Abstract
At the time of translation, nascent proteins are thought to be sorted into their final subcellular localization sites, based on the part of their amino acid sequences (i.e., sorting or targeting signals). Thus, it is interesting to computationally recognize these signals from the amino acid sequences of any given proteins and to predict their final subcellular localization with such information, supplemented with additional information (e.g., k-mer frequency). This field has a long history and many prediction tools have been released. Even in this era of proteomic atlas at the single-cell level, researchers continue to develop new algorithms, aiming at accessing the impact of disease-causing mutations/cell type-specific alternative splicing, for example. In this article, we overview the entire field and discuss its future direction.
Highlights
We should not underestimate the importance of non-coding genes, the main players of the genetic system of living organisms are still regarded as protein-coding genes, which specify amino acid sequence information
The readers are recommended to refer to additional reviews by other authors and ourselves, too (Imai and Nakai, 2010, 2019; Du and Xu, 2013; Nielsen, 2017; Nielsen et al, 2019)
60% of mitochondrial proteins possess an N-terminal cleavable targeting signal. These presequences are typically recognized by the translocase of the outer membrane (TOM) receptors, which consist of Tom20 and Tom22, in the TOM complex
Summary
Received: 18 September 2020 Accepted: 03 November 2020 Published: 25 November 2020. Citation: Imai K and Nakai K (2020) Tools for the Recognition of Sorting Signals and the Prediction of Subcellular. Localization of Proteins From Their Amino Acid Sequences. At the time of translation, nascent proteins are thought to be sorted into their final subcellular localization sites, based on the part of their amino acid sequences (i.e., sorting or targeting signals). It is interesting to computationally recognize these signals from the amino acid sequences of any given proteins and to predict their final subcellular localization with such information, supplemented with additional information (e.g., k-mer frequency). This field has a long history and many prediction tools have been released.
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