New tool enables mapping of protein interaction networks at scale

Date:
August 3, 2021
Source:
University of California - San Diego
Summary:
Bioengineers have developed a technology capable of revealing the
protein-protein interactions (PPI) among thousands of proteins
in a cell, in a single experiment. The tool, called PROPER-seq
(protein-protein interaction sequencing), allows researchers to map
the PPI network from their cells of interest within several weeks,
without any specialized resources such as antibodies or pre-made
gene libraries.



FULL STORY ========================================================================== Proteins -- long chains of amino acids -- each play a unique role in
keeping our cells and bodies functioning, from carrying out chemical
reactions, to delivering messages, and protecting us from potentially
harmful foreign invaders. More recent research has shown that these
proteins not only serve their individual purpose, but also interact with
other proteins to carry out even more numerous and complex functions
through these protein-protein interactions (PPI).


========================================================================== Collectively, all the protein-protein interactions in a cell form a PPI network. Experimentally identifying a PPI network within human cells
has required a tremendous amount of time and resources, with experiments required to identify every individual PPI, and many additional experiments
to investigate these protein pairs for network-level interactions.

Now, bioengineers at the University of California San Diego have developed
a technology capable of revealing the PPIs among thousands of proteins,
in a single experiment. The tool, called PROPER-seq (protein-protein interaction sequencing), allows researchers to map the PPI network from
their cells of interest within several weeks, without any specialized
resources such as antibodies or premade gene libraries.

The researchers describe this technology in Molecular Cell on August
3. They applied PROPER-seq on human embryonic kidney cells, T lymphocytes,
and endothelial cells, and identified 210,518 PPIs involving 8,635
proteins.

"PROPER-seq is capable of scanning the order of 10,000x10,000 protein
pairs in one experiment," said Kara Johnson, a recent UC San Diego bioengineering Ph.D.

alumna and the first author of this paper. The research was conducted
in bioengineering professor Sheng Zhong's lab.

The central idea of PROPER-seq is to label every PPI with a unique DNA sequence, and then read these DNA sequence labels through next-generation sequencing. To implement this idea, Zhong's team developed a technique
called SMART-display, which attaches a unique DNA barcode to every
protein. They also devised a method called "Incubation, ligation and sequencing" (INLISE) to sequence the pair of DNA barcodes that are
attached to two interacting proteins. The third component of PROPER-seq is
a software package called PROPERseqTools, that incorporates statistical
tools to identify the PPIs from the DNA sequencing data. This trio of
tools -- SMART-display, INLISE, and PROPERseqTools -- together is known
as PROPER-seq.



==========================================================================
A laboratory would start the PROPER-seq protocol with their cells of
interest, and obtain the output as a list of identified PPIs. The user
can also get the DNA sequence read counts and other statistics associated
with every identified PPI.

Zhong's team applied PROPER-seq on human embryonic kidney cells, T
lymphocytes, and endothelial cell,s and obtained 210,518 PPIs involving
8,635 proteins. The team created a public database with a web interface
to download, search, and visualize these PPIs.

The team validated the PROPER-seq-identified PPIs (called PROPER v1.0)
with previously characterized PPIs documented in PPI databases. The team
found more than 1,300 and 2,400 PPIs in PROPER v1.0 are supported
by previous co- immunoprecipitation experiments and affinity
purification-mass spectrometry experiments, respectively.

The team experimentally validated four PROPER-seq identified PPIs that
have not been reported in the literature. These four PPIs involve PARP1,
a critical protein for DNA repair and a drug target of several human
cancers, and four other proteins involved in the trafficking of molecules
and transcription regulation. These validations suggest mechanistic
links between PARP1 and import/export of molecules to/from the nucleus
as well as gene transcription.

Their results show that PROPER v1.0 overlaps with more than
17,000 computationally predicted PPIs without prior experimental
validation. The experimental support offered by PROPER-seq to these
previously uncharacterized PPIs suggests the solid predictive ability
of protein structure-based computational models.

The team found PROPER v1.0 overlaps with one hundred synthetic lethal
(SL) gene pairs. An SL gene pair can cause cell death when both genes
of this gene pair are lost. This finding suggests a connection between
physical interactions (PPIs) and human genetic interactions.

Looking forward, the team hopes PROPER-seq can assist researchers in
screening many protein pairs and identify PPIs of interest. In addition,
the PROPER-seq identified PPIs from different labs can expand the PPI
networks' reference maps and illuminate cell-type-specific PPIs.

Other major contributors to this work include UC San Diego bioengineering
Ph.D.

student Zhijie Qi, bioengineering postdoc alumna Zhangmin Yan, and bioinformatics and systems biology Ph.D. student Xingzhao Wen, who
carried out protein network analysis. Professor Zhen Chen at City
of Hope collaborated with Zhong's team on validation of PROPER-seq
identified PPIs.

This work is supported by the National Institutes of Health, and the
Ella Fitzgerald Charitable Foundation.

========================================================================== Story Source: Materials provided by
University_of_California_-_San_Diego. Note: Content may be edited for
style and length.


========================================================================== Journal Reference:
1. Kara L. Johnson, Zhijie Qi, Zhangming Yan, Xingzhao Wen, Tri
C. Nguyen,
Kathia Zaleta-Rivera, Chien-Ju Chen, Xiaochen Fan, Kiran Sriram,
Xueyi Wan, Zhen Bouman Chen, Sheng Zhong. Revealing protein-protein
interactions at the transcriptome scale by sequencing. Molecular
Cell, 2021; DOI: 10.1016/j.molcel.2021.07.006 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210803121321.htm

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