Jul 1, Michael Taussig; Proteomics: From Protein Sequence to Function, Briefings in Functional Genomics, This content is only available as a PDF. Request PDF on ResearchGate | On Jan 1, , Harry Dailey and others published Proteomics - from protein sequence to function: S. R. Pennington, M. J. . Feb 16, Request PDF on ResearchGate | On Jun 1, , Harry A. Dailey and others published Proteomics - from protein sequence to function.

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ary structure prediction, membrane protein bioinformatics, human proteome, Keywords: Sequence, structure, function, proteins, membrane, bioinformatics. Ebook Proteomics From Protein Sequence To Function currently available at for review only, if you need complete ebook Proteomics From Protein. and function of proteins and the diverse biological functions that they perform. exponential production of genomic sequence data for a rapidly increasing.

DNA in the genome is only one aspect of the complex mechanism that keeps an organism running — so decoding the DNA is one step towards understanding the process. However, by itself, it does not specify everything that happens within the organism.

The basic flow of genetic information in a cell is as follows. The complete set of RNA also known as its transcriptome is subject to some editing cutting and pasting to become messenger-RNA, which carries information to the ribosome, the protein factory of the cell, which then translates the message into protein.

Figure 1. Genes, proteins, and molecular machines Source: U. This project aims to completely sequence the entire rice genome 12 rice chromosomes and subsequently apply the knowledge to improve rice production.

Pocket K No. 15: 'Omics' Sciences: Genomics, Proteomics, and Metabolomics

In , the draft genome sequences of two agriculturally important subspecies of rice, indica and japonica, were published. Once completed, the rice genome sequence will serve as a model system for other cereal grasses and will assist in identifying important genes in maize, wheat, oats, sorghum, and millet.

The complete set of proteins in a cell can be referred to as its proteome and the study of protein structure and function and what every protein in the cell is doing is known as proteomics. The proteome is highly dynamic and it changes from time to time in response to different environmental stimuli. The goal of proteomics is to understand how the structure and function of proteins allow them to do what they do, what they interact with, and how they contribute to life processes.

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Related articles in Google Scholar. Citing articles via Google Scholar. Interplay between miRNAs and host genes and their role in cancer. Computational approaches for alternative and transient secondary structures of ribonucleic acids. Tantalizing dilemma in risk prediction from disease scoring statistics. Utility programs such as PeptideProphet [ 14 ] and ProteinProphet [ 15 ] are designed to improve the accuracy of peptide and protein identification using statistical models, while DBParser [ 16 ] employs a parsimony principal to consolidate redundant protein assignments.

Several publicly available search algorithms were evaluated and benchmarked for sensitivity and specificity recently [ 17 ].

Furthermore, an entire pipeline has been developed for experiment annotation, database searching, peptide mining, and protein identification [ 18 ]. Once proteins are identified from the biological samples, they need to be analyzed for functional involvement in metabolic and signaling pathways and cellular functions and processes.

Many programs have been developed for the biological interpretation of large lists of genes from genome-scale experiments, mostly for microarray gene expression data, with a few being extended to proteomics data.

As the Gene Ontology GO [ 19 ] has become the common standard for genome annotation, most of these programs provide functional analysis in the context of GO for examples, see http: A few examples include: A major issue in proteomics and tandem mass spectroscopy protein identification is that the general purpose protein sequence databases leave out many alternative splice iso-forms or include them only in the text comments. As a result, proteomic analysis may fail to identify bona fide protein products of alternative splice isoforms because the target sequence was not present in the database being searched.

The absence of real protein sequences in the sequence library may further lead to incorrect peptide and protein identification due to the presence of degenerate peptides corresponding to more than one protein in the sequence database. Another common problem when dealing with a large list of proteins annotated in different places is the lack of standardization. Even different versions of the same protein database may result in different IDs if the database identifier is not stable.

The lack of common protein identifiers and naming standards presents a challenge for integrating annotations from multiple heterogeneous sources for biological interpretation of proteomic data.

Consequently, expression data analysis is often carried out in an ad hoc manner, resulting in a fragmented and inefficient use of rich annotations available in numerous information resources. Built upon the infrastructure developed by the investigative team at the Protein Information Resource [ 29 ], iProXpress facilitates protein identification using a comprehensive sequence library and functional interpretation using integrated data.

The iProXpress integrated protein expression analysis system is designed for function and pathway discovery from large-scale proteomic data, in a systems biology context, providing rich functional descriptions for individual proteins and detecting functional relationships among them. The system consists of three major software modules to support functionalities in protein mapping, functional annotation and expression profiling Fig.

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The protein mapping module is designed to map user-submitted data to corresponding UniProt entries for data analysis in the protein-centric framework. In the future, iProXpress will accept other user-supplied high-throughput data types.

