Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences

Imai, Keisuke; Nakai, Kenta

doi:10.3389/fgene.2020.607812

Cited by 25 publications

(20 citation statements)

References 133 publications

(160 reference statements)

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“…However, prediction of subcellular localization is di cult for schistosomal proteins due to the low homology of the proteins outside the genus, which complicates functional annotation 46 . To date, many prediction methods of the subcellular localization of eukaryotic proteins are mainly based on the targeting of signal, sequence-based, or annotation-based features 47 . Identi ed tegument proteins from different proteomic analyses have shown high diversity of predicted subcellular locations [15][16][17][18] .…”

Section: Discussionmentioning

confidence: 99%

Comparative proteome analysis of the tegument of male and female adult Schistosoma mansoni

Winkelmann

Salazar

Hentschker

et al. 2022

Preprint

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The tegument, as the surface layer of adult male and female Schistosoma spp. represents the protective barrier of the worms to the hostile environment of the host bloodstream. Here we present the first comparative analysis of sex-specific tegument proteins of paired or virgin Schistosoma mansoni. We applied a new and highly sensitive workflow, allowing detection of even low abundance proteins. Therefore, a streptavidin-biotin affinity purification technique in combination with single pot solid-phase enhanced sample preparation was established for subsequent LC-MS/MS analysis. We were able to identify 1519 tegument proteins for male and female virgin and paired worms and categorized them by sex. Bioinformatic analysis revealed an involvement of female-specific tegument proteins in signaling pathways of cellular processes and antioxidant mechanisms. Male-specific proteins were found to be enriched in processes linked to phosphorylation and signal transduction. This suggests a task sharing between the sexes that might be necessary for survival in the host. Our datasets provide a basis for further studies to understand and ultimately decipher the strategies of the two worm sexes to evade the immune system.

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Section: Discussionmentioning

confidence: 99%

Comparative proteome analysis of the tegument of male and female adult Schistosoma mansoni

Winkelmann

Salazar

Hentschker

et al. 2022

Preprint

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“… Signal peptide-based features: In addition to the sequence features mentioned above, functional or signal peptide regions were used in this prediction. The signal peptide was associated with the transfer to or function of a protein in its localization site [ 57 ]. Nuclear localization signals (NLSs) were used as important features for detecting nuclear proteins.…”

Section: Methodsmentioning

confidence: 99%

Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

Wattanapornprom

Thammarongtham

Hongsthong

et al. 2021

Life

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The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and reliability. The prediction of plant protein subcellular localization is challenging because the underlying problem is not only a multiclass, but also a multilabel problem. Generally, plant proteins can be found in 10–14 locations/compartments. The number of proteins in some compartments (nucleus, cytoplasm, and mitochondria) is generally much greater than that in other compartments (vacuole, peroxisome, Golgi, and cell wall). Therefore, the problem of imbalanced data usually arises. Therefore, we propose an ensemble machine learning method based on average voting among heterogeneous classifiers. We first extracted various types of features suitable for each type of protein localization to form a total of 479 feature spaces. Then, feature selection methods were used to reduce the dimensions of the features into smaller informative feature subsets. This reduced feature subset was then used to train/build three different individual models. In the process of combining the three distinct classifier models, we used an average voting approach to combine the results of these three different classifiers that we constructed to return the final probability prediction. The method could predict subcellular localizations in both single- and multilabel locations, based on the voting probability. Experimental results indicated that the proposed ensemble method could achieve correct classification with an overall accuracy of 84.58% for 11 compartments, on the basis of the testing dataset.

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“…In addition, several tools are utilized for the recognition of sorting signals as well as the prediction of subcellular localization of proteins from their amino acid sequences (Imai & Nakai, 2020). For predicting signal peptides and their cleavage sites, many prediction methods, such as SPEPlip (Fariselli et al, 2003), SignalP 4.0 (Petersen et al, 2011), Phobius (Krogh et al, 2007), and DeepSig (Savojardo et al, 2018), have been developed.…”

Section: Introductionmentioning

confidence: 99%

Determination of Putative Vacuolar Proteases, PEP4 and PRB1 in a Novel Yeast Expression Host Meyerozyma guilliermondii Strain SO Using Bioinformatics Tools

Lorrine¹,

Rahman²,

Tan³

et al. 2022

JST

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Meyerozyma guilliermondii strain SO, a newly isolated yeast species from spoilt orange, has been used as a host to express the recombinant proteins using methylotrophic yeast promoters. However, as a novel yeast expression system, the vacuolar proteases of this yeast have not been determined, which may have contributed to the low level of heterologous protein secretions. Thus, this study aimed to determine intra- and extracellular proteolytic activity and identify the putative vacuolar proteases using bioinformatics techniques. A clear zone was observed from the nutrient agar skimmed milk screening plate. Proteolytic activity of 117.30 U/ml and 75 U/ml were obtained after 72 h of cultivation for both extracellular and intracellular proteins, respectively. Next, the Hidden Markov model (HMM) was used to detect the presence of the vacuolar proteases (PEP4 and PRB1) from the strain SO proteome. Aspartyl protease (PEP4) with 97.55% identity to Meyerozyma sp. JA9 and a serine protease (PRB1) with 70.91% identity to Candida albicans were revealed. The homology with other yeast vacuolar proteases was confirmed via evolutionary analysis. PROSPER tool prediction of cleavage sites postulated that PEP4 and PRB1 might have caused proteolysis of heterologous proteins in strain SO. In conclusion, two putative vacuolar proteases (PEP4 and PRB1) were successfully identified in strain SO. Further characterization can be done to understand their specific properties, and their effects on heterologous protein expression can be conducted via genome editing.

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Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences

Cited by 25 publications

References 133 publications

Comparative proteome analysis of the tegument of male and female adult Schistosoma mansoni

Comparative proteome analysis of the tegument of male and female adult Schistosoma mansoni

Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

Determination of Putative Vacuolar Proteases, PEP4 and PRB1 in a Novel Yeast Expression Host Meyerozyma guilliermondii Strain SO Using Bioinformatics Tools

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