How to use Theoretical Fragment Ions

Calculate theoretical monoisotopic m/z for peptide fragment ions in a, b, c, x, y, and z series, with fragment sequences and charge states.

This service uses Pyteomics 5.0.1 with local sequence algorithms and bundled mass constants. Modified peptides and mass-spectrometry data-file analysis are outside this workflow.

How Theoretical Fragment Ions | Pyteomics Works

Submit peptides of 2–200 residues and choose ion series. The default b/y pair is commonly used for collision-induced fragmentation; c/z or additional series can support other interpretations. a, b, and c ions contain N-terminal fragments; x, y, and z contain C-terminal fragments. Every length from one residue through peptide length minus one is included at each charge from +1 through Maximum Fragment Charge. Calculations use Pyteomics' standard ion compositions (including its z convention), monoisotopic masses, and protons as charge carriers. The output is a mass list, not a prediction of fragment intensity, probability, or instrument observability. Radical variants, neutral losses, modifications, and full-length precursor ions are not included.

Submit up to 100 sequences and 50000 residues total. Output tables are capped at 200000 rows; narrow the digestion range or fragment selections for large submissions.

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 Theoretical Fragment Ions | Pyteomics on Neurosnap

Using Theoretical Fragment Ions | Pyteomics on Neurosnap could drastically accelerate sequence-based peptide analysis with downloadable results.

Analyze multiple named sequences in a single submission.
Review sortable tables and download CSV results.
Use consistent mass and cleavage conventions across sequences.

How to Use Theoretical Fragment Ions | Pyteomics on Neurosnap

To harness the capabilities of Theoretical Fragment Ions | Pyteomics, 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 Theoretical Fragment Ions | Pyteomics.
Provide Inputs: Provide all the inputs specified within the submission panel and optionally configure the tool as desired.
Run Tool: Submit the Theoretical Fragment Ions | Pyteomics 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 Theoretical Fragment Ions | Pyteomics in publications or research outputs.

Goloborodko AA et al. Pyteomics—a Python framework for exploratory data analysis and rapid software prototyping in proteomics. Analytical Chemistry (2013). https://doi.org/10.1021/ac301425u.

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

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