ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning

Jana Sperschneider; Peter N. Dodds; Karam B. Singh; Jennifer M. Taylor

doi:10.1111/nph.14946

Prediction of LEA Plant Proteins Based on Machine Learning

Journal of Physics Conference Series ◽

10.1088/1742-6596/1631/1/012020 ◽

2020 ◽

Vol 1631 ◽

pp. 012020

Author(s):

Siquan Hu ◽

Meidi Zhao ◽

Zhiguo Shi ◽

Min Zhang

Keyword(s):

Machine Learning ◽

Plant Proteins

Download Full-text

Detecting Novel Sequence Signals in Targeting Peptides Using Deep Learning

10.1101/639203 ◽

2019 ◽

Cited By ~ 3

Author(s):

J.J. Almagro Armenteros ◽

M. Salvatore ◽

O. Emanuelsson ◽

O. Winther ◽

G. von Heijne ◽

...

Keyword(s):

Machine Learning ◽

Secretory Pathway ◽

Plant Proteins ◽

Learning Approaches ◽

Machine Learning Methods ◽

Sorting Signals ◽

Transit Peptides ◽

Targeting Peptide ◽

The One ◽

State Of Art

AbstractIn bioinformatics, machine learning methods have been used to predict features embedded in the sequences. In contrast to what is generally assumed, machine learning approaches can also provide new insights into the underlying biology. Here, we demonstrate this by presenting TargetP 2.0, a novel state of art method to identify N-terminal sorting signals, which direct proteins to the secretory pathway, mitochondria and chloroplasts or other plastids.By examining the strongest signals from the attention layer in the network, we find that the second residue in the protein, i.e. the one following the initial methionine, has a strong influence on the classification. When subsequently examining all targeting peptides, we observe that two-thirds of chloroplast and thylakoid transit peptides have an alanine in position two, but only 20% of other plant proteins. Further highlighting the importance of the second residue, we also note that in fungi and single-celled eukaryotes, less than 30% of the targeting peptides have an amino acid that allows the removal of the N-terminal methionine compared with 60% for the proteins without targeting peptide.TargetP 2.0 is available at http://www.cbs.dtu.dk/services/TargetP-2.0/index.php

Download Full-text

ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning

10.1101/182428 ◽

2017 ◽

Cited By ~ 6

Author(s):

Jana Sperschneider ◽

Peter N. Dodds ◽

Karam B. Singh ◽

Jennifer M. Taylor

Keyword(s):

Machine Learning ◽

Plant Pathogens ◽

False Positive Rate ◽

Effector Proteins ◽

Computational Method ◽

Host Cells ◽

Plant Proteins ◽

Functional Studies ◽

Positive Rate ◽

Improving Accuracy

AbstractThe plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate between these localizations. We present ApoplastP, the first method for predicting if an effector or plant protein localizes to the apoplast. ApoplastP uncovers features for apoplastic localization common to both effectors and plant proteins, namely an enrichment in small amino acids and cysteines as well as depletion in glutamic acid. ApoplastP predicts apoplastic localization in effectors with sensitivity of 75% and false positive rate of 5%, improving accuracy of cysteine-rich classifiers by over 13%. ApoplastP does not depend on the presence of a signal peptide and correctly predicts the localization of unconventionally secreted plant and effector proteins. The secretomes of fungal saprophytes, necrotrophic pathogens and extracellular pathogens are enriched for predicted apoplastic proteins. Rust pathogen secretomes have the lowest percentage of apoplastic proteins, but these are highly enriched for predicted effectors. ApoplastP pioneers apoplastic localization prediction using machine learning. It will facilitate functional studies and will be valuable for predicting if an effector localizes to the apoplast or if it enters plant cells. ApoplastP is available at http://apoplastp.csiro.au.

Download Full-text

Detecting sequence signals in targeting peptides using deep learning

Life Science Alliance ◽

10.26508/lsa.201900429 ◽

2019 ◽

Vol 2 (5) ◽

pp. e201900429 ◽

Cited By ~ 58

Author(s):

Jose Juan Almagro Armenteros ◽

Marco Salvatore ◽

Olof Emanuelsson ◽

Ole Winther ◽

Gunnar von Heijne ◽

...

Keyword(s):

Machine Learning ◽

Secretory Pathway ◽

State Of The Art ◽

Plant Proteins ◽

Learning Approaches ◽

Machine Learning Methods ◽

Sorting Signals ◽

Transit Peptides ◽

Targeting Peptide ◽

The One

In bioinformatics, machine learning methods have been used to predict features embedded in the sequences. In contrast to what is generally assumed, machine learning approaches can also provide new insights into the underlying biology. Here, we demonstrate this by presenting TargetP 2.0, a novel state-of-the-art method to identify N-terminal sorting signals, which direct proteins to the secretory pathway, mitochondria, and chloroplasts or other plastids. By examining the strongest signals from the attention layer in the network, we find that the second residue in the protein, that is, the one following the initial methionine, has a strong influence on the classification. We observe that two-thirds of chloroplast and thylakoid transit peptides have an alanine in position 2, compared with 20% in other plant proteins. We also note that in fungi and single-celled eukaryotes, less than 30% of the targeting peptides have an amino acid that allows the removal of the N-terminal methionine compared with 60% for the proteins without targeting peptide. The importance of this feature for predictions has not been highlighted before.

Download Full-text

Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Download Full-text