Advancement in protein inference from shotgun proteomics using peptide detectability.
A major challenge in shotgun proteomics has been the assignment of identified peptides to the proteins from which they originate, referred to as the protein inference problem. Redundant and homologous protein sequences present a challenge in being correctly identified, as a set of peptides may in many cases represent multiple proteins. One simple solution to this problem is the assignment of the smallest number of proteins that explains the identified peptides. However, it is not certain that a natural system should be accurately represented using this minimalist approach. In this paper, we propose a reformulation of the protein inference problem by utilizing the recently introduced concept of peptide detectability. We also propose a heuristic algorithm to solve this problem and evaluate its performance on synthetic and real proteomics data. In comparison to a greedy implementation of the minimum protein set algorithm, our solution that incorporates peptide detectability performs favorably.
Alves
Pedro
P
School of Informatics, Indiana University, Bloomington, USA.
Arnold
Randy J
RJ
Novotny
Milos V
MV
Radivojac
Predrag
P
Reilly
James P
JP
Tang
Haixu
H
D000465:Algorithms; D000818:Animals; D001923:Brain Chemistry; D019295:Computational Biology; D030562:Databases, Protein; D010446:Peptide Fragments / Q000302:isolation & purification; D040081:Protein Array Analysis / Q000706:statistics & numerical data; D040901:Proteomics / Q000706:statistics & numerical data*; D051381:Rats; D049490:Systems Biology; D053719:Tandem Mass Spectrometry
D010446:Peptide Fragments