Structure‐Function Effects in Primary Immunodeficiencies

Korpi, M; Väliaho, Jouni; Vihinen, Mauno

doi:10.1046/j.1365-3083.2000.00799.x

Cited by 9 publications

(12 citation statements)

References 40 publications

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“…We have predicted the consequences of all the mutations on protein structure and function based on sequence and structure information [Holinski-Feder et al, 1998;Jin et al, 1995;Korpi et al, 2000;Maniar et al, 1995;Mattsson et al, 2000;Okoh and Vihinen, 1999;Saha et al, 1997;Shen and Vihinen, 2004;Speletas et al, 2001;Vihinen et al, 1995aVihinen et al, ,b, 1994aVoøchovský et al, 1995;Zhu et al, 1994]. In Figure 6, all the disease causing missense mutations are indicated within PH, SH2, and the kinase domains.…”

Section: Structure^function Correlationsmentioning

confidence: 99%

BTKbase: the mutation database for X-linked agammaglobulinemia

Väliaho¹,

Smith²,

Vihinen³

2006

Hum. Mutat.

185

139

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For the Immunogenetics Special IssueX-linked agammaglobulinemia (XLA) is a hereditary immunodeficiency caused by mutations in the gene encoding Bruton tyrosine kinase (BTK). XLA patients have a decreased number of mature B cells and a lack of all immunoglobulin isotypes, resulting in susceptibility to severe bacterial infections. XLA-causing mutations are collected in a mutation database (BTKbase), which is available at http://bioinf.uta.fi/BTKbase. For each patient the following information is given (when available): the identification of the entry, a plain English description of the mutation followed by a reference, formal characterization of the mutation, and the various parameters from the patient. BTKbase is implemented with the MUTbase program suite, which provides an easy, interactive, and quality controlled submission of information to mutation databases.

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Section: Structure^function Correlationsmentioning

confidence: 99%

BTKbase: the mutation database for X-linked agammaglobulinemia

Väliaho¹,

Smith²,

Vihinen³

2006

Hum. Mutat.

185

139

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“…Sequences surrounding disease-causing CpG mutation sites were analyzed from a number of diseases [Ollila et al, 1996], and GP correlations have been done for some IDs [Lindvall et al, 2005;Vihinen and Durandy, 2006]. Structurefunction relationships have been analyzed for autoimmune regulator (AIRE; MIM] 607358) [Pitkänen et al, 2000;Halonen et al, 2004], Bloom syndrome protein (BLM; MIM] 604610) , Bruton tyrosine kinase (BTK; MIM] 300300) [Vihinen et al, 1994a[Vihinen et al, ,b, 1995a[Vihinen et al, ,b, 1999aOkoh and Vihinen, 1999;Korpi et al, 2000;Shen and Vihinen, 2004] .…”

Section: Using the Idbasesmentioning

confidence: 99%

Immunodeficiency mutation databases (IDbases)

Piirilä¹,

Väliaho²,

Vihinen³

2006

Hum. Mutat.

119

107

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Primary immunodeficiencies (IDs) are a heterogenic group of inherited disorders of the immune system. Immunodeficiency patients have increased susceptibility to recurrent and persistent, even life-threatening infections. Mutations in a large number of genes can cause defects in different cellular functions and lead to impaired immune response. To date, approximately 150 IDs and more than 100 affected genes have been identified. ID-related genes are distributed throughout the genome, and diseases can be inherited in an X-linked, an autosomal recessive, or an autosomal dominant way. We have collected ID mutation data into locus-specific patient-related mutation databases, IDbases (http://bioinf.uta.fi/IDbases). Mutations are described at DNA, mRNA, and protein levels with links to reference sequences and reference articles. The mutation data has been collated into entries along with some clinical information. IDbases offer an easy way, e.g., to find recently identified mutations, to reveal genotype-phenotype correlations, and to discover a specific mutation or to examine the most common mutations in a single immunodeficiency related gene. At the moment we have databases for 107 ID genes with 4,140 public patient entries. An exhaustive statistical analysis of mutation data from the IDbases was made. Missense and nonsense mutations are the most common mutation types, and the most common single substitution is a nonsense mutation from tryptophan to a stop codon. Arginine is the most mutated as well as the most abundant mutant amino acid.

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“…We have modelled several domains and whole protein structures. In this study we examined 14 models [18][19][20][21][22][23][24][25][26][27][28][29]. Among these are Src homology 3 (SH3) domains present in BTK, p67 phox and p47 phox , SH2 domains in BTK and SH2D1A, and a plekstrin homology (PH) domain and kinase domain both in BTK.…”

Section: Resultsmentioning

confidence: 99%

“…Y361C was classified as structural-functional, R307G as functional (pY binding) and G302E, Y334S, L358F and H362Q as structural mutations affecting protein conformation. The SH2 domain model was used for the classification of BTK mutations in a number of publications [26,43,44,46,51]. Mutation C337G was predicted to affect the pY+1 binding site and L346R the pY+3 site [52].…”

Section: Btk Sh2 Domainmentioning

confidence: 99%

Evaluation of Accuracy and Applicability of Protein Models: Retrospective Analysis of Biological and Biomedical Predictions

Khan

Vihinen

2009

In Silico Biology

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In order to study protein function and activity structural data is required. Since experimental structures are available for just a small fraction of all known protein sequences, computational methods such as protein modelling can provide useful information. Over the last few decades we have predicted, with homology modelling methods, the structures for numerous proteins. In this study we assess the structural quality and validity of the biological and medical interpretations and predictions made based on the models. All the models had correct scaffolding and were ranked at least as correct or good by numerical evaluators even though the sequence identity with the template was as low as 8%. The biological explanations made based on models were well in line with experimental structures and other experimental studies. Retrospective analysis of homology models indicates the power of protein modelling when made carefully from sequence alignment to model building and refinement. Modelling can be applied to studying and predicting different kinds of biological phenomena and according to our results it can be done so with success.

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Structure‐Function Effects in Primary Immunodeficiencies

Cited by 9 publications

References 40 publications

BTKbase: the mutation database for X-linked agammaglobulinemia

BTKbase: the mutation database for X-linked agammaglobulinemia

Immunodeficiency mutation databases (IDbases)

Evaluation of Accuracy and Applicability of Protein Models: Retrospective Analysis of Biological and Biomedical Predictions

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