scholarly journals The PPI3D web server for searching, analyzing and modeling protein–protein interactions in the context of 3D structures

2016 ◽  
pp. btw756 ◽  
Author(s):  
Justas Dapkūnas ◽  
Albertas Timinskas ◽  
Kliment Olechnovič ◽  
Mindaugas Margelevičius ◽  
Rytis Dičiūnas ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Web Server ◽  
Protein Protein Interactions ◽  
3D Structures

Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation

2019 ◽  
Vol 21 (5) ◽  
pp. 1798-1805 ◽  
Author(s):  
Kai Yu ◽  
Qingfeng Zhang ◽  
Zekun Liu ◽  
Yimeng Du ◽  
Xinjiao Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Web Server ◽  
Data Sets ◽  
Lysine Acetylation ◽  
Protein Acetylation ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Protein Lysine Acetylation ◽  
Specific Regulation

Abstract Protein lysine acetylation regulation is an important molecular mechanism for regulating cellular processes and plays critical physiological and pathological roles in cancers and diseases. Although massive acetylation sites have been identified through experimental identification and high-throughput proteomics techniques, their enzyme-specific regulation remains largely unknown. Here, we developed the deep learning-based protein lysine acetylation modification prediction (Deep-PLA) software for histone acetyltransferase (HAT)/histone deacetylase (HDAC)-specific acetylation prediction based on deep learning. Experimentally identified substrates and sites of several HATs and HDACs were curated from the literature to generate enzyme-specific data sets. We integrated various protein sequence features with deep neural network and optimized the hyperparameters with particle swarm optimization, which achieved satisfactory performance. Through comparisons based on cross-validations and testing data sets, the model outperformed previous studies. Meanwhile, we found that protein–protein interactions could enrich enzyme-specific acetylation regulatory relations and visualized this information in the Deep-PLA web server. Furthermore, a cross-cancer analysis of acetylation-associated mutations revealed that acetylation regulation was intensively disrupted by mutations in cancers and heavily implicated in the regulation of cancer signaling. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of protein acetylation in various biological processes to promote the research on prognosis and treatment of cancers. Therefore, the Deep-PLA predictor and protein acetylation interaction networks could provide helpful information for studying the regulation of protein acetylation. The web server of Deep-PLA could be accessed at http://deeppla.cancerbio.info.

scholarly journals PRISM: a web server and repository for prediction of protein–protein interactions and modeling their 3D complexes

2014 ◽  
Vol 42 (W1) ◽  
pp. W285-W289 ◽  
Author(s):  
Alper Baspinar ◽  
Engin Cukuroglu ◽  
Ruth Nussinov ◽  
Ozlem Keskin ◽  
Attila Gursoy
Keyword(s):  
Protein Interactions ◽  
Web Server ◽  
Protein Protein Interactions

Plasmod-PPI: A web-server predicting complex biopolymer targets in plasmodium with entropy measures of protein–protein interactions

Polymer ◽  
2010 ◽  
Vol 51 (1) ◽  
pp. 264-273 ◽  
Author(s):  
Yamilet Rodriguez-Soca ◽  
Cristian R. Munteanu ◽  
Julian Dorado ◽  
Juan Rabuñal ◽  
Alejandro Pazos ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Web Server ◽  
Protein Protein Interactions ◽  
Entropy Measures

scholarly journals PPI-MASS: An Interactive Web Server to Identify Protein-Protein Interactions From Mass Spectrometry-Based Proteomics Data

2021 ◽  
Vol 8 ◽  
Author(s):  
Mariela González-Avendaño ◽  
Simón Zúñiga-Almonacid ◽  
Ian Silva ◽  
Boris Lavanderos ◽  
Felipe Robinson ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Web Server ◽  
Target Protein ◽  
Protein Protein Interactions ◽  
Proteomics Data ◽  
Functional Protein ◽  
Web Platform ◽  
The Web

