Accelerated isotope fine structure calculation using pruned transition trees.

Area of Science:

• Computational Chemistry
• Analytical Chemistry
• Bioinformatics

Background:

• Accurate isotope pattern calculation is vital for mass spectrometry data interpretation.
• High-resolution mass spectrometry requires precise mass and probability calculations for isotopologues.
• Existing methods can be computationally intensive and memory-demanding.

Purpose of the Study:

• To develop a fast and memory-efficient algorithm for theoretical isotope pattern calculation.
• To improve the routine interpretation of mass spectrometric data.
• To provide a robust method for handling polyisotopic compounds in high-resolution experiments.

Main Methods:

• Introduction of a novel treelike structure for deriving subisotopologues.
• Utilizing single isotope replacement transitions within separable tree branches.
• Implementing a two-phase approach: initial detection of most probable isotopologues followed by parallelized pruning using relative thresholds.
• Validation through large-scale benchmark simulations with thousands of molecular formulas.

Main Results:

• The treelike algorithm significantly enhances calculation speed and memory efficiency.
• The method effectively prunes low-probable isotopologues, improving data interpretation.
• Benchmark simulations demonstrated performance gains and reduced data distortion with relative thresholds.
• The algorithm successfully handled a large and diverse set of molecular formulas.

Conclusions:

• The novel treelike algorithm offers a substantial improvement in calculating theoretical isotope patterns.
• The enviPat R-package and web interface provide accessible tools for researchers.
• This advancement facilitates more routine and accurate interpretation of complex mass spectrometric data.