Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing

Area of Science:

• Proteomics
• Computational Biology
• Mass Spectrometry

Background:

• Open modification searching (OMS) is crucial for identifying peptides with unknown modifications.
• Existing OMS tools are computationally intensive, leading to long search times and suboptimal results.
• There is a need for optimized tools to enhance the speed and accuracy of OMS.

Purpose of the Study:

• To introduce ANN-SoLo, a novel tool designed for fast and accurate open spectral library searching.
• To address the computational limitations of current OMS strategies.
• To improve the identification of both unmodified and modified peptides.

Main Methods:

• Utilizes approximate nearest neighbor (ANN) indexing to efficiently select relevant library spectra for comparison.
• Implements a cascade search strategy to maximize identifications while controlling the false discovery rate.
• Employs a shifted dot product score for sensitive matching of modified spectra to their unmodified counterparts.

Main Results:

• ANN-SoLo demonstrates state-of-the-art performance in speed and the number of identifications.
• Achieves an order of magnitude speedup compared to existing tools like SpectraST.
• Confidently identifies more spectra than SpectraST and MSFragger on human cell line data.

Conclusions:

• ANN-SoLo significantly enhances the speed and accuracy of open modification searching.
• Provides a powerful and efficient solution for proteomic data analysis.
• Enables more comprehensive and sensitive identification of modified peptides.