Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms

Abstract

When predicting the subcellular localization of proteins from their amino acid sequences, there are basically three approaches: signal-based, global property-based, and homology-based. Each of these has its advantages and drawbacks, and it is important when comparing methods to know which approach was used. Various statistical and machine learning algorithms are used with all three approaches, and various measures and standards are employed when reporting the performances of the developed methods. This chapter presents a number of available methods for prediction of sorting signals and subcellular localization, but rather than providing a checklist of which predictors to use, it aims to function as a guide for critical assessment of prediction methods.

MATH-Domain Family Shows Response toward Abiotic Stress in Arabidopsis and Rice

“…Further, the protein sequences identified were analyzed for their characteristics such as pI, molecular weight using TAIR (release 10.0; Berardini et al, 2015 ) and TIGR (release 7.0; Kawahara et al, 2013 ) for Arabidopsis and rice respectively. The subcellular localization of the MDC proteins of Arabidopsis was predicted based on SUBA database ( http://suba3.plantenergy.uwa.edu.au/ ) while that of rice was predicted using subCELlular LOcalization predictor (CELLO v. 2.5: http://cello.life.nctu.edu.tw/ ) (Yu et al, 2006 ) and re-confirmed using WoLF PSORT, an advanced protein subcellular localization prediction tool ( http://www.genscript.com/wolf-psort.html ) (Horton et al, 2007 ; Nielsen, 2016 ).…”

The SubCons webserver: A user friendly web interface for state‐of‐the‐art subcellular localization prediction

“…It is not trivial to determine how similar a pair of proteins has to be in order to infer the possible subcellular localization. Using sequence alignment programs such as BLAST, it is possible to transfer the subcellular localization annotation from the best hit to the query, or in another word to infer subcellular localization from the annotation of homologs which do not necessarily have experimentally known subcellular localization …”

“…Sequence‐based methods use co‐ and post‐translational targeting signals, linear motifs detections, amino acid distributions, gapped‐paired, surface or pseudo amino acid compositions to predict the localization from the sequence directly. In contrast, annotation‐based methods use annotations from databases including localization of homologous protein(s), annotated gene ontology terms, functional domains, text information from PubMed abstracts and protein‐protein interactions . The most successful methods use a combination of both the approaches.…”

Predicting eukaryotic protein secretion without signals

“…In Gram-negative bacteria, the type I, III, IV, and VI secretion pathways function without signal peptides, and in some cases, there is evidence of Nterminal or C-terminal sorting signals [8,13]. In Gram-positive bacteria, there are also a few known pathways (Wss, holin, and SecA2) [13,14]. This paper will discuss prediction of non-classical secretion in eukaryotes only; prediction in bacteria has been described elsewhere [8,14].…”

“…In Gram-positive bacteria, there are also a few known pathways (Wss, holin, and SecA2) [13,14]. This paper will discuss prediction of non-classical secretion in eukaryotes only; prediction in bacteria has been described elsewhere [8,14].…”