SysPIMP: the web-based systematical platform for identifying human disease-related mutated sequences from mass spectrometry

Abstract

Some mutations resulting in protein sequence change might be tightly related to certain human diseases by affecting its roles, such as sickle cell anemia. Until now several databases, such as PMD, OMIM and HGMD, have been developed, providing useful information about human disease-related mutation. Tandem mass spectrometry (MS) has been used for characterizing proteins in various conditions; however, there is no system in place for finding disease-related mutated proteins within the MS results. Here, a Systematical Platform for Identifying Mutated Proteins (SysPIMP; http://pimp.starflr.info/) was developed to efficiently identify human disease-related mutated proteins within MS results. SysPIMP comprises of three layers: (i) a standardized data warehouse, (ii) a pipeline layer for maintaining human disease databases and X!Tandem and BLAST and (iii) a web-based interface. From OMIM AV part, PMD and SwissProt databases, 35 497 non-redundant human disease-related mutated sequences were collected with disease information described by OMIM terms. With the interfaces to browse sequences archived in SysPIMP, X!Tandem, an open source database-search engine used to identify proteins within MS data, was integrated into SysPIMP to help support the detection of potential human disease-related mutants in MS results. In addition, together with non-redundant disease-related mutated sequences, original non-mutated sequences are also provided in SysPIMP for comparative research. Based on this system, SysPIMP will be the platform for efficiently and intensively studying human diseases caused by mutation.

CanProVar: a human cancer proteome variation database

“…As an example, pull-down experiments in human tumor samples demonstrated that interactions between the oncoprotein MDM2 and the ribosomal protein L11 can be disrupted by missense mutations in MDM2, which help retain MDM2's full p53-suppressive function while escaping inhibition by L11 [Lindstrom, et al, 2007]. As another example, shotgun proteomics data analysis usually relies on database search and adding protein mutation information into the database can help identify mutated proteins [Schandorff, et al, 2007; Xi, et al, 2009]. …”

Quest for Cancer Biomarkers: Assaying Mutant Proteins and RNA that Provides the Much Needed Specificity

“…For this reason, we employed an integrated genomics and proteomics strategy to create a Human Protein Mutant Database (HPMD), against which experimentallyderived secretome peptide spectra were searched. HPMD was created using publicly available datasets of known functional mutations (31,479) and SNPs (140,440 mutations) from UniProt [51], Protein Mutation Database, OMIM and SysPIMP [52]. Using iMASp, we detected 112 putative mutated tryptic peptides (corresponding to 57 proteins) from a panel of 18 human CRC cell line secretome data.…”

An examination of the OMIM database for associating mutation to a consensus reference sequence

“…Although it is not included in each record, an important part of many OMIM records is the ALLELIC VARIANTS (AV) section, which primarily describes disease-producing mutations (Xi et al, 2009). The Entrez Programming Utilities (eUtils) provide a stable Entrez query and database system interface (Wheeler et al, 2007).…”