ANN-SoLo

ANN-SoLo performs open modification searching (OMS) of spectral libraries in proteomics to identify peptides with post-translational modifications by matching modified spectra to unmodified counterparts across a broad precursor mass window.

Key Features:

• Open Modification Searching (OMS): Uses a broad precursor mass window to enable matching of modified spectra to unmodified library spectra and inference of modification types from precursor mass differences.
• Approximate Nearest Neighbor (ANN) Indexing: Applies ANN indexing to select a limited subset of the most relevant library spectra for each query spectrum, reducing computational cost.
• GPU-Accelerated Candidate Selection: Offloads candidate selection to graphics processing units to accelerate computations.
• Feature Hashing: Converts high-resolution spectra into low-dimensional vectors via a feature hashing scheme.
• Low-Level Code Optimizations: Incorporates code-level optimizations to further improve computational efficiency.
• Large-Scale Performance: Enables open searches at scale with up to an order of magnitude speed improvement over earlier versions.
• Implementation: Implemented in Python and C++.

Scientific Applications:

• Peptide identification with post-translational modifications: Identifies peptides carrying post-translational modifications through open spectral library searches.
• Modification type inference: Infers modification types from observed precursor mass differences between matched modified and unmodified spectra.
• Proteome-wide modification landscape analysis: Supports large-scale exploration of protein modifications and has been demonstrated on the draft human proteome dataset.

Methodology:

Performs OMS using a broad precursor mass window, employs approximate nearest neighbor (ANN) indexing for candidate selection with GPU acceleration, converts high-resolution spectra to low-dimensional vectors via feature hashing, and applies low-level code optimizations.