Structural Characteristics Determine the Cause of the Low Enzyme Activity of Two Thiopurine S-Methyltransferase Allelic Variants: A Biophysical Characterization of TPMT*2 and TPMT*5

Pharmacogenetic studies of thiopurine methyltransferase genotype-phenotype concordance and effect of methotrexate on thiopurine metabolism

The discovery and implementation of thiopurine methyltransferase (TPMT) pharmacogenetics has been a success story and has reduced the suffering from serious adverse reactions during thiopurine treatment of childhood leukaemia and inflammatory bowel disease. This MiniReview summarizes four studies included in Dr Zimdahl Kahlin's doctoral thesis as well as the current knowledge on this field of research. The genotype‐phenotype concordance of TPMT in a cohort of 12 663 individuals with clinically analysed TPMT status is described. Notwithstanding the high concordance, the benefits of combined genotyping and phenotyping for TPMT status determination are discussed. The results from the large cohort also demonstrate that the factors of gender and age affect TPMT enzyme activity. In addition, characterization of four previously undescribed TPMT alleles (TPMT*41, TPMT*42, TPMT*43 and TPMT*44) shows that a defective TPMT enzyme could be caused by several different mechanisms. Moreover, the folate analogue methotrexate (MTX), used in combination with thiopurines during maintenance therapy of childhood leukaemia, affects the metabolism of thiopurines and interacts with TPMT, not only by binding and inhibiting the enzyme activity but also by regulation of its gene expression.

Comprehensive study of thiopurine methyltransferase genotype, phenotype, and genotype-phenotype discrepancies in Sweden

Thiopurines are widely used in the treatment of leukemia and inflammatory bowel diseases. Thiopurine metabolism varies among individuals because of differences in the polymorphic enzyme thiopurine methyltransferase (TPMT, EC 2.1.1.67), and to avoid severe adverse reactions caused by incorrect dosing it is recommended that the patient's TPMT status be determined before the start of thiopurine treatment. This study describes the concordance between genotyping for common TPMT alleles and phenotyping in a Swedish cohort of 12,663 patients sampled before or during thiopurine treatment. The concordance between TPMT genotype and enzyme activity was 94.5%. Compared to the genotype, the first measurement of TPMT enzyme activity was lower than expected for 4.6% of the patients. Sequencing of all coding regions of the TPMT gene in genotype/phenotype discrepant individuals led to the identification of rare and novel TPMT alleles. Fifteen individuals (0.1%) with rare or novel genotypes were identified, and three TPMT alleles (TPMT*42, *43, and *44) are characterized here for the first time. These 15 patients would not have been detected as carrying a deviating TPMT genotype if only genotyping of the most common TPMT variants had been performed. This study highlights the benefit of combining TPMT genotype and phenotype determination in routine testing. More accurate dose recommendations can be made, which might decrease the number of adverse reactions and treatment failures during thiopurine treatment.

In Vitro Protein Stability of Two Naturally Occurring Thiopurine S-Methyltransferase Variants: Biophysical Characterization of TPMT*6 and TPMT*8

Thiopurine S-methyltransferase (TPMT) is a polymorphic enzyme involved in the metabolism and inactivation of thiopurine substances administered as immunosuppressants in the treatment of malignancies and autoimmune diseases. In this study, the naturally occurring variants, TPMT*6 (Y180F) and TPMT*8 (R215H), have been biophysically characterized. Despite being classified as low and intermediate in vivo enzyme activity variants, respectively, our results demonstrate a discrepancy because both TPMT*6 and TPMT*8 were found to exhibit normal functionality in vitro. While TPMT*8 exhibited biophysical properties almost indistinguishable from those of TPMTwt, the TPMT*6 variant was found to be destabilized. Furthermore, the contributions of the cofactor S-adenosylmethionine (SAM) to the thermodynamic stability of TPMT were investigated, but only a modest stabilizing effect was observed. Also presented herein is a new method for studies of the biophysical characteristics of TPMT and its variants using the extrinsic fluorescent probe 8-anilinonaphthalene-1-sulfonic acid (ANS). ANS was found to bind strongly to all investigated TPMT variants with a K_d of approximately 0.2 μM and a 1:1 binding ratio as determined by isothermal titration calorimetry (ITC). Circular dichroism and fluorescence measurements showed that ANS binds exclusively to the native state of TPMT, and binding to the active site was confirmed by molecular modeling and simulated docking as well as ITC measurements. The strong binding of the probe to native TPMT and the conformity of the obtained results demonstrate the advantages of using ANS binding characteristics in studies of this protein and its variants.

Robust and convenient analysis of protein thermal and chemical stability

We present the software CDpal that is used to analyze thermal and chemical denaturation data to obtain information on protein stability. The software uses standard assumptions and equations applied to two-state and various types of three-state denaturation models in order to determine thermodynamic parameters. It can analyze denaturation monitored by both circular dichroism and fluorescence spectroscopy and is extremely flexible in terms of input format. Furthermore, it is intuitive and easy to use because of the graphical user interface and extensive documentation. As illustrated by the examples herein, CDpal should be a valuable tool for analysis of protein stability.

