Accelerating a cross-correlation score function to search modifications using a single GPU

Abstract

Abstract Background A cross-correlation (XCorr) score function is one of the most popular score functions utilized to search peptide identifications in databases, and many computer programs, such as SEQUEST, Comet, and Tide, currently use this score function. Recently, the HiXCorr algorithm was developed to speed up this score function for high-resolution spectra by improving the preprocessing step of the tandem mass spectra. However, despite the development of the HiXCorr algorithm, the score function is still slow because candidate peptides increase when post-translational modifications (PTMs) are considered in the search. Results We used a graphics processing unit (GPU) to develop the accelerating score function derived by combining Tide’s XCorr score function and the HiXCorr algorithm. Our method is 2.7 and 5.8 times faster than the original Tide and Tide-Hi, respectively, for 50 Da precursor tolerance. Our GPU-based method produced identical scores as did the CPU-based Tide and Tide-Hi. Conclusion We propose the accelerating score function to search modifications using a single GPU. The software is available at https://github.com/Tide-for-PTM-search/Tide-for-PTM-search.

Keywords

• Peptide identification
• Tide
• Cross-correlation score function
• High performance computing
• PTM search