scholarly journals Targeted Searches for Novel Peptides in Big Mass Spectrometry Data Sets

2017 ◽  
Author(s):  
Yu Gao ◽  
Jiao Ma ◽  
Alan Saghatelian ◽  
John R. Yates
Keyword(s):  
Mass Spectrometry ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Protein Sequences ◽  
Interaction Network ◽  
Database Search ◽  
Mass Spectrometry Data ◽  
Data Sets ◽  
Speed Up ◽  
Guilt By Association

We present Post-Acquisition Targeted Searches (PATS), an easy-to-use tool that allows the identification of novel peptide/protein sequences from existing big mass spectrometry data sets. PATS filters out the unrelated peptidome before the time-consuming database search to significantly speed up the identification. Using interactome data sets, PATS visualizes protein interaction network and helps to assign putative functions to the target protein based on the “guilt by association” concept.

Construction and Analysis of Protein-Protein Interaction Network

2019 ◽  
pp. 204-220
Author(s):  
Divya Dasagrandhi ◽  
Arul Salomee Kamalabai Ravindran ◽  
Anusuyadevi Muthuswamy ◽  
Jayachandran K. S.
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Data Sets ◽  
Protein Protein Interaction ◽  
Huge Data ◽  
Associated Proteins ◽  
High Throughput Experiments ◽  
Generation Sequencing ◽  
Protein Protein Interaction Network

Understanding the mechanisms of a disease is highly complicated due to the complex pathways involved in the disease progression. Despite several decades of research, the occurrence and prognosis of the diseases is not completely understood even with high throughput experiments like DNA microarray and next-generation sequencing. This is due to challenges in analysis of huge data sets. Systems biology is one of the major divisions of bioinformatics and has laid cutting edge techniques for the better understanding of these pathways. Construction of protein-protein interaction network (PPIN) guides the modern scientists to identify vital proteins through protein-protein interaction network, which facilitates the identification of new drug target and associated proteins. The chapter is focused on PPI databases, construction of PPINs, and its analysis.

scholarly journals Probing the Protein Interaction Network of Pseudomonas aeruginosa Cells by Chemical Cross-Linking Mass Spectrometry

Structure ◽  
2015 ◽  
Vol 23 (4) ◽  
pp. 762-773 ◽  
Author(s):  
Arti T. Navare ◽  
Juan D. Chavez ◽  
Chunxiang Zheng ◽  
Chad R. Weisbrod ◽  
Jimmy K. Eng ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Pseudomonas Aeruginosa ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Cross Linking ◽  
Chemical Cross Linking

scholarly journals The Aryl-Hydrocarbon Receptor Protein Interaction Network (AHR-PIN) as Identified by Tandem Affinity Purification (TAP) and Mass Spectrometry

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Dorothy M. Tappenden ◽  
Hye Jin Hwang ◽  
Longlong Yang ◽  
Russell S. Thomas ◽  
John J. LaPres
Keyword(s):  
Mass Spectrometry ◽  
Gene Regulation ◽  
Aryl Hydrocarbon Receptor ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Affinity Purification ◽  
Interaction Network ◽  
Tandem Affinity Purification ◽  
Toxic Response ◽  
Aryl Hydrocarbon

The aryl-hydrocarbon receptor (AHR), a ligand activated PAS superfamily transcription factor, mediates most, if not all, of the toxicity induced upon exposure to various dioxins, dibenzofurans, and planar polyhalogenated biphenyls. While AHR-mediated gene regulation plays a central role in the toxic response to dioxin exposure, a comprehensive understanding of AHR biology remains elusive. AHR-mediated signaling starts in the cytoplasm, where the receptor can be found in a complex with the heat shock protein of 90 kDa (Hsp90) and the immunophilin-like protein, aryl-hydrocarbon receptor-interacting protein (AIP). The role these chaperones and other putative interactors of the AHR play in the toxic response is not known. To more comprehensively define the AHR-protein interaction network (AHR-PIN) and identify other potential pathways involved in the toxic response, a proteomic approach was undertaken. Using tandem affinity purification (TAP) and mass spectrometry we have identified several novel protein interactions with the AHR. These interactions physically link the AHR to proteins involved in the immune and cellular stress responses, gene regulation not mediated directly via the traditional AHR:ARNT heterodimer, and mitochondrial function. This new insight into the AHR signaling network identifies possible secondary signaling pathways involved in xenobiotic-induced toxicity.

scholarly journals RPINaptaBASE: a Database for RNA-Protein Interaction Network Analysis and Aptamer Design

2019 ◽  
Author(s):  
Mahsa Torkamanian-Afshar ◽  
Hossein Lanjanian ◽  
Sajjad Nematzadeh ◽  
Maryam Tabarzad ◽  
Ali Najafi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Analysis ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Protein Sequences ◽  
Interaction Network ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Wide Range

Abstract Abstract Background The RNA-protein interactions play crucial roles in the biological processes. Recent developments to clarify RNA and protein structural features have the urgent need for designing various databases, related to the specificity and the mechanism of the underlying interactions between a protein and an RNA molecule. The majority of these databases have focused on RNAs or proteins macromolecules independently, and they do not have the capability to run integrated queries on the RNA-protein complex. Theses existing databases have a linear query structure. Furthermore, they only focus on interacting (positive) samples and they do not contain non-interacting (negative) samples. Results We developed a Database for RNA-Protein Interaction Network Analysis and Aptamer Design (RPINaptaBASE). RPINaptaBASE has a nested query approach that enables users to apply nonlinear query analysis. The query engine module contains a wide range of features related to RNA and protein sequences and secondary structure elements of these macromolecules, which are helpful to generate custom datasets, especially for machine learning approaches. In this version, more than 175 features were calculated and available to users. It provides a web interface with download management services allowing users to generate desired datasets of unique RNA or protein sequences in independent lists. Furthermore; the web service empowers users to create artificial datasets of positive and negative samples from RNA-protein complexes. In order to present negative samples, the idea of distinguishing protein sequences by their clans and families was employed to efficiently generate non-interacting pairs. Conclusion This database prepares a user-friendly platform to study RNA-protein interactions. It also provides an important simplified contribution to the oligonucleotide-aptamer design process using machine learning algorithms. RPINaptaBASE is freely available at http://rpinbase.com

scholarly journals Low throughput protocol for immunoprecipitation followed by mass spectrometry of cells stably expressing an HA-tagged protein v1

2020 ◽  
Author(s):  
Harper not provided not provided JW
Keyword(s):  
Mass Spectrometry ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Gene Editing ◽  
Protein Complexes ◽  
Interaction Network ◽  
Epitope Tag ◽  
Powerful Approach

Analysis of protein complexes by mass spectrometry provides a powerful approach for identifying proteins that associate with other proteins. Frequently, this can be done by expressing the protein of interest with an epitope tag, such as a Hemagglutinin-A (HA) epitope, using either a stably expressed lentivirus or by gene editing the HA epitope into the gene of interest. The protocol has been used extensively to create the Bioplex protein interaction network [Huttlin et al Nature. 545:505-509 (2017); Huttlin et al Cell, 162: 425-440 (2015)].

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