Structural and functional impact of missense mutations in TPMT: An integrated computational approach

BACKGROUND

Thiopurine S-methyltransferase (TPMT) detoxifies thiopurine drugs which are used for treatment of various diseases including inflammatory bowel disease (IBD), and hematological malignancies. Individual variation in TPMT activity results from mutations in TPMT gene. In this study, the effects of all the known missense mutations in TPMT enzyme were studied at the sequence and structural level

METHODS

A broad set of bioinformatic tools was used to assess all the known missense mutations affecting enzyme activity. The effects of these mutations on protein stability, aggregation propensity, and residue interaction network were analyzed.

RESULTS

Our results indicate that the missense mutations have diverse effects on TPMT structure and function. Stability and aggregation propensities are affected by various mutations. Several mutations also affect residues in ligand binding site.

CONCLUSIONS

In vitro study of missense mutation is laborious and time-consuming. However, computational methods can be used to obtain information about effects of missense mutations on protein structure. In this study, the effects of most of the mutations on enzyme activity could be explained by computational methods. Thus, the present approach can be used for understanding the protein structure-function relationships.

A comparative study of polyethylene glycol hydrogels derivatized with the RGD peptide and the cell-binding domain of fibronectin

The goal of our study was to compare the biological responses of cells cultured on polyethylene glycol (PEG) hydrogels functionalized with varying concentrations of the widely used adhesion peptide, RGD, and the cell-binding domain of fibronectin, III(9-10). We used Michael addition chemistry to covalently link cysteines in GRGDSPC and glutathione S-transferase (GST) tagged III(9-10) (GST-III(9-10)), to the acrylate groups in PEG diacrylate (PEGDA). Conjugation of GST-III(9-10) to PEGDA occurred through cysteine residues in GST. Ellman's reagent and immunoblotting studies demonstrated an efficiency of 90% or more for PEG conjugation of 1 μM GST-III(9-10) or GRDGSPC in 10% (wt/vol) PEGDA at 37°C for 1 h. Circular dichroism and limited proteolysis demonstrated that conjugating PEGDA to GST-III(9-10) did not significantly perturb its native secondary structure. Sodium dodecyl sulfate polyacrylamide gel electrophoresis characterization of the wash solution of PEG hydrogels after photopolymerization demonstrated that >95% of the 1 μM GST-III(9-10) was incorporated into the PEG hydrogels after cross-linking. PEG hydrogels derivatized with 1 μM GST-III(9-10) had significantly higher cell adhesion and spreading than PEG hydrogels with 1 μM GRGDSPC. A comparable adhesion response between GRGDSPC and GST-III(9-10) was obtained when the former was at millimolar and the latter at micromolar concentration. The amount and type of conjugate in the PEG hydrogel derivative was statistically more significant than hydrogel rigidity in stimulating the biological responses observed. This report presents new evidence of the robustness of III(9-10) in mediating cell adhesion and spreading on PEG hydrogels.

MTMDAT-HADDOCK: high-throughput, protein complex structure modeling based on limited proteolysis and mass spectrometry

BACKGROUND

MTMDAT is a program designed to facilitate analysis of mass spectrometry data of proteins and biomolecular complexes that are probed structurally by limited proteolysis. This approach can provide information about stable fragments of multidomain proteins, yield tertiary and quaternary structure data, and help determine the origin of stability changes at the amino acid residue level. Here, we introduce a pipeline between MTMDAT and HADDOCK, that facilitates protein-protein complex structure probing in a high-throughput and highly automated fashion.

RESULTS

A new feature of MTMDAT allows for the direct identification of residues that are involved in complex formation by comparing the mass spectra of bound and unbound proteins after proteolysis. If 3D structures of the unbound components are available, this data can be used to define restraints for data-driven docking to calculate a model of the complex. We describe here a new implementation of MTMDAT, which includes a pipeline to the data-driven docking program HADDOCK, thus streamlining the entire procedure. This addition, together with usability improvements in MTMDAT, enables high-throughput modeling of protein complexes from mass spectrometry data. The algorithm has been validated by using the protein-protein interaction between the ubiquitin-binding domain of proteasome component Rpn13 and ubiquitin. The resulting structural model, based on restraints extracted by MTMDAT from limited proteolysis and modeled by HADDOCK, was compared to the published NMR structure, which relied on twelve unambiguous intermolecular NOE interactions. The MTMDAT-HADDOCK structure was of similar quality to structures generated using only chemical shift perturbation data derived by NMR titration experiments.

CONCLUSIONS

The new MTMDAT-HADDOCK pipeline enables direct high-throughput modeling of protein complexes from mass spectrometry data. MTMDAT-HADDOCK can be downloaded from http://www.ifm.liu.se/chemistry/molbiotech/maria_sunnerhagens_group/mtmdat/together with the manual and example files. The program is free for academic/non-commercial purposes.