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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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Raw NGS Data for "Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins"

Authors: Megan Leander, Zhuang Liu, Qiang Cui, Srivatsan Raman;

Raw NGS Data for "Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins"

Abstract

This directory contains relevant fastq files used for deep sequencing analysis in the publication “Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins”. Fastq files are provided for presorted, uninduced and induced populations from DMS experiments of four homologs (TtgR, TetR, RolR, and MphR). Three replicates were performed for each sample. Data analysis of this deep sequencing data was performed using custom scripts, which are described in the methods section of the publication.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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