Biomedical subjects

Jens Erik Nielsen

Publications and source records attributed to Jens Erik Nielsen.

7 recordsLinked to original sources

PubMed2006

Redesigning protein pKa values.

The ability to re-engineer enzymatic pH-activity profiles is of importance for industrial applications of enzymes. We theoretically explore the feasibility of re-engineering enzymatic pH-activity profiles by changing active site pK(a) values using point mutations. We calculate the maximum achievable DeltapK(a) values for 141 target titratable groups in seven enzymes by introducing conservative net-charge altering point mutations. We examine the importance of the number of mutations introduced, their distance from the target titratable group, and the characteristics of the target group itself. The results show that multiple mutations at 10A can change pK(a) values up to two units, but that the introduction of a requirement to keep other pK(a) values constant reduces the magnitude of the achievable DeltapK(a). The algorithm presented shows a good correlation with existing experimental data and is available for download and via a web server at http://enzyme.ucd.ie/pKD.

Algorithms↗

PubMed2006

pKD: re-designing protein pKa values.

The pK(a) values in proteins govern the pH-dependence of protein stability and enzymatic activity. A large number of mutagenesis experiments have been carried out in the last three decades to re-engineer the pH-activity and pH-stability profile of enzymes and proteins. We have developed the pKD webserver (http://polymerase.ucd.ie/pKa_Design), which predicts sets of point mutations that will change the pK(a) values of a set of target residues in a given direction, thus allowing for targeted re-design of the pH-dependent characteristics of proteins. The server provides the user with an interactive experience for re-designing pK(a) values by pre-calculating DeltapK(a) values from all feasible point mutations. Design solutions are found in less than 10 min for a typical design job for a medium-sized protein. Mutant DeltapK(a) values calculated by the pKD web server are in close agreement with those produced by comparing results from full-fledged pK(a) calculation methods.

Computer Graphics↗

PubMed2006

Analysing the pH-dependent properties of proteins using pKa calculations.

The results of protein pKa calculations are routinely being analysed to understand the pH-dependence of protein characteristics such as stability and catalysis. Systems of functionally important titratable groups are identified from protein from pKa calculations, but the rationalisation of the behaviour of such systems is inherently problematic due to a lack of theoretical tools and methods. I present a number of novel methods for analysing the results of protein pKa calculations which have been embedded in a graphical user interface (pKaTool). In the present paper I present novel methods for assessing the reliability of protein pKa calculations and for analysing the roles of individual residues in determining active site pKa values and the pH-dependence of protein stability. The methods presented are freely available to academic researchers at http://enzyme.ucd.ie/Science/pKa/pKaTool .

Animals↗

PubMed2005

Impact of finasteride treatment on perioperative bleeding before transurethral resection of the prostate: a prospective randomized study.

In this preliminary, randomized study, 35 men were pretreated for 3 months with finasteride or placebo before transurethral resection of the prostate. The study was inconclusive because it did not show any benefit in terms of reducing perioperative bleeding during or after the resection but there is a need for a large, prospective, randomized study.

Aged↗

PubMed2003

On the evaluation and optimization of protein X-ray structures for pKa calculations.

The calculation of the physical properties of a protein from its X-ray structure is of importance in virtually every aspect of modern biology. Although computational algorithms have been developed for calculating everything from the dynamics of a protein to its binding specificity, only limited information is available on the ability of these methods to give accurate results when used with a particular X-ray structure. We examine the ability of a pKa calculation algorithm to predict the proton-donating residue in the catalytic mechanism of hen egg white lysozyme. We examine the correlation between the ability of the pKa calculation method to obtain the correct result and the overall characteristics of 41 X-ray structures such as crystallization conditions, resolution, and the output of structure validation software. We furthermore examine the ability of energy minimizations (EM), molecular dynamics (MD) simulations, and structure-perturbation methods to optimize the X-ray structures such that these give correct results with the pKa calculation algorithm. We propose a set of criteria for identifying the proton donor in a catalytic mechanism, and demonstrate that the application of these criteria give highly accurate prediction results when using unmodified X-ray structures. More specifically, we are able to successfully identify the proton donor in 85% of the X-ray structures when excluding structures with crystal contacts near the active site. Neither the use of the overall characteristics of the X-ray structures nor the optimization of the structure by EM, MD, or other methods improves the results of the pKa calculation algorithm. We discuss these results and their implications for the design of structure-based energy calculation algorithms in general.

Algorithms↗

PubMed2003

Calculating pKa values in enzyme active sites.

The ionization properties of the active-site residues in enzymes are of considerable interest in the study of the catalytic mechanisms of enzymes. Knowledge of these ionization constants (pKa values) often allows the researcher to identify the proton donor and the catalytic nucleophile in the reaction mechanism of the enzyme. Estimates of protein residue pKa values can be obtained by applying pKa calculation algorithms to protein X-ray structures. We show that pKa values accurate enough for identifying the proton donor in an enzyme active site can be calculated by considering in detail only the active-site residues and their immediate electrostatic interaction partners, thus allowing for a large decrease in calculation time. More specifically we omit the calculation of site-site interaction energies, and the calculation of desolvation and background interaction energies for a large number of pairs of titratable groups. The method presented here is well suited to be applied on a genomic scale, and can be implemented in most pKa calculation algorithms to give significant reductions in calculation time with little or no impact on the accuracy of the results. The work presented here has implications for the understanding of enzymes in general and for the design of novel biocatalysts.

Algorithms↗

PubMed2002

Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations.

We have developed a computer algorithm, FOLDEF (for FOLD-X energy function), to provide a fast and quantitative estimation of the importance of the interactions contributing to the stability of proteins and protein complexes. The predictive power of FOLDEF was tested on a very large set of point mutants (1088 mutants) spanning most of the structural environments found in proteins. FOLDEF uses a full atomic description of the structure of the proteins. The different energy terms taken into account in FOLDEF have been weighted using empirical data obtained from protein engineering experiments. First, we considered a training database of 339 mutants in nine different proteins and optimised the set of parameters and weighting factors that best accounted for the changes in stability of the mutants. The predictive power of the method was then tested using a blind test mutant database of 667 mutants, as well as a database of 82 protein-protein complex mutants. The global correlation obtained for 95 % of the entire mutant database (1030 mutants) is 0.83 with a standard deviation of 0.81 kcal mol(-1) and a slope of 0.76. The present energy function uses a minimum of computational resources and can therefore easily be used in protein design algorithms, and in the field of protein structure and folding pathways prediction where one requires a fast and accurate energy function. FOLDEF is available via a web-interface at http://fold-x.embl-heidelberg.de

Algorithms↗