scholarly journals BaCelLo: a Balanced subCellular Localization predictor.

2007 ◽  
Author(s):  
Andrea Pierleoni ◽  
Pier Luigi Martelli ◽  
Piero Fariselli ◽  
Rita Casadio
Keyword(s):  
Subcellular Localization ◽  
Localization Predictor

DETECTING AND SORTING TARGETING PEPTIDES WITH NEURAL NETWORKS AND SUPPORT VECTOR MACHINES

2006 ◽  
Vol 04 (01) ◽  
pp. 1-18 ◽  
Author(s):  
JOHN HAWKINS ◽  
MIKAEL BODÉN
Keyword(s):  
Support Vector Machines ◽  
Subcellular Localization ◽  
Support Vector ◽  
Plant Proteins ◽  
Final Model ◽  
Classifier Design ◽  
Vector Machines ◽  
Localization Predictor ◽  
Plant Data ◽  
Targeting Peptide

This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).

scholarly journals BaCelLo: a balanced subcellular localization predictor

2006 ◽  
Vol 22 (14) ◽  
pp. e408-e416 ◽  
Author(s):  
A. Pierleoni ◽  
P. L. Martelli ◽  
P. Fariselli ◽  
R. Casadio
Keyword(s):  
Subcellular Localization ◽  
Localization Predictor

scholarly journals The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier

2018 ◽  
Vol 34 (13) ◽  
pp. 2185-2194 ◽  
Author(s):  
Zhen Cao ◽  
Xiaoyong Pan ◽  
Yang Yang ◽  
Yan Huang ◽  
Hong-Bin Shen
Keyword(s):  
Subcellular Localization ◽  
Ensemble Classifier ◽  
Localization Predictor ◽  
Non Coding Rnas

scholarly journals EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features

2018 ◽  
Vol 443 ◽  
pp. 138-146 ◽  
Author(s):  
Md. Raihan Uddin ◽  
Alok Sharma ◽  
Dewan Md Farid ◽  
Md. Mahmudur Rahman ◽  
Abdollah Dehzangi ◽  
...  
Keyword(s):  
Subcellular Localization ◽  
Structural Features ◽  
Protein Subcellular Localization ◽  
Gram Positive ◽  
Localization Predictor

Investigating the functional link between TMEM165 and SPCA1

2019 ◽  
Vol 476 (21) ◽  
pp. 3281-3293 ◽  
Author(s):  
Elodie Lebredonchel ◽  
Marine Houdou ◽  
Hans-Heinrich Hoffmann ◽  
Kateryna Kondratska ◽  
Marie-Ange Krzewinski ◽  
...  
Keyword(s):  
Subcellular Localization ◽  
Complete Loss ◽  
Protein Family ◽  
Uncharacterized Protein ◽  
Functional Link ◽  
P Type

TMEM165 was highlighted in 2012 as the first member of the Uncharacterized Protein Family 0016 (UPF0016) related to human glycosylation diseases. Defects in TMEM165 are associated with strong Golgi glycosylation abnormalities. Our previous work has shown that TMEM165 rapidly degrades with supraphysiological manganese supplementation. In this paper, we establish a functional link between TMEM165 and SPCA1, the Golgi Ca2+/Mn2+ P-type ATPase pump. A nearly complete loss of TMEM165 was observed in SPCA1-deficient Hap1 cells. We demonstrate that TMEM165 was constitutively degraded in lysosomes in the absence of SPCA1. Complementation studies showed that TMEM165 abundance was directly dependent on SPCA1's function and more specifically its capacity to pump Mn2+ from the cytosol into the Golgi lumen. Among SPCA1 mutants that differentially impair Mn2+ and Ca2+ transport, only the Q747A mutant that favors Mn2+ pumping rescues the abundance and Golgi subcellular localization of TMEM165. Interestingly, the overexpression of SERCA2b also rescues the expression of TMEM165. Finally, this paper highlights that TMEM165 expression is linked to the function of SPCA1.

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