Regulation of protein–protein binding by coupling between phosphorylation and intrinsic disorder: analysis of human protein complexes

Transcription of the Saccharomyces cerevisiae COX6 gene is regulated by heme and carbon source. It is also affected by the HAP2/3/4 transcription factor complex and by SNF1 and SSN6. Previously, we have shown that most of this regulation is mediated through UAS6, an 84-bp upstream activation segment of the COX6 promoter. In this study, by using linker scanning mutagenesis and protein binding assays, we have identified three elements within UAS6 and one element downstream of it that are important. Two of these, HDS1 (heme-dependent site 1; between -269 and -251 bp) and HDS2 (between -228 and -220 bp), mediate regulation of COX6 by heme. Both act negatively. The other two elements, domain 2 (between -279 and -269 bp) and domain 1 (between -302 and -281 bp), act positively. Domain 2 is required for optimal transcription in cells grown in repressing but not derepressing carbon sources. Domain 1 is essential for transcription per se in cells grown on repressing carbon sources, is required for optimal transcription in cells grown on a derepressing carbon source, is sufficient for glucose repression-derepression, and is the element of UAS6 at which HAP2 affects COX6 transcription. This element contains the major protein binding sites within UAS6. It has consensus binding sequences for ABF1 and HAP2. Gel mobility shift experiments show that domain 1 binds ABF1 and forms different numbers of DNA-protein complexes in extracts from cells grown in repressing or derepressing carbon sources. In contrast, gel mobility shift experiments have failed to reveal that HAP2 or HAP3 binds to domain 1 or that hap3 mutations affect the complexes bound to it. Together, these findings permit the following conclusions: COX6 transcription is regulated both positively and negatively; heme and carbon source exert their effects through different sites; domain 1 is absolutely essential for transcription on repressing carbon sources; ABF1 is a major component in the regulation of COX6 transcription; and the HAP2/3/4 complex most likely affects COX6 transcription indirectly.

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Systematic discovery of endogenous human ribonucleoprotein complexes

10.1101/480061 ◽

2018 ◽

Cited By ~ 2

Author(s):

Anna L. Mallam ◽

Wisath Sae-Lee ◽

Jeffrey M. Schaub ◽

Fan Tu ◽

Anna Battenhouse ◽

...

Keyword(s):

Rna Binding ◽

Rna Binding Proteins ◽

Protein Complexes ◽

Embryonic Stem ◽

Human Protein ◽

Disease States ◽

Ribonucleoprotein Complexes ◽

Associated Proteins ◽

Domains Of Life ◽

Factor C

AbstractRNA-binding proteins (RBPs) play essential roles in biology and are frequently associated with human disease. While recent studies have systematically identified individual RBPs, their higher order assembly intoRibonucleoprotein (RNP) complexes has not been systematically investigated. Here, we describe a proteomics method for systematic identification of RNP complexes in human cells. We identify 1,428 protein complexes that associate with RNA, indicating that over 20% of known human protein complexes contain RNA. To explore the role of RNA in the assembly of each complex, we identify complexes that dissociate, change composition, or form stable protein-only complexes in the absence of RNA. Importantly, these data also provide specific novel insights into the function of well-studied protein complexes not previously known to associate with RNA, including replication factor C (RFC) and cytokinetic centralspindlin complex. Finally, we use our method to systematically identify cell-type specific RNA-associated proteins in mouse embryonic stem cells. We distribute these data as a resource, rna.MAP (rna.proteincomplexes.org) which provides a comprehensive dataset for the study of RNA-associated protein complexes. Our system thus provides a novel methodology for further explorations across human tissues and disease states, as well as throughout all domains of life.SummaryAn exploration of human protein complexes in the presence and absence of RNA reveals endogenous ribonucleoprotein complexes

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Differential nuclear protein binding to 5-azacytosine-containing DNA as a potential mechanism for 5-aza-2'-deoxycytidine resistance

Molecular and Cellular Biology ◽

10.1128/mcb.7.9.3076-3083.1987 ◽

1987 ◽

Vol 7 (9) ◽

pp. 3076-3083

Author(s):

L A Michalowsky ◽

P A Jones

Keyword(s):

Protein Binding ◽

Cell Line ◽

Cytosine Methylation ◽

Nuclear Protein ◽

Protein Complexes ◽

Lethal Dose ◽

Tight Binding ◽

Cell Types ◽

Resistant Cell ◽

Relative Increase

A clonal cell line (56-42) that was stably and exclusively resistant to the toxic effects of the antileukemic agent 5-aza-2'-deoxycytidine (5-aza-CdR) was derived from C3H 10T1/2 C18 cells after multiple treatments with 5-aza-CdR. The 50% lethal dose of 5-aza-CdR for these cells was 1.3 microM, which was 15-fold greater than that for the parental cells. Cell line 56-42 was slightly cross-resistant to the ribo-analog 5-azacytidine, but was sensitive to the nucleoside analog 1-beta-D-arabinofuranosylcytosine and to colcemid. Both parental and resistant cell lines incorporated equimolar amounts of 5-aza-CdR into DNA. Resistance was therefore not due to decreased activation, increased detoxification, or reduced incorporation of the drug. The overall level of cytosine methylation in the resistant clone was 80% lower than the level in the sensitive cells. Therefore, the potential number of hemimethylated sites created by the incorporation of equivalent amounts of 5-aza-CdR into the DNA of the two cell types was much greater in the sensitive cells. Furthermore, 5-azacytosine-substituted DNA from the sensitive cells bound 100% more nuclear protein in the form of highly stable complexes. The incorporation of 5-aza-CdR opposite methylated cytosine residues in DNA of the sensitive cells thus resulted in increased nuclear protein binding at hemimethylated sites. This relative increase in tight-binding protein complexes was shown to occur in living cells and may well disrupt replication and transcription and instigate cell death. The differential binding of proteins to hypomethylated, azacytosine-containing DNA may thus mediate a novel mechanism of drug resistance.

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Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes

Molecular Systems Biology ◽

10.15252/msb.20167490 ◽

2017 ◽

Vol 13 (6) ◽

pp. 932 ◽

Cited By ~ 84

Author(s):

Kevin Drew ◽

Chanjae Lee ◽

Ryan L Huizar ◽

Fan Tu ◽

Blake Borgeson ◽

...

Keyword(s):

Mass Spectrometry ◽

Protein Complexes ◽

Human Protein

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ISLAND: in-silico proteins binding affinity prediction using sequence information

BioData Mining ◽

10.1186/s13040-020-00231-w ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Wajid Arshad Abbasi ◽

Adiba Yaseen ◽

Fahad Ul Hassan ◽

Saiqa Andleeb ◽

Fayyaz Ul Amir Afsar Minhas

Keyword(s):

Machine Learning ◽

Protein Binding ◽

Binding Affinity ◽

State Of The Art ◽

Protein Complexes ◽

Protein Structures ◽

Sequence Information ◽

Binding Affinity Prediction ◽

Generalization Performance ◽

Affinity Prediction

Abstract Background Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning. Method We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity. Results We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software. Conclusion This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem.

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A new computational approach to analyze human protein complexes and predict novel protein interactions

Genome Biology ◽

10.1186/gb-2007-8-12-r256 ◽

2007 ◽

Vol 8 (12) ◽

pp. R256 ◽

Cited By ~ 8

Author(s):

Sara Zanivan ◽

Ilaria Cascone ◽

Chiara Peyron ◽

Ivan Molineris ◽

Serena Marchio ◽

...

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Computational Approach ◽

Human Protein ◽

Novel Protein

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