ArticleBMC genomics2008
Investigation of transmembrane proteins using a computational approach.
Article in BMC genomics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 25 citations in OpenAlex.
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- A critical evaluation of in silico methods for detection of membrane protein intrinsic disorder.Biophysical journal · 2014Article
- Intrinsic disorder in the BK channel and its interactome.PloS one · 2014Article
- A decade and a half of protein intrinsic disorder: biology still waits for physics.Protein science : a publication of the Protein Society · 2013Review
- Phosphoproteomic analysis reveals major default phosphorylation sites outside long intrinsically disordered regions of Arabidopsis plasma membrane proteins.Proteome science · 2012Article
- The use of classification trees for bioinformatics.Wiley interdisciplinary reviews. Data mining and knowledge discovery · 2011Article
- Analysis of structured and intrinsically disordered regions of transmembrane proteins.Molecular bioSystems · 2009Article
- Secretory granule to the nucleus: role of a multiply phosphorylated intrinsically unstructured domain.The Journal of biological chemistry · 2009Article
- Analyzing adjuvant radiotherapy suggests a non monotonic radio-sensitivity over tumor volumes.BMC genomics · 2008Article
- Genomics, molecular imaging, bioinformatics, and bio-nano-info integration are synergistic components of translational medicine and personalized healthcare research.BMC genomics · 2008Article
- Promoting synergistic research and education in genomics and bioinformatics.BMC genomics · 2008Review
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Authors and funding
5 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAn important subfamily of membrane proteins are the transmembrane alpha-helical proteins, in which the membrane-spanning regions are made up of alpha-helices. Given the obvious biological and medical significance of these proteins, it is of tremendous practical importance to identify the location of transmembrane segments. The difficulty of inferring the secondary or tertiary structure of transmembrane proteins using experimental techniques has led to a surge of interest in applying techniques from machine learning and bioinformatics to infer secondary structure from primary structure in these proteins. We are therefore interested in determining which physicochemical properties are most useful for discriminating transmembrane segments from non-transmembrane segments in transmembrane proteins, and for discriminating intrinsically unstructured segments from intrinsically structured segments in transmembrane proteins, and in using the results of these investigations to develop classifiers to identify transmembrane segments in transmembrane proteins.
resultsWe determined that the most useful properties for discriminating transmembrane segments from non-transmembrane segments and for discriminating intrinsically unstructured segments from intrinsically structured segments in transmembrane proteins were hydropathy, polarity, and flexibility, and used the results of this analysis to construct classifiers to discriminate transmembrane segments from non-transmembrane segments using four classification techniques: two variants of the Self-Organizing Global Ranking algorithm, a decision tree algorithm, and a support vector machine algorithm. All four techniques exhibited good performance, with out-of-sample accuracies of approximately 75%.
conclusionsSeveral interesting observations emerged from our study: intrinsically unstructured segments and transmembrane segments tend to have opposite properties; transmembrane proteins appear to be much richer in intrinsically unstructured segments than other proteins; and, in approximately 70% of transmembrane proteins that contain intrinsically unstructured segments, the intrinsically unstructured segments are close to transmembrane segments.
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