Extended Data Fig. 5 | Comparison of the drug screening results using different variations of the network proximity-based screening methods. (a) Network proximity-based drug screening using directed human protein-protein interactome vs. undirected human protein-protein interactome. (b) Network proximity-based drug screening using degree preserved edge shuffling vs. degree preserved node shuffling. PCC, Pearson correlation coefficient.
Phosphorylation is one of the most dynamic and widespread post-translational modifications regulating virtually every aspect of eukaryotic cell biology. Here we present a comprehensive phosphoproteomic dataset for budding yeast, comprised of over 30,000 high confidence phosphorylation sites identified by mass spectrometry. This single dataset nearly doubles the size of the known phosphoproteome in budding yeast and defines a set of cell cycle-regulated phosphorylation events. With the goal of enhancing the identification of functional phosphorylation events, we performed computational positioning of phosphorylation sites on available 3D protein structures and systematically identified events predicted to regulate protein complex architecture. Results reveal a large number of phosphorylation sites mapping to or near protein interaction interfaces, many of which result in steric or electrostatic "clashes" predicted to disrupt the interaction. Phosphorylation site mutants experimentally validate our predictions and support a role for phosphorylation in negatively regulating protein-protein interactions. With the advancement of Cryo-EM and the increasing number of available structures, our approach should help drive the functional and spatial exploration of the phosphoproteome.
Studying protein-protein interaction networks provide key evidence for the underlying molecular mechanisms. Mass spectrometry-based proteomic approaches have been playing a pivotal role in deciphering these interaction networks, along with precise quantification for individual interactions. In this mini-review we discuss the available techniques and methods for qualitative and quantitative elucidation of protein-protein interaction networks. We then summarize the down-stream computational strategies for identification and quantification of interactions from those techniques. Finally, we highlight the challenges and limitations of current computational pipelines in eliminating false positive interactors, followed by a summary of the innovative algorithms to address these issues, along with the scope for future improvements.
Long noncoding
RNA
s (lnc
RNA
s) can regulate target gene expression by acting in
cis
(locally) or in
trans
(non‐locally). Here, we performed genome‐wide expression analysis of Toll‐like receptor (
TLR
)‐stimulated human macrophages to identify pairs of
cis
‐acting lnc
RNA
s and protein‐coding genes involved in innate immunity. A total of 229 gene pairs were identified, many of which were commonly regulated by signaling through multiple
TLR
s and were involved in the cytokine responses to infection by group B
Streptococcus
. We focused on elucidating the function of one lnc
RNA
, named
lnc‐
MARCKS
or
ROCKI
(Regulator of Cytokines and Inflammation), which was induced by multiple
TLR
stimuli and acted as a master regulator of inflammatory responses.
ROCKI
interacted with
APEX
1 (apurinic/apyrimidinic endodeoxyribonuclease 1) to form a ribonucleoprotein complex at the
MARCKS
promoter. In turn,
ROCKI
–
APEX
1 recruited the histone deacetylase
HDAC
1, which removed the H3K27ac modification from the promoter, thus reducing
MARCKS
transcription and subsequent Ca
2+
signaling and inflammatory gene expression. Finally, genetic variants affecting
ROCKI
expression were linked to a reduced risk of certain inflammatory and infectious disease in humans, including inflammatory bowel disease and tuberculosis. Collectively, these data highlight the importance of
cis
‐acting lnc
RNA
s in
TLR
signaling, innate immunity, and pathophysiological inflammation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.