Subcellular Localization of Intracellular Human Proteins by Construction of Tagged Fusion Proteins and Transient Expression in COS-7 Cells

2008 ◽  
pp. 353-367
Author(s):  
John E. Collins
Keyword(s):  
Subcellular Localization ◽  
Transient Expression ◽  
Fusion Proteins ◽  
Human Proteins

scholarly journals Differential Subcellular Localization Regulates Oncogenic Signaling by ROS1 Kinase Fusion Proteins

2018 ◽  
Vol 79 (3) ◽  
pp. 546-556 ◽  
Author(s):  
Dana S. Neel ◽  
David V. Allegakoen ◽  
Victor Olivas ◽  
Manasi K. Mayekar ◽  
Golzar Hemmati ◽  
...  
Keyword(s):  
Subcellular Localization ◽  
Fusion Proteins ◽  
Oncogenic Signaling

scholarly journals The Ploc_Bal-Mhum Is a Powerful Web-Serve for Predicting the Subcellular Localization of Human Proteins Purely Based on Their Sequence Information

2020 ◽  
Vol 1 (2) ◽  
pp. 01-05
Author(s):  
Kuo Chou
Keyword(s):  
Artificial Intelligence ◽  
Subcellular Localization ◽  
Web Server ◽  
Sequence Information ◽  
Human Proteins

In 2019 a very powerful web-server, or AI (Artificial Intelligence) tool, has been developed for predicting the subcellular localization of human proteins purely according to their information for the multi-label systems, in which a same protein may appear or travel between two or more locations and hence its identification needs the multi-label mark.

A simple and effective method for protein subcellular localization using Agrobacterium-mediated transformation of onion epidermal cells

Biologia ◽  
2007 ◽  
Vol 62 (5) ◽  
Author(s):  
Wei Sun ◽  
Ziyi Cao ◽  
Yan Li ◽  
Yanxiu Zhao ◽  
Hui Zhang
Keyword(s):  
Subcellular Localization ◽  
Particle Bombardment ◽  
Fusion Proteins ◽  
Fluorescent Proteins ◽  
Transformed Cells ◽  
Protein Subcellular Localization ◽  
Transformation Protocol ◽  
Agrobacterium Mediated Transformation ◽  
The Mean ◽  
Onion Epidermal Cells

AbstractA modified Agrobacterium-mediated transformation protocol has been successfully used for transient expression of the intrinsically fluorescent proteins and their fusion proteins in onion epidermis. The mean of the transformed cells rate per peel is about 10.5±0.9%, while that of the particle bombardment method is at the range 2.0±0.4%. To compare with the prevailing method of micro-projectile bombardment, the modified Agrobacterium-mediated transformation may provide with higher efficiency and even more simplified manipulability on a lower budget.

scholarly journals The C-terminus of cytochrome b5 confers endoplasmic reticulum specificity by preventing spontaneous insertion into membranes

2007 ◽  
Vol 401 (3) ◽  
pp. 701-709 ◽  
Author(s):  
Matthew P. A. Henderson ◽  
Yeen Ting Hwang ◽  
John M. Dyer ◽  
Robert T. Mullen ◽  
David W. Andrews
Keyword(s):  
Endoplasmic Reticulum ◽  
Subcellular Localization ◽  
Lipid Bilayers ◽  
Fusion Proteins ◽  
Molecular Mechanisms ◽  
Cytochrome B5 ◽  
Terminal Sequence ◽  
C Terminus

The molecular mechanisms that determine the correct subcellular localization of proteins targeted to membranes by tail-anchor sequences are poorly defined. Previously, we showed that two isoforms of the tung oil tree [Vernicia (Aleurites) fordii] tail-anchored Cb5 (cytochrome b5) target specifically to ER (endoplasmic reticulum) membranes both in vivo and in vitro [Hwang, Pelitire, Henderson, Andrews, Dyer and Mullen (2004) Plant Cell 16, 3002–3019]. In the present study, we examine the targeting of various tung Cb5 fusion proteins and truncation mutants to purified intracellular membranes in vitro in order to assess the importance of the charged CTS (C-terminal sequence) in targeting to specific membranes. Removal of the CTS from tung Cb5 proteins resulted in efficient binding to both ER and mitochondria. Results from organelle competition, liposome-binding and membrane proteolysis experiments demonstrated that removal of the CTS results in spontaneous insertion of tung Cb5 proteins into lipid bilayers. Our results indicate that the CTSs from plant Cb5 proteins provide ER specificity by preventing spontaneous insertion into incorrect subcellular membranes.

scholarly journals The pLoc bal-mHum is a powerful web-serve for predicting the subcellular localization of human proteins purely based on their sequence information

2020 ◽  
pp. 1-4
Author(s):  
Kuo-Chen Chou ◽  
Keyword(s):  
Artificial Intelligence ◽  
Subcellular Localization ◽  
Web Server ◽  
Sequence Information ◽  
Human Proteins

In 2019 a very powerful web-server, or AI (Artificial Intelligence) tool, has been developed for predicting the subcellular localization of human proteins purely according to their information for the multi-label systems [1], in which a same protein may appear or travel between two or more locations and hence its identification needs the multi-label mark [2].

scholarly journals HumDLoc: Human Protein Subcellular Localization Prediction Using Deep Neural Network

2020 ◽  
Vol 21 (7) ◽  
pp. 546-557
Author(s):  
Rahul Semwal ◽  
Pritish Kumar Varadwaj
Keyword(s):  
Machine Learning ◽  
Subcellular Localization ◽  
Predictive Model ◽  
Sequence Data ◽  
Predictive Performance ◽  
Protein Subcellular Localization ◽  
Subcellular Localization Prediction ◽  
Human Proteins ◽  
Protein Subcellular Localization Prediction ◽  
Localization Prediction

Aims: To develop a tool that can annotate subcellular localization of human proteins. Background: With the progression of high throughput human proteomics projects, an enormous amount of protein sequence data has been discovered in the recent past. All these raw sequence data require precise mapping and annotation for their respective biological role and functional attributes. The functional characteristics of protein molecules are highly dependent on the subcellular localization/ compartment. Therefore, a fully automated and reliable protein subcellular localization prediction system would be very useful for current proteomic research. Objective: To develop a machine learning-based predictive model that can annotate the subcellular localization of human proteins with high accuracy and precision. Methods: In this study, we used the PSI-CD-HIT homology criterion and utilized the sequence-based features of protein sequences to develop a powerful subcellular localization predictive model. The dataset used to train the HumDLoc model was extracted from a reliable data source, Uniprot knowledge base, which helps the model to generalize on the unseen dataset. Result : The proposed model, HumDLoc, was compared with two of the most widely used techniques: CELLO and DeepLoc, and other machine learning-based tools. The result demonstrated promising predictive performance of HumDLoc model based on various machine learning parameters such as accuracy (≥97.00%), precision (≥0.86), recall (≥0.89), MCC score (≥0.86), ROC curve (0.98 square unit), and precision-recall curve (0.93 square unit). Conclusion: In conclusion, HumDLoc was able to outperform several alternative tools for correctly predicting subcellular localization of human proteins. The HumDLoc has been hosted as a web-based tool at https://bioserver.iiita.ac.in/HumDLoc/.

Subcellular Localization of Signal Peptide Fusion Proteins Expressed in E. coli

2021 ◽  
Vol 2021 (2) ◽  
pp. pdb.prot102145
Author(s):  
Clara L. Kielkopf ◽  
William Bauer ◽  
Ina L. Urbatsch
Keyword(s):  
Subcellular Localization ◽  
Signal Peptide ◽  
Fusion Proteins ◽  
E Coli
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