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Abstract:
Prediction and structural modeling of protein-protein interactions (PPIs) are essential for understanding biological processes. Most large-scale experimental and computational approaches that predict PPIs do not provide structural information. We present a novel approach, XLEC, combining cross-linking mass spectrometry (XL-MS) and evolutionary coupling (EC) data for efficient proteome-wide prediction and modeling of PPIs. While EC derived from multiple sequence alignments primarily yield information on direct contacts between proteins across the interface, XL-MS data preferentially captures longer-range interactions, hence these methods contain complementary information. XLEC integrates information from both approaches in a machine learning-based model and subsequent restraint-based modeling of the complex structure. We applied XLEC to data from the murine mitochondrial proteome and compared its performance to those of XL-MS and EC separately. Our assessment suggests that the XLEC predictor outperforms those of XL-MS, and EC for PPI prediction (ROC-AUC: 0.73, 0.64, and 0.68, respectively). Furthermore, XLEC-based modeling of PPIs achieved excellent L-RMSD (<10 Å) for 16.4% of the benchmark dataset (EC only: 7%, XL-MS only: 2.9%;). Using XLEC, we generated more than 400 de novo PPI models revealing novel insights into the mitochondrial interactome including the interaction between the alanine aminotransferase 2 (ALAT2) and the hexokinase-1 (HXK1) and the interaction between the DNA topoisomerase I (TOP1M) and the electrogenic aspartate/glutamate antiporter (SLC25A13).
