A Brief History of Protein Sorting Prediction

Nielsen, Henrik; Tsirigos, Konstantinos D.; Brunak, Søren; Heijne, Gunnar von

doi:10.1007/s10930-019-09838-3

Cited by 201 publications

(147 citation statements)

References 178 publications

(218 reference statements)

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“…In computer science, machine learning (ML) methods were developed for many purpose including pattern classification . Due to their various advantages, ML methods have also been applied in computational biology . Many ML methods are specialized in classification with high accuracy, and can also provide insights into the intrinsic differences in classification model.…”

Section: Introductionmentioning

confidence: 99%

Recognition of protein allosteric states and residues: Machine learning approaches

2018

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Allostery is a process by which proteins transmit the effect of perturbation at one site to a distal functional site upon certain perturbation. As an intrinsically global effect of protein dynamics, it is difficult to associate protein allostery with individual residues, hindering effective selection of key residues for mutagenesis studies. The machine learning models including decision tree (DT) and artificial neural network (ANN) models were applied to develop classification model for a cell signaling allosteric protein with two states showing extremely similar tertiary structures in both crystallographic structures and molecular dynamics simulations. Both DT and ANN models were developed with 75% and 80% of predicting accuracy, respectively. Good agreement between machine learning models and previous experimental as well as computational studies of the same protein validates this approach as an alternative way to analyze protein dynamics simulations and allostery. In addition, the difference of distributions of key features in two allosteric states also underlies the population shift hypothesis of dynamics-driven allostery model. © 2018 Wiley Periodicals, Inc.

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Section: Introductionmentioning

confidence: 99%

Recognition of protein allosteric states and residues: Machine learning approaches

2018

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“…Many proteins present a signal peptide that directs them outside of the cell or to certain intracellular compartments after protein synthesis. For this reason, the cell synthesizes preforms, which are then matured through proteolytic processes [ 25 ]. In addition, for some enzymes, it has been reported that the presence of an additional N -terminal peptide absent in mature form has the ability to modulate the enzyme function by inhibiting its activity until the proteolytic removal of the peptide [ 26 , 27 ].…”

Section: Resultsmentioning

confidence: 99%

Gene Organization, Expression, and Localization of Ribotoxin-Like Protein Ageritin in Fruiting Body and Mycelium of Agrocybe aegerita

Baglivo

Ragucci

D’Incecco

et al. 2020

IJMS

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The edible mushroom Agrocybe aegerita produces a ribotoxin-like protein known as Ageritin. In this work, the gene encoding Ageritin was characterized by sequence analysis. It contains several typical features of fungal genes such as three short introns (60, 55 and 69 bp) located at the 5′ region of the coding sequence and typical splice junctions. This sequence codes for a precursor of 156 amino acids (~17-kDa) containing an additional N-terminal peptide of 21 amino acid residues, absent in the purified toxin (135 amino acid residues; ~15-kDa). The presence of 17-kDa and 15-kDa forms was investigated by Western blot in specific parts of fruiting body and in mycelia of A. aegerita. Data show that the 15-kDa Ageritin is the only form retrieved in the fruiting body and the principal form in mycelium. The immunolocalization by confocal laser scanning microscopy and transmission electron microscopy proves that Ageritin has vacuolar localization in hyphae. Coupling these data with a bioinformatics approach, we suggest that the N-terminal peptide of Ageritin (not found in the purified toxin) is a new signal peptide in fungi involved in intracellular routing from endoplasmic reticulum to vacuole, necessary for self-defense of A. aegerita ribosomes from Ageritin toxicity.

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“…A plant protein secreted via the canonical secretory pathway presents a N-terminal signal peptide typically 15-30 AAs long, which enables translocation of the protein across the ER in plants and which is cleaved upon secretion (Emanuelsson et al, 2007). The presence of a transit or signal peptide (SP) in secreted proteins can be predicted in silico using online algorithms such as signalP or targetP (http://www.cbs.dtu.dk/services) (Nielsen et al, 2019) and the localization of secreted proteins to the apoplast can be in silico validated using the online tool apoplastP (http://apoplastp. csiro.au) (Sperschneider et al, 2018b).…”

Section: Secretion Of Proteins and Peptides By Plants And Fungimentioning

confidence: 99%

The Multiple Facets of Plant–Fungal Interactions Revealed Through Plant and Fungal Secretomics

2020

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The plant secretome is usually considered in the frame of proteomics, aiming at characterizing extracellular proteins, their biological roles and the mechanisms accounting for their secretion in the extracellular space. In this review, we aim to highlight recent results pertaining to secretion through the conventional and unconventional protein secretion pathways notably those involving plant exosomes or extracellular vesicles. Furthermore, plants are well known to actively secrete a large array of different molecules from polymers (e.g. extracellular RNA and DNA) to small compounds (e.g. ATP, phytochemicals, secondary metabolites, phytohormones). All of these play pivotal roles in plant-fungi (or oomycetes) interactions, both for beneficial (mycorrhizal fungi) and deleterious outcomes (pathogens) for the plant. For instance, recent work reveals that such secretion of small molecules by roots is of paramount importance to sculpt the rhizospheric microbiota. Our aim in this review is to extend the definition of the plant and fungal secretomes to a broader sense to better understand the functioning of the plant/microorganisms holobiont. Fundamental perspectives will be brought to light along with the novel tools that should support establishing an environment-friendly and sustainable agriculture.

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A Brief History of Protein Sorting Prediction

Cited by 201 publications

References 178 publications

Recognition of protein allosteric states and residues: Machine learning approaches

Recognition of protein allosteric states and residues: Machine learning approaches

Gene Organization, Expression, and Localization of Ribotoxin-Like Protein Ageritin in Fruiting Body and Mycelium of Agrocybe aegerita

The Multiple Facets of Plant–Fungal Interactions Revealed Through Plant and Fungal Secretomics

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