Mass spectrometry-based proteomics methods are widely used to identify and quantify protein complexes involved in diverse biological processes. Specifically, tandem mass spectrometry methods represent an accurate and sensitive strategy for identifying protein-protein interactions. However, most of these approaches provide only lists of peptide fragments associated with a target protein, without performing further analyses to discriminate physical or functional protein-protein interactions. Here, we present the PPI-MASS web server, which provides an interactive analytics platform to identify protein-protein interactions with pharmacological potential by filtering a large protein set according to different biological features. Starting from a list of proteins detected by MS-based methods, PPI-MASS integrates an automatized pipeline to obtain information of each protein from freely accessible databases. The collected data include protein sequence, functional and structural properties, associated pathologies and drugs, as well as location and expression in human tissues. Based on this information, users can manipulate different filters in the web platform to identify candidate proteins to establish physical contacts with a target protein. Thus, our server offers a simple but powerful tool to detect novel protein-protein interactions, avoiding tedious and time-consuming data postprocessing. To test the web server, we employed the interactome of the TRPM4 and TMPRSS11a proteins as a use case. From these data, protein-protein interactions were identified, which have been validated through biochemical and bioinformatic studies. Accordingly, our web platform provides a comprehensive and complementary tool for identifying protein-protein complexes assisting the future design of associated therapies.

scholarly journals CCharPPI web server: computational characterization of protein-protein interactions from structure

2014 ◽  
Vol 31 (1) ◽  
pp. 123-125 ◽  
Author(s):  
I. H. Moal ◽  
B. Jimenez-Garcia ◽  
J. Fernandez-Recio
Keyword(s):  
Protein Interactions ◽  
Web Server ◽  
Protein Protein Interactions

scholarly journals PPISearch: a web server for searching homologous protein–protein interactions across multiple species

2009 ◽  
Vol 37 (suppl_2) ◽  
pp. W369-W375 ◽  
Author(s):  
Chun-Chen Chen ◽  
Chun-Yu Lin ◽  
Yu-Shu Lo ◽  
Jinn-Moon Yang
Keyword(s):  
Protein Interactions ◽  
Web Server ◽  
Protein Protein Interactions ◽  
Homologous Protein ◽  
Multiple Species

Trypano-PPI: A Web Server for Prediction of Unique Targets in Trypanosome Proteome by using Electrostatic Parameters of Protein−protein Interactions

2010 ◽  
Vol 9 (2) ◽  
pp. 1182-1190 ◽  
Author(s):  
Yamilet Rodriguez-Soca ◽  
Cristian R. Munteanu ◽  
Julián Dorado ◽  
Alejandro Pazos ◽  
Francisco J. Prado-Prado ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Web Server ◽  
Protein Protein Interactions

scholarly journals Structure-based validation can drastically under-estimate error rate in proteome-wide cross-linking mass spectrometry studies

2019 ◽  
Author(s):  
Kumar Yugandhar ◽  
Ting-Yi Wang ◽  
Shayne D. Wierbowski ◽  
Elnur Elyar Shayhidin ◽  
Haiyuan Yu
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Error Rates ◽  
Cross Linking ◽  
Protein Protein Interactions ◽  
3D Structures ◽  
Cross Links ◽  
Assess Quality ◽  
Almost All

AbstractRecent, rapid advances in cross-linking mass spectrometry (XL-MS) has enabled detection of novel protein-protein interactions and their structural dynamics at the proteome scale. Given the importance and scale of the novel interactions identified in these proteome-wide XL-MS studies, thorough quality assessment is critical. Almost all current XL-MS studies validate cross-links against known 3D structures of representative protein complexes. However, current structure validation approach only includes cross-links where both peptides mapped to the 3D structures. Here we provide theoretical and experimental evidence demonstrating this approach can drastically underestimate error rates for proteome-wide XL-MS datasets. Addressing current shortcomings, we propose and demonstrate a comprehensive set of four metrics, including orthogonal experimental validation to thoroughly assess quality of proteome-wide XL-MS datasets.

Sign in / Sign up

Export Citation Format

Share Document