How to use TSignal Signal Peptide Prediction Online

Use TSignal to predict signal-peptide types and candidate cleavage sites.

TSignal assigns an unstructured sequence of residue labels to the first 70 amino acids. The first label determines whether a signal peptide is predicted and its type: Sec/SPase I (S), Sec/SPase II (L), Tat/SPase I (T), Tat/SPase II (W), or Sec/SPase IV, pilin-like (P). I, O, and M denote inside, outside, and transmembrane regions. These N-terminal annotations are not a whole-protein topology map.

How TSignal Signal Peptide Prediction Works

Submit Input Sequences with their native N termini. No organism selection is needed for the released model. Each sequence must have 30–10000 standard amino acids or X; at most 100 sequences and 100000 residues total are allowed. Only the first 70 residues are scored.

First Label Confidence is the conditional probability of the selected first residue label, not the sum of probabilities for all signal-peptide types. Label Confidence is conditional on previous greedy labels. These scores are not calibrated experimental probabilities.

For a signal-positive sequence, Cleavage After Position follows the released predictor's convention: the last occurrence of the first signal label identifies the final signal residue (one-based). Cleavage Confidence is the probability of the next residue's selected label. The decoder permits non-contiguous labels; review predictions marked Review non-contiguous labels before using their candidate cleavage site. If the signal label reaches the end of the analyzed region, cleavage is unresolved and no mature sequence is exported.

Download predictions.csv, residue_predictions.csv, and mature_sequences.fasta. Mature sequences include the full unscored tail after the candidate cleavage site, including candidates flagged for review. The FASTA can be empty when no cleavage site is resolved. Predictions support experimental prioritization and do not establish secretion efficiency or experimental cleavage.

What is Neurosnap?

Neurosnap is the leading platform for bioinformatics and computational science focused on expanding access to powerful modeling and simulation tools. Because many state-of-the-art machine learning systems remain complex to install, configure, and scale, Neurosnap offers a clean, browser-based workspace that removes the burden of infrastructure management, dependency conflicts, and command-line tooling.

Built for biologists, chemists, and cross-disciplinary scientists, the platform enables advanced computational workflows without requiring expertise in software engineering or cloud architecture. Researchers can launch analyses through an intuitive interface, connect programmatically through a comprehensive API, and rely on automated resource management to scale workloads efficiently. By taking care of the underlying compute and operational complexity, Neurosnap allows teams to devote their energy to scientific progress and faster iteration. Security and data protection remain foundational principles, with clear safeguards outlined in our Terms of Use and Privacy Policy to ensure your work stays protected.

Advancing Discovery with TSignal Signal Peptide Prediction on Neurosnap

Using TSignal Signal Peptide Prediction on Neurosnap could drastically accelerate transformer-based signal-peptide screening from protein sequences.

Compare up to 100 named protein sequences per job.
Distinguish five secretion-signal types.
Inspect residue annotations and download candidate mature sequences.

How to Use TSignal Signal Peptide Prediction on Neurosnap

To harness the capabilities of TSignal Signal Peptide Prediction, researchers can follow this streamlined workflow within Neurosnap:

Access Neurosnap: Start by logging in to the Neurosnap website.
Select Tool: From the list of available tools, choose TSignal Signal Peptide Prediction.
Provide Inputs: Provide all the inputs specified within the submission panel and optionally configure the tool as desired.
Run Tool: Submit the TSignal Signal Peptide Prediction job and Neurosnap will execute it in the cloud, automatically notifying you as soon as your results are ready.
Review Output: Explore your results through rich visualizations, including figures, plots, and interactive views designed to help you analyze findings with clarity and confidence.

Citations

Please cite the original work when using TSignal Signal Peptide Prediction in publications or research outputs.

Dumitrescu A et al. TSignal: A transformer model for signal peptide prediction. bioRxiv (2022). https://doi.org/10.1101/2022.06.02.493958.

Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/

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