Dataset Information

Making light work of fine-tuning channelrhodopsins.

ABSTRACT: The development of genetically engineered proteins that can control cell excitability with light have revolutionized our understanding of the nervous system. The most widely used of these optogenetic tools is the light-gated ion channel, channelrhodopsin 2 (ChR2). A new study by Cho et al. describes the development of ChR2 variants with improved photocurrents and more selective ion permeability using an automated multifaceted fluorescence-based screening. This methodological framework holds promise not only in refining features of ChR2, but also for other proteins in which fluorescence phenotyping is possible.

SUBMITTER: Moorhouse AJ

PROVIDER: S-EPMC6422102 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Making light work of fine-tuning channelrhodopsins.

Moorhouse Andrew J AJ Power John M JM

The Journal of biological chemistry 20190301 11

The development of genetically engineered proteins that can control cell excitability with light have revolutionized our understanding of the nervous system. The most widely used of these optogenetic tools is the light-gated ion channel, channelrhodopsin 2 (ChR2). A new study by Cho <i>et al.</i> describes the development of ChR2 variants with improved photocurrents and more selective ion permeability using an automated multifaceted fluorescence-based screening. This methodological framework hol ...[more]

PMID: 30877261

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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

Making light work of fine-tuning channelrhodopsins.

Publications

Making light work of fine-tuning channelrhodopsins.

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