UniRef is a non-redundant reference database maintained at PIR that provides complete non-redundant sequence coverage by combining identical sequences and sub-fragments into single entries. Currently containing about 3. The sequence space is further expanded by representing the variant sequences such as splice variants and isoforms annotated in UniProtKB as separate UniRef entries.

This data list is extended on a regular basis to guarantee full coverage of sequence space.

UniRef is the most comprehensive database of its kind due to the curation activities of the UniProt consortium. UniParc, currently containing over 5.

Challenges and Solutions in Proteomics

Though many UniParc entries may still undergo sequence revisions, UniParc sequences not present in UniRef nevertheless are useful as a supplement to the sequence library for complete coverage. While the sequence library covers proteins in source organisms from the entire taxonomic range, options will be provided for users to select sub-datasets based on taxonomy e.

In the future, the sequence library will be further expanded to improve the sensitivity of protein identification. New data sources to be integrated will include: For proteomic data interpretation, a high level of annotation, minimal level of redundancy, and high degree of data integration is critical. To cross-validate the ID mapping, the peptide sequence of each mapped protein is matched against the cross-referenced U-niProt sequence to confirm the correct assignment.

For many-to-one mapping, where multiple IDs map to the same UniProt protein, as is often the case for GI numbers, this mapping removes redundancy effectively.

For proteins that cannot be mapped to UniProt entries through ID mapping, iProXpress searches their peptide sequences against the entire UniRef sequence set, or against a species-specific subset if appropriate. There are several mapping scenarios.

In the case of one-to-one mapping, where the peptide matches exactly one UniProt protein, that distinct protein receives the assignment. When all matched protein entries are in the same UniRef90 cluster, the peptides are mapped to the representative sequence from that cluster or to the member protein from the same species. Proteins in the input list that are not mapped after ID and peptide mapping to UniRef are mapped to the unique UniParc sequence library.


All proteins mapped with sequence variations are flagged for manual validation. To demonstrate the ability of iProXpress to process large-scale proteomic datasets, we analyzed the proteome of a human embryonic carcinoma stem cell line, NTera2, using our robust offline multidimensional protein identification strategy together with automated linear ion trap mass spectrometry. This large scale proteomic profiling of the Ntera2 cell and its comparison with DNA microarray data contribute to a better understanding of gene regulation at a global level.

After the protein mapping, rich annotation is presented in a protein information matrix based on sequence analysis and integration of information from the iProClass database.

Pre-computed sequence features include homologous proteins in KEGG, BioCarta and other curated pathway databases to populate pathway annotation, InterProScan for family, domain and motif identification, and Phobius for trans-membrane helix and signal peptide prediction.

Properties derived from homology-based inference are presented in the information matrix with evidence attribution. Functional profiling analysis aims at discovering the functional significance of expressed proteins, the plausible functions and pathways, and the hidden relationships and interconnecting components of proteins, such as proteins sharing common functions, pathways, or cellular networks.

As shown in Fig. For functional categorization, proteins are grouped based on annotations such as GO terms and KEGG and BioCarta pathways, and then correlated with sequence similarity to identify relationships among individual proteins or protein groups. The functional categorization chart B displays the frequency number of occurrences of proteins in each functional category.

The cross-comparison matrix C shows the comparative distribution of functional categories in multiple datasets.

Bioinformatics Resources for In Silico Proteome Analysis

Functional profiling: A protein information matrix, B functional categorization chart, C cross-comparison matrix, D graphical GO hierarchy. To correlate functional associations of expressed proteins in different samples, the relative enrichment of a given functional category in each sample will be calculated to identify all samples that contain a statistically significant proportion of proteins that are associated with the given category.

Likewise, the system will point to groups of proteins that show a statistically significant correlation with certain pathways or functions, thus enabling characterization of biological pathways. Evidence on differential protein expression, protein interactions, pathway membership, and other attributes is combined to provide the evidence for pathway and network participation.Others are membrane proteins that act as receptors whose main function is to bind a signaling molecule and induce a biochemical response in the cell.

Author information Article notes Copyright and License information Disclaimer. Utility of a direct dual-mode development analysis on blotted protein mixtures. Assessing cerebrospinal fluid rhinorrhea: Many programs have been developed for the biological interpretation of large lists of genes from genome-scale experiments, mostly for microarray gene expression data, with a few being extended to proteomics data.

The term "tertiary structure" is often used as synonymous with the term fold. For instance, of the 20, or so proteins encoded by the human genome, only 6, are detected in lymphoblastoid cells. It currently June contains statistical and analytical data for the proteins from 77 complete genomes.

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