Distribution analysis of nonsynonymous polymorphisms within the G-protein-coupled receptor gene family

Lee, Andria; Rana, Brinda K.; Schiffer, Hans H.; Schork, Nicholas J.; Brann, Mark R.; Insel, Paul A.; Weiner, David M.

doi:10.1016/s0888-7543(03)00009-0

Cited by 28 publications

(25 citation statements)

References 20 publications

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“…Ultimately, we have found that diseasecausing mutations tend to cluster within the C-terminal lobe rather than the N-terminal lobe (manuscript in preparation). Similar biases have been observed within structural features of other gene families as well (Lee et al, 2003).…”

Section: Discussionsupporting

confidence: 79%

Accurate prediction of deleterious protein kinase polymorphisms

Torkamani

Schork

2007

Bioinformatics

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Motivation: Contemporary, high-throughput sequencing efforts have identified a rich source of naturally occurring single nucleotide polymorphisms (SNPs), a subset of which occur in the coding region of genes and result in a change in the encoded amino acid sequence (non-synonymous coding SNPs or 'nsSNPs'). It is hypothesized that a subset of these nsSNPs may underlie common human disease. Testing all these polymorphisms for disease association would be time consuming and expensive. Thus, computational methods have been developed to both prioritize candidate nsSNPs and make sense of their likely molecular physiologic impact. Results: We have developed a method to prioritize nsSNPs and have applied it to the human protein kinase gene family. The results of our analyses provide high quality predictions and outperform available whole genome prediction methods (74% versus 83% prediction accuracy). Our analyses and methods consider both DNA sequence conservation, which most traditional methods are based on, as well unique structural and functional features of kinases. We provide a ranked list of common kinase nsSNPs that have a higher probability of impacting human disease based on our analyses.

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Section: Discussionsupporting

confidence: 79%

Accurate prediction of deleterious protein kinase polymorphisms

Torkamani

Schork

2007

Bioinformatics

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“…The high level of residue burial also could increase the susceptibility of transmembrane regions to deleterious substitutions, such as those that occur in genetic diseases. In water-soluble proteins, 80% of disease-causing mutations were found to destabilize structure (17) (6,18) and potassium channels (19) has been observed, although the structural basis of this observation has not been investigated. We therefore collected a set of disease-causing variants as described in Methods and listed in Table S2.…”

Section: Is Residue Burial An Important Factor Controlling Evolutionamentioning

confidence: 99%

“…Membrane proteins have been found to diverge faster overall than soluble proteins (2,3), but this increased rate is confined entirely to the rapidly evolving extramembrane regions. Transmembrane regions, on average, diverge much more slowly than the extramembrane regions more slowly than soluble proteins (1,(4)(5)(6).…”

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confidence: 99%

Structural imperatives impose diverse evolutionary constraints on helical membrane proteins

Oberai

Joh

Pettit

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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The amino acid sequences of transmembrane regions of helical membrane proteins are highly constrained, diverging at slower rates than their extramembrane regions and than water-soluble proteins. Moreover, helical membrane proteins seem to fall into fewer families than water-soluble proteins. The reason for the differential restrictions on sequence remains unexplained. Here, we show that the evolution of transmembrane regions is slowed by a previously unrecognized structural constraint: Transmembrane regions bury more residues than extramembrane regions and soluble proteins. This fundamental feature of membrane protein structure is an important contributor to the differences in evolutionary rate and to an increased susceptibility of the transmembrane regions to disease-causing single-nucleotide polymorphisms.disease mutation ͉ potassium channel ͉ protein folding ͉ protein stability ͉ single nucleotide polymorphisms E volutionary rates vary considerably in different cellular compartments (1). Membrane proteins have been found to diverge faster overall than soluble proteins (2, 3), but this increased rate is confined entirely to the rapidly evolving extramembrane regions. Transmembrane regions, on average, diverge much more slowly than the extramembrane regions more slowly than soluble proteins (1, 4-6).A major factor controlling protein sequence divergence is the need to preserve protein function by maintaining a folded structure (7). Because the physical forces that drive folding can change with environment, proteins in different cellular locations can be subject to distinct evolutionary constraints. Membrane proteins, in particular, must accommodate to a dramatically varied environment, ranging from hydrocarbon chains in the bilayer core to water as they emerge from the membrane (8, 9). It therefore seems possible that distinct structural imperatives found in different environments could be an important contributor to evolutionary rates. An obvious sequence adaptation is the hydrophobic matching of the protein exterior, reflected in an apolar transmembrane amino acid composition. Although amino acid diversity is more limited in the transmembrane segments, simple compositional differences do not explain the slower divergence rates of transmembrane regions (1, 4, 5).Here, we find that the transmembrane regions of membrane proteins bury more residues on average than soluble proteins and much more than extramembrane regions, a possible mechanism for increasing stabilization in the absence of the hydrophobic effect. Because buried residues evolve at slower rates than surface residues (10-12), the higher level of residue burial in the transmembrane regions leads to slower sequence divergence. Moreover, we find that higher residue burial may explain a higher prevalence of disease-causing mutations in the transmembrane region of membrane proteins compared with the extramembrane regions. Results and DiscussionTransmembrane Regions Bury More Residues. Fig. 1A shows plots of the fractional surface area buried per residue v...

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“…For instance, by systematically analyzing examples of genetic variation in the coding and non-coding regions of GPCR loci, Rana et al [2001] indicated that the residues in GPCRs involved in ligand binding, G protein coupling, and regulating can be altered by polymorphisms; therefore, the knowledge of the genetic variants that influence transcription, translation, receptor folding, and expression on cell surface, or perturb receptor function, is critical for GPCRs identification, which may provide further information on the development of new agents or the optimization of existing agents [Lee et al, 2003;Small et al, 2003;Tang and Insel, 2005].…”

Section: Genetics Analysis Reveals Clues To Gpcrs Identificationmentioning

confidence: 99%

Computational identification and analysis of G protein‐coupled receptor targets

Feng

Jiang

2006

Drug Development Research

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The completion of the human genome sequence has provided a large pool of potential drug targets for disease therapy. G protein–coupled receptors (GPCRs), which are central to signaling networks that regulate basic cellular processes, represent the most important known class of therapeutic targets for multiple disease states. Bioinformatics approaches can be applied to facilitate the identification of novel GPCRs, understanding their physiological and pathological roles, and screening for drug discovery. The present review summarizes current bioinformatics approaches that can be used to identify and analyze GPCR targets. In addition, the limitations of these technologies with the intention of setting reasonable expectations are also discussed together with some potential avenues for GPCR research. Drug Dev. Res. 67:771–780, 2006. © 2007 Wiley‐Liss, Inc.

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Distribution analysis of nonsynonymous polymorphisms within the G-protein-coupled receptor gene family

Cited by 28 publications

References 20 publications

Accurate prediction of deleterious protein kinase polymorphisms

Accurate prediction of deleterious protein kinase polymorphisms

Structural imperatives impose diverse evolutionary constraints on helical membrane proteins

Computational identification and analysis of G protein‐coupled receptor targets

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