How to use USPNet Signal Peptide Prediction Online

Predict six signal-peptide classes and candidate cleavage sites with USPNet using uploaded multiple-sequence alignments.

How USPNet Signal Peptide Prediction Works

Upload an alignment ZIP alongside the protein sequences. Include one named .a3m, .fasta, or .fa file per input, for example protein_1.a3m for sequence protein_1. The first row must match the first 70 residues of that protein, or the entire sequence if shorter. Trim alignments to at most 70 columns. Rows must have equal lengths after removing A3M lowercase insertions, dots, and stars. Each alignment can contain up to 2048 rows; the worker retains up to 128 diverse rows including the query. Alignments are supplied by you; this service does not search sequence databases or generate alignments.

Select Organism. Unknown uses the checkpoint trained without organism information. Known groups use the organism-aware checkpoint. For Eukaryotes, non-Sec/SPase I signal classes are reassigned to No Signal Peptide, following the published inference code. Raw Prediction retains the original model call and Organism Adjustment identifies this reassignment.

Global class scores are cosine-normalized scores, not probabilities. Prediction Score is the score of the final selected class, including an organism adjustment. Position-specific Label Confidence is the softmax confidence of the selected residue label. Scores are not calibrated experimental probabilities. The released model initializes LSTM states randomly; an internal fixed seed makes initialization repeatable for the same ordered submission.

The first SPase I or SPase II cleavage label in the 70-position output determines Cleavage After Position, a one-based final signal-residue coordinate. If no cleavage label appears, it falls outside a short input, or no mature residues remain, the site is unresolved and no mature sequence is exported. Residue labels cover only observed input residues. These are N-terminal annotations, not a whole-protein topology map.

Download predictions.csv, residue_predictions.csv, and mature_sequences.fasta. Mature sequences retain the full unscored protein tail after resolved cleavage. The FASTA can be empty. Use predictions to prioritize experimental validation of secretion and 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 USPNet Signal Peptide Prediction on Neurosnap

Using USPNet Signal Peptide Prediction on Neurosnap could drastically accelerate signal-peptide screening with sequence or alignment embeddings.

Compare up to 100 named proteins.
Inspect six signal-peptide classes and residue annotations.
Download candidate mature sequences.

How to Use USPNet Signal Peptide Prediction on Neurosnap

To harness the capabilities of USPNet 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 USPNet 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 USPNet 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 USPNet Signal Peptide Prediction in publications or research outputs.

Shen J, Yu Q, Chen S, Tan Q, Li J, Li Y. Unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model. Nature Computational Science 4, 29–42 (2024).

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

Similar Services

Explore related tools that support similar research workflows:

DeepSig Signal Peptide Prediction

TSignal Signal Peptide Prediction

USPNet-fast Signal Peptide Prediction

In Silico Digestion | Pyteomics

Proudly supporting 50,000+ scientists worldwide, including 7,000+ leading biotech and global biopharma organizations.