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Prediction of the human membrane proteome
KTH, School of Biotechnology (BIO), Proteomics.ORCID iD: 0000-0003-0198-7137
KTH, School of Biotechnology (BIO), Proteomics.
KTH, School of Biotechnology (BIO), Proteomics.ORCID iD: 0000-0001-8993-048X
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2010 (English)In: Proteomics, ISSN 1615-9853, E-ISSN 1615-9861, Vol. 10, no 6, 1141-1149 p.Article in journal (Refereed) Published
Abstract [en]

Membrane proteins are key molecules in the cell, and are important targets for pharmaceutical drugs. Few three-dimensional structures of membrane proteins have been obtained, which makes computational prediction of membrane proteins crucial for studies of these key molecules. Here, seven membrane protein topology prediction methods based on different underlying algorithms, such as hidden Markov models, neural networks and support vector machines, have been used for analysis of the protein sequences from the 21 416 annotated genes in the human genome. The number of genes coding for a protein with predicted cc-helical transmembrane region(s) ranged from 5508 to 7651, depending on the method used. Based on a majority decision method, we estimate 5539 human genes to code for membrane proteins, corresponding to approximately 26% of the human protein-coding genes. The largest fraction of these proteins has only one predicted transmembrane region, but there are also many proteins with seven predicted transmembrane regions, including the G-protein coupled receptors. A visualization tool displaying the topologies suggested by the eight prediction methods, for all predicted membrane proteins, is available on the public Human Protein Atlas portal (

Place, publisher, year, edition, pages
2010. Vol. 10, no 6, 1141-1149 p.
Keyword [en]
Bioinformatics, Human proteome, Membrane protein, Prediction
National Category
Biochemistry and Molecular Biology Biochemistry and Molecular Biology
URN: urn:nbn:se:kth:diva-28356DOI: 10.1002/pmic.200900258ISI: 000276337800004ScopusID: 2-s2.0-77949732088OAI: diva2:389721
Knut and Alice Wallenberg Foundation
QC 20110120Available from: 2011-01-20 Created: 2011-01-14 Last updated: 2011-03-17Bibliographically approved
In thesis
1. Mapping the human proteome using bioinformatic methods
Open this publication in new window or tab >>Mapping the human proteome using bioinformatic methods
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The fundamental goal of proteomics is to gain an understanding of the expression and function of the proteome on the level of individual proteins, on the level of defined cell types and on the level of the entire organism. In this thesis, the human proteome is explored using membrane protein topology prediction methods to define the human membrane proteome and by global protein expression profiling, which relies on a complex study of the location and expression levels of proteins in tissues and cells.

A whole-proteome analysis was performed based on the predicted protein-coding genes of humans using a selection of membrane protein topology prediction methods. The study used a majority decision-based method, which estimated that approximately 26% of the human genes encode for a membrane protein. The prediction results are displayed in a visualization tool to facilitate the selection of antigens to be used for antibody generation.

Global protein expression profiles in a large number of cells and tissues in the human body were analyzed for more than 4000 protein targets, based on data from the antibody-based immunohistochemistry and immunofluorescence methods within the framework of the Human Protein Atlas project. The results revealed few cell-type specific proteins and a high fraction of human proteins expressed in most cells, suggesting that cell and tissue specificity is attained by a fine-tuned regulation of protein levels. The expression profiles were also used to analyze the relationship between 45 cell lines by hierarchical clustering and principal component analysis. The global protein expression patterns overall reflected the tumor origin of the cells, and also allowed for identification of proteins of importance for distinguishing different categories of cell lines, as defined by phenotype of progenitor cell. In addition, the protein distribution in 16 subcellular compartments in three of the human cell lines was mapped. A large fraction of proteins were localized in two or more compartments and, in line with previous results, a majority of proteins were detected in all three cell lines.

Finally, mass spectrometry-based protein expression levels were compared to RNA-seq-based transcript expression levels in three cell lines. Highly ubiquitous mRNA expression was found and the changes of expression levels between the cell lines showed high correlations between proteins and transcripts. Large general differences in abundance of proteins from various functional classes were observed. A comparison between categories based on expression levels revealed that, in general, genes with varying expression levels between the cell lines or only expressed in one cell line were highly enriched for cell-surface proteins.

These studies show a path for a systematic analysis to characterize the proteome in human cells, tissues and organs.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology., 2011. 66 p.
Trita-BIO-Report, ISSN 1654-2312 ; 2011:4
proteome, transcriptome, bioinformatics, membrane protein prediction, subcellular localization, protein expression level, cell line, immunohistochemistry, immunofluorescence
National Category
Bioinformatics and Systems Biology
Research subject
SRA - Molecular Bioscience
urn:nbn:se:kth:diva-31477 (URN)978-91-7415-886-1 (ISBN)
Public defence
2011-04-08, F3, Lindstedtsvägen 26, KTH, Stockholm, 14:41 (English)
The Human Protein Atlas project
Knut and Alice Wallenberg Foundation
QC 20110317Available from: 2011-03-17 Created: 2011-03-16 Last updated: 2011-03-17Bibliographically approved

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