scholarly journals Characterization of major chromatin assembly proteins in tetrahymena thermophile using proeomic and molecular evolutionary analyses

2021 ◽  
Author(s):  
Syed N Shah
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Affinity Purification ◽  
Regulation Of Gene Expression ◽  
Chromatin Assembly ◽  
Histone Chaperones ◽  
Protein Protein Interactions ◽  
Selective Forces ◽  
Assembly Proteins

Histones H3/H4 are deposited onto DNA in a replication-dependent or independent fashion by the CAF1 and HIRA protein complexes. Despite the identification of these protein complexes, mechanistic details remain unclear. Recently, we showed that in T. thermophila histone chaperones Nrp1, Asf1 and the Impβ6 importin function together to transport newly synthesized H3/H4 from the cytoplasm to the nucleus. To characterize chromatin assembly proteins in T.thermophila, I used affinity purification combined with mass spectrometry to identify protein-protein interactions of Nrp1, Cac2 subunit of CAF1, HIRA and histone modifying Hat1-complex in T. thermophila. I found that the three-subunit T.thermophila CAF1 complex interacts with Casein Kinase 2 (CKII), possibly accounting for previously reported human CAF1phosphorylation. I also found that Hat2 subunit of HAT1 complex is also shared by CAF1 complex as its Cac3 subunit. This suggests that Hat2/Cac3 might exist in two separate pools of protein complexes. Remarkably, proteomic analysis of Hat2/Cac3 in turn revealed that it forms several complexes with other proteins including SIN3, RXT3, LIN9 and TESMIN, all of which have known roles in the regulation of gene expression. Finally, I asked how selective forces might have impacted on the function of proteins involved in H3/H4 transport. Focusing on NASP which possesses several TPR motifs, I showed that its protein-protein interactions are conserved in T. thermophila. Using molecular evolutionary methods I show that different TPRs in NASP evolve at different rates possibly accounting for the functional diversity observed among different family members.

scholarly journals Characterization of major chromatin assembly proteins in tetrahymena thermophile using proeomic and molecular evolutionary analyses

2021 ◽  
Author(s):  
Syed N Shah
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Affinity Purification ◽  
Regulation Of Gene Expression ◽  
Chromatin Assembly ◽  
Histone Chaperones ◽  
Protein Protein Interactions ◽  
Selective Forces ◽  
Assembly Proteins

Histones H3/H4 are deposited onto DNA in a replication-dependent or independent fashion by the CAF1 and HIRA protein complexes. Despite the identification of these protein complexes, mechanistic details remain unclear. Recently, we showed that in T. thermophila histone chaperones Nrp1, Asf1 and the Impβ6 importin function together to transport newly synthesized H3/H4 from the cytoplasm to the nucleus. To characterize chromatin assembly proteins in T.thermophila, I used affinity purification combined with mass spectrometry to identify protein-protein interactions of Nrp1, Cac2 subunit of CAF1, HIRA and histone modifying Hat1-complex in T. thermophila. I found that the three-subunit T.thermophila CAF1 complex interacts with Casein Kinase 2 (CKII), possibly accounting for previously reported human CAF1phosphorylation. I also found that Hat2 subunit of HAT1 complex is also shared by CAF1 complex as its Cac3 subunit. This suggests that Hat2/Cac3 might exist in two separate pools of protein complexes. Remarkably, proteomic analysis of Hat2/Cac3 in turn revealed that it forms several complexes with other proteins including SIN3, RXT3, LIN9 and TESMIN, all of which have known roles in the regulation of gene expression. Finally, I asked how selective forces might have impacted on the function of proteins involved in H3/H4 transport. Focusing on NASP which possesses several TPR motifs, I showed that its protein-protein interactions are conserved in T. thermophila. Using molecular evolutionary methods I show that different TPRs in NASP evolve at different rates possibly accounting for the functional diversity observed among different family members.

scholarly journals Characterization of known protein complexes using k-connectivity and other topological measures

F1000Research ◽  
2015 ◽  
Vol 2 ◽  
pp. 172
Author(s):  
Suzanne R Gallagher ◽  
Debra S Goldberg
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Clustering Coefficient ◽  
Edge Density ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Unknown Protein ◽  
Topological Measures

Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless of the number of proteins in the complex. Because of this, as complex size increases, several measures of the complex decrease within protein-protein interaction networks. However, k-connectivity, the number of vertices or edges that need to be removed in order to disconnect a graph, may be consistently high for protein complexes. The property of k-connectivity has been little used previously in the investigation of protein-protein interactions. To understand the discriminative power of k-connectivity and other topological measures for identifying unknown protein complexes, we characterized these properties in known Saccharomyces cerevisiae protein complexes in networks generated both from highly accurate X-ray crystallography experiments which give an accurate model of each complex, and also as the complexes appear in high-throughput yeast 2-hybrid studies in which new complexes may be discovered. We also computed these properties for appropriate random subgraphs.We found that clustering coefficient, mutual clustering coefficient, and k-connectivity are better indicators of known protein complexes than edge density, degree, or betweenness. This suggests new directions for future protein complex-finding algorithms.

scholarly journals A rapid and accurate approach for Prediction of interactomes from co-elution data (PrInCE)

2017 ◽  
Author(s):  
R. Greg Stacey ◽  
Michael A. Skinnider ◽  
Nichollas E. Scott ◽  
Leonard J. Foster
Keyword(s):  
Protein Interactions ◽  
Isotope Labeling ◽  
Protein Complexes ◽  
Affinity Purification ◽  
Ease Of Use ◽  
Label Free ◽  
Protein Protein Interactions ◽  
Bioinformatics Pipeline ◽  
Vast Number ◽  
Free Data

AbstractBackgroundAn organism’s protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome.ResultsHere we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, better performance, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2015b). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE, where usage instructions can be found. An example dataset and output are also provided for testing purposes.ConclusionsPrInCE is the first fast and easy-to-use data analysis pipeline that predicts interactomes and protein complexes from co-elution data. PrInCE allows researchers without bioinformatics proficiency to analyze high-throughput co-elution datasets.

scholarly journals RNA Proximity Labeling: A New Detection Tool for RNA–Protein Interactions

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2270
Author(s):  
Ronja Weissinger ◽  
Lisa Heinold ◽  
Saira Akram ◽  
Ralf-Peter Jansen ◽  
Orit Hermesh
Keyword(s):  
Protein Interactions ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Protein Complexes ◽  
Affinity Purification ◽  
Cellular Functions ◽  
Cellular Compartments ◽  
Rna Protein Complexes

Multiple cellular functions are controlled by the interaction of RNAs and proteins. Together with the RNAs they control, RNA interacting proteins form RNA protein complexes, which are considered to serve as the true regulatory units for post-transcriptional gene expression. To understand how RNAs are modified, transported, and regulated therefore requires specific knowledge of their interaction partners. To this end, multiple techniques have been developed to characterize the interaction between RNAs and proteins. In this review, we briefly summarize the common methods to study RNA–protein interaction including crosslinking and immunoprecipitation (CLIP), and aptamer- or antisense oligonucleotide-based RNA affinity purification. Following this, we focus on in vivo proximity labeling to study RNA–protein interactions. In proximity labeling, a labeling enzyme like ascorbate peroxidase or biotin ligase is targeted to specific RNAs, RNA-binding proteins, or even cellular compartments and uses biotin to label the proteins and RNAs in its vicinity. The tagged molecules are then enriched and analyzed by mass spectrometry or RNA-Seq. We highlight the latest studies that exemplify the strength of this approach for the characterization of RNA protein complexes and distribution of RNAs in vivo.

Protein–protein interactions

2010 ◽  
Vol 38 (4) ◽  
pp. 875-878 ◽  
Author(s):  
Mike P. Williamson ◽  
Michael J. Sutcliffe
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Present Article ◽  
High Throughput ◽  
Protein Interactions ◽  
Metabolic Pathway ◽  
Protein Complexes ◽  
Affinity Purification ◽  
Good Evidence ◽  
Protein Protein Interactions ◽  
Two Hybrid

In the present article, we describe the two standard high-throughput methods for identification of protein complexes: two-hybrid screens and TAP (tandem affinity purification) tagging. These methods have been used to characterize the interactome of Saccharomyces cerevisiae, showing that the majority of proteins are part of complexes, and that complexes typically consist of a core to which are bound ‘party’ and ‘dater’ proteins. Complexes typically are merely the sum of their parts. A particularly interesting type of complex is the metabolon, containing enzymes within the same metabolic pathway. There is reasonably good evidence that metabolons exist, but they have not been detected using high-thoughput assays, possibly because of their fragility.

scholarly journals Characterization of known protein complexes using k-connectivity and other topological measures

