Dataset Information

Fine Tuning of a Type 1 Interferon Antagonist.

ABSTRACT: Type I interferons are multi-potent cytokines that serve as first line of defense against viruses and other pathogens, posses immunomudolatory functions and elicit a growth inhibitory response. In recent years it has been shown that interferons are also detrimental, for example in lupus, AIDS, tuberculosis and cognitive decline, highlighted the need to develop interferon antagonists. We have previously developed the antagonist IFN-1ant, with much reduced binding to the IFNAR1 receptor and enhanced binding to IFNAR2. Here, we further tune the IFN-1ant by producing three additional antagonists based on IFN-1ant but with altered activity profiles. We show that in all three cases the antiproliferative activity of interferons is blocked and the induction of gene transcription of immunomudolatory and antiproliferative associated genes are substantially decreased. Conversely, each of the new antagonists elicits a different degree of antiviral response, STAT phosphorylation and related gene induction. Two of the new antagonists promote decreased activity in relation to the original IFN-1ant, while one of them promotes increased activity. As we do not know the exact causes of the detrimental effects of IFNs, the four antagonists that were produced and analyzed provide the opportunity to investigate the extent of antagonistic and agonistic activity optimal for a given condition.

SUBMITTER: Urin V

PROVIDER: S-EPMC4497658 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Fine Tuning of a Type 1 Interferon Antagonist.

Urin Victoria V Levin Doron D Sharma Nanaocha N Harari Daniel D Schreiber Gideon G

PloS one 20150709 7

Type I interferons are multi-potent cytokines that serve as first line of defense against viruses and other pathogens, posses immunomudolatory functions and elicit a growth inhibitory response. In recent years it has been shown that interferons are also detrimental, for example in lupus, AIDS, tuberculosis and cognitive decline, highlighted the need to develop interferon antagonists. We have previously developed the antagonist IFN-1ant, with much reduced binding to the IFNAR1 receptor and enhanc ...[more]

PMID: 26158644

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Project description:To create a dataset of PSMs that does not exhibit tryptic bias, we selected PSMs with a uniform distribution of amino acids at the C-terminal peptide positions from two datasets: MassIVE-KB [Wang2018] and PROSPECT [Shouman2022]. The MassIVE-KB dataset contains 30 million PSMs and consists entirely of data generated using trypsin; hence, only a small proportion of the MassIVE-KB peptides do not end in K or R, corresponding to those that appear at the C-terminus of the corresponding protein. The PROSPECT dataset is a collection of 61 million PSMs generated from synthetic peptides. To avoid overrepresentation of some peptides in this dataset, we randomly selected at most 100 PSMs per unique peptide, similar to the processing that was done during the creation of the MassIVE-KB dataset. This pre-selection step reduced the size of the PROSPECT dataset to 12.6 million PSMs. Finally, to create a non-enzymatic dataset containing 1 million peptides, we selected 50,000 PSMs for each canonical amino acid. These PSMs were selected at random from MassIVE-KB, then supplemented as necessary from PROSPECT to obtain the desired count (see Yilmaz et al. [Yilmaz2023] Supplementary Table S1). This dataset contained PSMs from 247,859 unique peptides, which were randomly split into training, validation and test sets in the ratio 80/10/10. FTP directory contains the mgf files corresponding to train, validation and test splits. [Wang2018] M. Wang, J. Wang, J. Carver, B. Pullman, S. Won Cha, N. Bandeira, "Assembling the Community-Scale Discoverable Human Proteome", Cell Systems, Volume 7, Issue 4, 2018. [Shouman2022] O. Shouman, W. Gabriel, V. Giurcoiu, V. Sternlicht, M. Wilhelm, "PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics", NeurIPS Datasets and Benchmarks, 2022 [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

Dataset Information

Fine Tuning of a Type 1 Interferon Antagonist.

Publications

Fine Tuning of a Type 1 Interferon Antagonist.

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