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Protein Sorting Signals

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Exploiting multi-layered vector spaces for signal peptide detection.

International journal of data mining and bioinformatics
Analysing and classifying sequences based on similarities and differences is a mathematical problem of escalating relevance and importance in many scientific disciplines. One of the primary challenges in applying machine learning algorithms to sequen...

Identification of New Fungal Peroxisomal Matrix Proteins and Revision of the PTS1 Consensus.

Traffic (Copenhagen, Denmark)
The peroxisomal targeting signal type 1 (PTS1) is a seemingly simple peptide sequence at the C-terminal end of most peroxisomal matrix proteins. PTS1 can be described as a tripeptide with the consensus motif [S/A/C] [K/R/H] L. However, this descripti...

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

The New phytologist
The 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 betwee...

DeepSig: deep learning improves signal peptide detection in proteins.

Bioinformatics (Oxford, England)
MOTIVATION: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization.

SignalP 5.0 improves signal peptide predictions using deep neural networks.

Nature biotechnology
Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish ...

The hunt for sORFs: A multidisciplinary strategy.

Experimental cell research
Growing evidence illustrates the shortcomings on the current understanding of the full complexity of the proteome. Previously overlooked small open reading frames (sORFs) and their encoded microproteins have filled important gaps, exerting their func...

Signal Peptides Generated by Attention-Based Neural Networks.

ACS synthetic biology
Short (15-30 residue) chains of amino acids at the amino termini of expressed proteins known as signal peptides (SPs) specify secretion in living cells. We trained an attention-based neural network, the Transformer model, on data from all available o...

Signal-3L 3.0: Improving Signal Peptide Prediction through Combining Attention Deep Learning with Window-Based Scoring.

Journal of chemical information and modeling
Signal peptides play an important role in guiding and transferring transmembrane proteins and secreted proteins. In recent years, with the explosive growth of protein sequences, computationally predicting signal peptides and their cleavage sites from...

Clinical applicability of deep learning-based respiratory signal prediction models for four-dimensional radiation therapy.

PloS one
For accurate respiration gated radiation therapy, compensation for the beam latency of the beam control system is necessary. Therefore, we evaluate deep learning models for predicting patient respiration signals and investigate their clinical feasibi...

Deep Learning-Based Remaining Useful Life Estimation of Bearings with Time-Frequency Information.

Sensors (Basel, Switzerland)
In modern industrial production, the prediction ability of remaining useful life of bearings directly affects the safety and stability of the system. Traditional methods require rigorous physical modeling and perform poorly for complex systems. In th...