Protein probability model for high-throughput protein identification from their peptides

Shotgun proteomics is the method of choice for high-throughput protein identification; however, robust statistical methods are essential to automatize this task while minimizing the number of false identifications. The development of an appropriate scoring model to identify proteins from their peptides using high-throughput shotgun proteomics is highly needed.

CNIC and UPV/EHU researchers have developed a novel protein-level scoring algorithm that uses the scores of the identified peptides and maintains all of the properties expected for a true protein probability. This protein probability model offers the scientific community an algorithm that is robust and easy to integrate into protein identification workflows, showing that the identification performance of this workflow is superior to that of other widely used methods.

Research article