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Accelerating open modification spectral library searching on tensor core in high-dimensional space.

Jaeyoung Kang1, Weihong Xu2, Wout Bittremieux3

  • 1Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, United States.

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|June 27, 2023
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Summary
This summary is machine-generated.

We developed HOMS-TC, a new algorithm for open modification searching (OMS) in mass spectrometry (MS) proteomics. This parallelized approach significantly accelerates the identification of modified peptides, improving discovery in large datasets.

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Area of Science:

  • Proteomics
  • Computational Biology
  • Biotechnology

Background:

  • Mass spectrometry (MS) proteomics has advanced significantly, improving throughput and reducing costs.
  • Spectral library searching is standard for annotating MS data but misses novel peptides, including those with post-translational modifications (PTMs).
  • Open modification searching (OMS) addresses this but faces challenges with large search spaces and long runtimes.

Purpose of the Study:

  • To introduce HOMS-TC, a novel algorithm designed to accelerate open modification searching (OMS) in mass spectrometry (MS) proteomics.
  • To leverage parallelism across the entire spectral library searching pipeline for enhanced efficiency.
  • To enable the discovery of novel peptides, particularly those with unexpected PTMs, within large-scale MS proteomics datasets.

Main Methods:

  • Developed a new, highly parallel encoding method using hyperdimensional computing to represent spectral data as hypervectors with minimal information loss.
  • Implemented HOMS-TC to process two stages of existing cascade search in parallel, efficiently identifying spectra similar to known peptides while accounting for PTMs.
  • Accelerated the HOMS-TC algorithm on NVIDIA's tensor core units within graphics processing units (GPUs) for substantial performance gains.

Main Results:

  • HOMS-TC demonstrates an average speedup of 31× compared to existing alternative search engines for OMS.
  • The algorithm achieves accuracy comparable to current competing search tools, ensuring reliable peptide identification.
  • The parallelized hyperdimensional computing approach effectively manages large search spaces inherent in OMS.

Conclusions:

  • HOMS-TC offers a significant advancement in the speed and efficiency of open modification searching (OMS) in MS proteomics.
  • The algorithm's parallel architecture and GPU acceleration make it suitable for analyzing the continuously growing scale of proteomics data.
  • HOMS-TC facilitates the discovery of previously unidentified peptides and PTMs, expanding the scope of proteomic analysis.