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 172 ◽  
Author(s):  
Suzanne R Gallagher ◽  
Debra S Goldberg
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Clustering Coefficient ◽  
Edge Density ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Unknown Protein ◽  
Topological Measures

Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless of the number of proteins in the complex. Because of this, as complex size increases, several measures of the complex decrease within protein-protein interaction networks. However, k-connectivity, the number of vertices or edges that need to be removed in order to disconnect a graph, may be consistently high for protein complexes. The property of k-connectivity has been little used previously in the investigation of protein-protein interactions. To understand the discriminative power of k-connectivity and other topological measures for identifying unknown protein complexes, we characterized these properties in known Saccharomyces cerevisiae protein complexes in networks generated both from highly accurate X-ray crystallography experiments which give an accurate model of each complex, and also as the complexes appear in high-throughput yeast 2-hybrid studies in which new complexes may be discovered. We also computed these properties for appropriate random subgraphs. We found that clustering coefficient, mutual clustering coefficient, and k-connectivity are better indicators of known protein complexes than edge density, degree, or betweenness. This suggests new directions for future protein complex-finding algorithms.

scholarly journals Recent advances in proximity-based labeling methods for interactome mapping

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 135 ◽  
Author(s):  
Laura Trinkle-Mulcahy
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Protein Complexes ◽  
Affinity Purification ◽  
Small Scale ◽  
Protein Protein Interactions ◽  
Large Scale Network ◽  
Complementary Approach

Proximity-based labeling has emerged as a powerful complementary approach to classic affinity purification of multiprotein complexes in the mapping of protein–protein interactions. Ongoing optimization of enzyme tags and delivery methods has improved both temporal and spatial resolution, and the technique has been successfully employed in numerous small-scale (single complex mapping) and large-scale (network mapping) initiatives. When paired with quantitative proteomic approaches, the ability of these assays to provide snapshots of stable and transient interactions over time greatly facilitates the mapping of dynamic interactomes. Furthermore, recent innovations have extended biotin-based proximity labeling techniques such as BioID and APEX beyond classic protein-centric assays (tag a protein to label neighboring proteins) to include RNA-centric (tag an RNA species to label RNA-binding proteins) and DNA-centric (tag a gene locus to label associated protein complexes) assays.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

2019 ◽  
Vol 26 (21) ◽  
pp. 3890-3910 ◽  
Author(s):  
Branislava Gemovic ◽  
Neven Sumonja ◽  
Radoslav Davidovic ◽  
Vladimir Perovic ◽  
Nevena Veljkovic
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Experimental Studies ◽  
Protein Protein Interactions ◽  
Web Based ◽  
Prediction Tools ◽  
Physiological Processes ◽  
Modern Drug ◽  
Ppi Prediction

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

Protein Interaction Domains and Post-Translational Modifications: Structural Features and Drug Discovery Applications

2020 ◽  
Vol 27 (37) ◽  
pp. 6306-6355 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Information ◽  
Protein Complexes ◽  
Structural Features ◽  
Protein Protein Interactions ◽  
Modular Architecture ◽  
Post Translational Modifications ◽  
Interaction Domains

Background:: Many pathways regarding healthy cells and/or linked to diseases onset and progression depend on large assemblies including multi-protein complexes. Protein-protein interactions may occur through a vast array of modules known as protein interaction domains (PIDs). Objective:: This review concerns with PIDs recognizing post-translationally modified peptide sequences and intends to provide the scientific community with state of art knowledge on their 3D structures, binding topologies and potential applications in the drug discovery field. Method:: Several databases, such as the Pfam (Protein family), the SMART (Simple Modular Architecture Research Tool) and the PDB (Protein Data Bank), were searched to look for different domain families and gain structural information on protein complexes in which particular PIDs are involved. Recent literature on PIDs and related drug discovery campaigns was retrieved through Pubmed and analyzed. Results and Conclusion:: PIDs are rather versatile as concerning their binding preferences. Many of them recognize specifically only determined amino acid stretches with post-translational modifications, a few others are able to interact with several post-translationally modified sequences or with unmodified ones. Many PIDs can be linked to different diseases including cancer. The tremendous amount of available structural data led to the structure-based design of several molecules targeting protein-protein interactions mediated by PIDs, including peptides, peptidomimetics and small compounds. More studies are needed to fully role out, among different families, PIDs that can be considered reliable therapeutic targets, however, attacking PIDs rather than catalytic domains of a particular protein may represent a route to obtain selective inhibitors.

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