we can already give a small outlook to the upcoming EuBIC 2020 Developer’s Meeting.

In this study, we faced the challenge of deciphering a protein that has been designed and expressed by E. coli in such a way that the amino acid sequence encodes two concatenated English sentences. The letters ‘O’ and ‘U’ in the sentence are both replaced by ‘K’ in the protein. The sequence cannot be found online and carried to-be-discovered modifications. With limited information in hand, to solve the challenge, we developed a workflow consisting of bottom-up proteomics, de novo sequencing and a bioinformatics pipeline for data processing and searching for frequently appearing words. We assembled a complete first question: “Have you ever wondered what the most fundamental limitations in life are?” and validated the result by sequence database search against a customized FASTA file. We also searched the spectra against an E. coli proteome database and found close to 600 endogenous, co-purified E. coli proteins and contaminants introduced during sample handling, which made the inference of the sentence very challenging. We conclude that E. coli can express English sentences, and that de novo sequencing combined with clever sequence database search strategies is a promising tool for the identification of uncharacterized proteins.

In a common proteomics analysis today, the origins of our sample in the vial are known and therefore a database dependent approach to identify the containing peptides can be used. The first YPIC challenge though provided us with 19 synthetic peptides, which together formed an English sentence. For the identification of these peptides, a de-novo approach was used, which brought us together with an internet search engine to the hidden sentence. But only having the sentence was not sufficient for us, we also wanted to identify as many as possible of the spectra in our data. Therefore, we created and refined a database approach from the de-novo method and finally could identify the peptide-sentence with a good overlap.