Recent progress in protein subcellular location prediction

Chou, Kuo‐Chen; Shen, Hong‐Bin

doi:10.1016/j.ab.2007.07.006

Cited by 812 publications

(496 citation statements)

References 83 publications

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“…In addition, computational techniques, which profit from the increased knowledge of known protein localisations, to predict the localisation of a new protein, are becoming increasingly faster and more accurate 483 .…”

Section: Introductionmentioning

confidence: 99%

Role of the pro-survival molecule Bfl-1 in melanoma

Hind

Carter

Harris

et al. 2015

The International Journal of Biochemistry & Cell Biology

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Melanoma, the cancer derived from the melanocytes of the skin, is becoming an ever more common cancer in the ageing population of the developed world and displays an intrinsic resistance to current therapies. This chemo resistance makes metastatic melanoma an extremely difficult cancer to treat leading to a 5 year survival rate of just 20%. As such, it is important to unearth new potential drug targets to increase the prospects for melanoma patients.Bfl-1 is a pro-survival protein of the Bcl-2 family of apoptosis regulating proteins. It has been found to be over-expressed in a small subset of chemo resistant tumours, including melanoma. Accordingly, I characterised the molecule in melanoma cells and determined its role in protecting these cells from chemotherapy-induced apoptosis. I elucidated the expression profile and half-lives of the protein and mRNA across a panel of melanoma cell-lines and healthy melanocytes. I also confirmed, using pathway specific inhibitors, that Bfl-1 was transcriptionally regulated by the NFB pathway and concluded through pulsechase experiments that Bfl-1 protein was degraded, at least in part, by the proteasome. Subcellular fractionation and indirect immunofluorescence techniques elucidated that Bfl-1 was located mostly at the mitochondria in both resting and apoptotic cells, with a diffuse level present throughout the cytoplasm. As well as characterising the protein in both melanoma cells and healthy primary melanocytes, I determined its role in the resistance of these cells to apoptosis. Over-expressed Bfl-1 was found to protect melanoma cells from apoptosis caused by a range of chemotherapeutic agents in A375 melanoma cells, which naturally expressed very low levels of Bfl-1. Further, knock down of Bfl-1 using siRNA technology in melanoma cells revealed the dependence of these cells on Bfl-1 for their resistance to certain chemotherapeutic agents currently used in melanoma treatment, including the MEK inhibitor PD901. All together, this research contributes to our understanding of Bfl-1 and highlights it as a potentially important therapeutic target in metastatic melanoma.

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Section: Introductionmentioning

confidence: 99%

Role of the pro-survival molecule Bfl-1 in melanoma

Hind

Carter

Harris

et al. 2015

The International Journal of Biochemistry & Cell Biology

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“…In statistical prediction, three cross-validation methods are often used: subsampling test, independent dataset test, and jackknife test (Chou and Zhang, 1995). However, as demonstrated in (Chou and Shen, 2007), the jackknife test has the least arbitrariness and therefore has been increasingly and widely used to test various prediction methods (see, e.g., (Chen et al, 2008;Chen and Han, 2009;Chou and Shen, 2008a;Chou and Shen, 2008b;Chou and Shen, 2009;Ding et al, 2009a;Ding et al, 2009b;Du and Li, 2008;Georgiou et al, 2009;2008;Nanni and Lumini, 2009;Rezaei et al, 2008;Shi et al, 2008;Tian et al, 2008;Wang et al, 2008;Xiao et al, 2009b;Zeng et al, 2009;). In the jackknife or leave-one-out test each case in the database is predicted for the model constructed using all the cases except the one being predicted.…”

Section: Resultsmentioning

confidence: 99%

A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer

Vilar

González-Dı́az

Santana

et al. 2009

Journal of Theoretical Biology

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The combination of the network theory and the calculation of Topological Indices (TIs) allow establishing relationships between the molecular structure of large molecules like the genes and proteins and their properties at a biological level. This type of models can be considered Quantitative Structure-Activity Relationships (QSAR) for biopolymers. In the present work a QSAR model is reported for proteins, related to Human Colorectal Cancer (HCC) and codified by different genes that have been identified experimentally by Sjöblom et al.(Science, 314 (2006)

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“…To avoid homology bias and remove the redundant sequences from the benchmark dataset, a cutoff threshold of 25% was imposed in (Chou and Shen, 2007;Chou, 2011;Chou et al, 2011;Wu et al, 2011) to exclude those proteins from the benchmark datasets that have 25% or greater sequence identity to any other in the same subset. However, in this study we did not use such a stringent criterion because even at the same site of the same protein, there could be several different single amino acid mutations with different effects.…”

Section: Datasetmentioning

confidence: 99%

SySAP: a system-level predictor of deleterious single amino acid polymorphisms

et al. 2011

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Single amino acid polymorphisms (SAPs), also known as non-synonymous single nucleotide polymorphisms (nsSNPs), are responsible for most of human genetic diseases. Discriminate the deleterious SAPs from neutral ones can help identify the disease genes and understand the mechanism of diseases. In this work, a method of deleterious SAP prediction at system level was established. Unlike most existing methods, our method not only considers the sequence and structure information, but also the network information. The integration of network information can improve the performance of deleterious SAP prediction. To make our method available to the public, we developed SySAP (a System-level predictor of deleterious Single Amino acid Polymorphisms), an easy-to-use and high accurate web server. SySAP is freely available at http://www.biosino.org/ SySAP/and http://lifecenter.sgst.cn/SySAP/.

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Recent progress in protein subcellular location prediction

Cited by 812 publications

References 83 publications

Role of the pro-survival molecule Bfl-1 in melanoma

Role of the pro-survival molecule Bfl-1 in melanoma

A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer

SySAP: a system-level predictor of deleterious single amino acid polymorphisms

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