pyLucXor: A Python Package for Accurate Phosphorylation Site Localization

Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

^* Corresponding author: Mingze Bai, This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Mass spectrometry-based phosphoproteomics has emerged as an indispensable tool for deciphering protein signaling networks; however, accurate localization of phosphorylation sites remains a persistent analytical challenge. Here, we present pyLucXor, a Python package that implements the LuciPHOr localization algorithm within a framework built upon PyOpenMS. A central methodological contribution of this work is the introduction of an alanine-decoy-based target-decoy strategy, which enables direct, site-level estimation of the false localization rate (FLR). Benchmarking on the publicly available synthetic phosphopeptide dataset PXD000138 demonstrates that, under stringent 1% site-level FLR control, pyLucXor confidently localized 51,468 phosphorylation sites with a localization accuracy of 98.55%, compared with 48,186 sites (98.84% accuracy) identified by the original LuciPHOr. These results indicate that pyLucXor recovers a substantially greater number of phosphorylation sites under equivalent FLR constraints while maintaining comparable localization accuracy. In summary, the alanine-decoy-based global FLR estimation framework provides a statistically principled and computationally scalable quality-control solution for large-scale phosphoproteomic studies.