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Investigation and prediction of the severity of p53 mutants using parameters from structural calculations
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics . Linköping University, The Institute of Technology.
Department of Oncology-Pathology, Cancer Center Karolinska (CCK), Karolinska Institutet, Stockholm, Sweden.
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics . Linköping University, The Institute of Technology.
2009 (English)In: The FEBS Journal, ISSN 1742-464X, Vol. 276, no 15, 4142-4155 p.Article in journal (Refereed) Published
Abstract [en]

A method has been developed to predict the effects of mutations in the p53 cancer suppressor gene. The new method uses novel parameters combined with previously established parameters. The most important parameter is the stability measure of the mutated structure calculated using molecular modelling. For each mutant, a severity score is reported, which can be used for classification into deleterious and nondeleterious. Both structural features and sequence properties are taken into account. The method has a prediction accuracy of 77% on all mutants and 88% on breast cancer mutations affecting WAF1 promoter binding. When compared with earlier methods, using the same dataset, our method clearly performs better. As a result of the severity score calculated for every mutant, valuable knowledge can be gained regarding p53, a protein that is believed to be involved in over 50% of all human cancers.

Place, publisher, year, edition, pages
2009. Vol. 276, no 15, 4142-4155 p.
Keyword [en]
Cancer; molecular modelling; mutations; p53; structural prediction
National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-20141DOI: 10.1111/j.1742-4658.2009.07124.xOAI: diva2:233395
Available from: 2009-08-31 Created: 2009-08-31 Last updated: 2009-10-19Bibliographically approved
In thesis
1. Mutational effects on protein structure and function
Open this publication in new window or tab >>Mutational effects on protein structure and function
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis several important proteins are investigated from a structural perspective. Some of the proteins are disease related while other have important but not completely characterised functions. The techniques used are general as demonstrated by applications on metabolic proteins (CYP21, CYP11B1, IAPP, ADH3), regulatory proteins (p53, GDNF) and a transporter protein (ANTR1).

When the protein CYP21 (steroid 21-hydroxylase) is deficient it causes CAH (congenital adrenal hyperplasia). For this protein, there are about 60 known mutations with characterised clinical phenotypes. Using manual structural analysis we managed to explain the severity of all but one of the mutations. By observing the properties of these mutations we could perform good predictions on, at the time, not classified mutations.

For the cancer suppressor protein p53, there are over thousand mutations with known activity. To be able to analyse such a large number of mutations we developed an automated method for evaluation of the mutation effect called PREDMUT. In this method we include twelve different prediction parameters including two energy parameters calculated using an energy minimization procedure. The method manages to differentiate severe mutations from non-severe mutations with 77% accuracy on all possible single base substitutions and with 88% on mutations found in breast cancer patients.

The automated prediction was further applied to CYP11B1 (steroid 11-beta-hydroxylase), which in a similar way as CYP21 causes CAH when deficient. A generalized method applicable to any kind of globular protein was developed. The method was subsequently evaluated on nine additional proteins for which mutants were known with annotated disease phenotypes. This prediction achieved 84% accuracy on CYP11B1 and 81% accuracy in total on the evaluation proteins while leaving 8% as unclassified. By increasing the number of unclassified mutations the accuracy of the remaining mutations could be increased on the evaluation proteins and substantially increase the classification quality as measured by the Matthews correlation coefficient. Servers with predictions for all possible single based substitutions are provided for p53, CYP21 and CYP11B1.

The amyloid formation of IAPP (islet amyloid polypeptide) is strongly connected to diabetes and has been studied using both molecular dynamics and Monte Carlo energy minimization. The effects of mutations on the amount and speed of amyloid formation were investigated using three approaches. Applying a consensus of the three methods on a number of interesting mutations, 94% of the mutations could be correctly classified as amyloid forming or not, evaluated with in vitro measurements.

In the brain there are many proteins whose functions and interactions are largely unknown. GDNF (glial cell line-derived neurotrophic factor) and NCAM (neural cell adhesion molecule) are two such neuron connected proteins that are known to interact. The form of interaction was studied using protein--protein docking where a docking interface was found mediated by four oppositely charged residues in respective protein. This interface was subsequently confirmed by mutagenesis experiments. The NCAM dimer interface upon binding to the GDNF dimer was also mapped as well as an additional interacting protein, GFRα1, which was successfully added to the protein complex without any clashes.

A large and well studied protein family is the alcohol dehydrogenase family, ADH. A class of this family is ADH3 (alcohol dehydrogenase class III) that has several known substrates and inhibitors. By using virtual screening we tried to characterize new ligands. As some ligands were already known we could incorporate this knowledge when the compound docking simulations were scored and thereby find two new substrates and two new inhibitors which were subsequently successfully tested in vitro.

ANTR1 (anion transporter 1) is a membrane bound transporter important in the photosynthesis in plants. To be able to study the amino acid residues involved in inorganic phosphate transportation a homology model of the protein was created. Important residues were then mapped onto the structure using conservation analysis and we were in this way able to propose roles of amino acid residues involved in the transportation of inorganic phosphate. Key residues were subsequently mutated in vitro and a transportation process could be postulated.

To conclude, we have used several molecular modelling techniques to find functional clues, interaction sites and new ligands. Furthermore, we have investigated the effect of muations on the function and structure of a multitude of disease related proteins.


Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. 80 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1271
Mutation, prediction, phenotypes, homology model, virtual screening, molecular dynamics, amyloid, cancer, membrane protein
National Category
Bioinformatics and Systems Biology
urn:nbn:se:liu:diva-50491 (URN)978-91-7393-539-9 (ISBN)
Public defence
2009-11-06, Planck, Fysikhuset, Campus Valla, Linköpings universitet, Linköping, 10:00 (English)
Available from: 2009-10-19 Created: 2009-10-12 Last updated: 2009-10-19Bibliographically approved

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