Chapter title |
A Guide to Computational Methods for Predicting Mitochondrial Localization
|
---|---|
Chapter number | 1 |
Book title |
Mitochondria
|
Published in |
Methods in molecular biology, March 2017
|
DOI | 10.1007/978-1-4939-6824-4_1 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6822-0, 978-1-4939-6824-4
|
Authors |
Su Sun, Bianca H. Habermann |
Editors |
Dejana Mokranjac, Fabiana Perocchi |
Abstract |
Predicting mitochondrial localization of proteins remains challenging for two main reasons: (1) Not only one but several mitochondrial localization signals exist, which primarily dictate the final destination of a protein in this organelle. However, most localization prediction algorithms rely on the presence of a so-called presequence (or N-terminal mitochondrial targeting peptide, mTP), which occurs in only ~70% of mitochondrial proteins. (2) The presequence is highly divergent on sequence level and therefore difficult to identify on the computer.In this chapter, we review a number of protein localization prediction programs and propose a strategy to predict mitochondrial localization. Finally, we give some helpful suggestions for bench scientists when working with mitochondrial protein candidates in silico. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 20 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 5 | 25% |
Student > Ph. D. Student | 4 | 20% |
Student > Doctoral Student | 2 | 10% |
Researcher | 2 | 10% |
Student > Master | 2 | 10% |
Other | 1 | 5% |
Unknown | 4 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 4 | 20% |
Agricultural and Biological Sciences | 4 | 20% |
Immunology and Microbiology | 2 | 10% |
Nursing and Health Professions | 1 | 5% |
Neuroscience | 1 | 5% |
Other | 1 | 5% |
Unknown | 7 | 35% |