MetaLP: An integrative linear programming method for protein inference in metaproteomics

Metaproteomics based on high-throughput tandem mass spectrometry (MS/MS) plays a crucial role in characterizing microbiome functions. The acquired MS/MS data is searched against a protein sequence database to identify peptides, which are then used to infer a list of proteins present in a metaproteome sample. While the problem of protein inference has been well-studied for proteomics of single organisms, it remains a major challenge for metaproteomics of complex microbial communities because of the large number of degenerate peptides shared among homologous proteins in different organisms. This challenge calls for improved discrimination of true protein identifications from false protein identifications given a set of unique and degenerate peptides identified in metaproteomics. MetaLP was developed here for protein inference in metaproteomics using an integrative linear programming method. Taxonomic abundance information extracted from metagenomics shotgun sequencing or 16s rRNA gene amplicon sequencing, was incorporated as prior information in MetaLP. Benchmarking with mock, human gut, soil, and marine microbial communities demonstrated significantly higher numbers of protein identifications by MetaLP than ProteinLP, PeptideProphet, DeepPep, PIPQ, and Sipros Ensemble. In conclusion, MetaLP could substantially improve protein inference for complex metaproteomes by incorporating taxonomic abundance information in a linear programming model.Author summary: Inferring a reliable list of proteins from identified peptides in metaproteomics is non-trivial because of the prevalence of degenerate peptides in many metaproteome databases. Degenerate peptides are shared among multiple proteins and, therefore, cannot be uniquely attributed to any protein. Here, we developed a protein inference algorithm, MetaLP, for shotgun proteomics analysis of microbial communities to better handle degenerate peptides. Two key innovations in MetaLP were the use of taxonomic abundances as prior information and the formulation of protein inference as a linear programming problem. These features enabled MetaLP to produce substantially more protein identifications in complex metaproteomic datasets than many existing protein inference algorithms.