Abstract
BackgroundEnumeration of all theoretically possible amino acid compositions is an important problem in several proteomics workflows, including peptide mass fingerprinting, mass defect labeling, mass defect filtering, and de novo peptide sequencing. Because of the high computational complexity of this task, reported methods for peptide enumeration were restricted to cover limited mass ranges (below 2 kDa). In addition, implementation details of these methods as well as their computational performance have not been provided. The increasing availability of parallel (multi-core) computers in all fields of research makes the development of parallel methods for peptide enumeration a timely topic.ResultsWe describe a parallel method for enumerating all amino acid compositions up to a given length. We present recursive procedures which are at the core of the method, and show that a single task of enumeration of all peptide compositions can be divided into smaller subtasks that can be executed in parallel. The computational complexity of the subtasks is compared with the computational complexity of the whole task. Pseudocodes of processes (a master and workers) that are used to execute the enumerating procedure in parallel are given. We present computational times for our method executed on a computer cluster with 12 Intel Xeon X5650 CPUs (72 cores) running Windows HPC Server. Our method has been implemented as a 32- and 64-bit Windows application using Microsoft Visual C++ and the Message Passing Interface. It is available for download at https://ispace.utmb.edu/users/rgsadygo/Proteomics/ParallelMethod.ConclusionWe describe implementation of a parallel method for generating mass distributions of all theoretically possible amino acid compositions.
Highlights
Enumeration of all theoretically possible amino acid compositions is an important problem in several proteomics workflows, including peptide mass fingerprinting, mass defect labeling, mass defect filtering, and de novo peptide sequencing
In this paper, we presented a detailed description of a parallel method for enumerating all theoretically possible amino acid compositions and discussed different aspects of its implementation
Enumeration of all amino acid compositions is important in several proteomics workflows, including peptide mass fingerprinting, mass defect labeling, mass defect filtering, and de novo peptide sequencing
Summary
Enumeration of all theoretically possible amino acid compositions is an important problem in several proteomics workflows, including peptide mass fingerprinting, mass defect labeling, mass defect filtering, and de novo peptide sequencing. It has been observed that peptide masses have a nonuniform, clustered distribution, which is explained by the fact that peptides are made of twenty amino acids with specific masses. This distribution consists of repeating peaks separated by approximately 1 Da, which become taller and wider as the mass increases. Nonuniformity (peaks, gaps) and discrete nature of the mass distribution of peptides are important for two major problems in MS-based proteomics: peptide identification and de novo sequencing
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