Method for rapid protein identification in a large database.

Protein identification is an integral part of proteomics research. The available tools to identify proteins in tandem mass spectrometry experiments are not optimized to face current challenges in terms of identification scale and speed owing to the exponential growth of the protein database and the accelerated generation of mass spectrometry data, as well as the demand for nonspecific digestion and post-modifications in complex-sample identification. As a result, a rapid method is required to mitigate such complexity and computation challenges. This paper thus aims to present an open method to prevent enzyme and modification specificity on a large database. This paper designed and developed a distributed program to facilitate application to computer resources. With this optimization, nearly linear speedup and real-time support are achieved on a large database with nonspecific digestion, thus enabling testing with two classical large protein databases in a 20-blade cluster. This work aids in the discovery of more significant biological results, such as modification sites, and enables the identification of more complex samples, such as metaproteomics samples.