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Identifying protein construct variants with increased crystallization propensity--a case study.

ABSTRACT: This study describes an efficient multiparallel automated workflow of cloning, expression, purification, and crystallization of a large set of construct variants for isolated protein domains aimed at structure determination by X-ray crystallography. This methodology is applied to MAPKAP kinase 2, a key enzyme in the inflammation pathway and thus an attractive drug target. The study reveals a distinct subset of truncation variants with improved crystallization properties. These constructs distinguish themselves by increased solubility and stability during a parallel automated multistep purification process including removal of the recombinant tag. High-throughput protein melting point analysis characterizes this subset of constructs as particularly thermostable. Both parallel purification screening and melting point determination clearly identify residue 364 as the optimal C terminus for the kinase domain. Moreover, all three constructs that ultimately crystallized feature this C terminus. At the N terminus, only three amino acids differentiate a noncrystallizing from a crystallizing construct. This study addresses the very common issues associated with difficult to crystallize proteins, those of solubility and stability, and the crucial importance of particular residues in the formation of crystal contacts. A methodology is suggested that includes biophysical measurements to efficiently identify and produce construct variants of isolated protein domains which exhibit higher crystallization propensity.

SUBMITTER: Malawski GA

PROVIDER: S-EPMC2242438 | biostudies-literature | 2006 Dec

REPOSITORIES: biostudies-literature

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Identifying protein construct variants with increased crystallization propensity--a case study.

Malawski Guido A GA Hillig Roman C RC Monteclaro Felipe F Eberspaecher Uwe U Schmitz Arndt A P AA Crusius Kerstin K Huber Martina M Egner Ursula U Donner Peter P Müller-Tiemann Beate B

Protein science : a publication of the Protein Society 20061201 12

This study describes an efficient multiparallel automated workflow of cloning, expression, purification, and crystallization of a large set of construct variants for isolated protein domains aimed at structure determination by X-ray crystallography. This methodology is applied to MAPKAP kinase 2, a key enzyme in the inflammation pathway and thus an attractive drug target. The study reveals a distinct subset of truncation variants with improved crystallization properties. These constructs disting ...[more]

PMID: 17132859

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Project description:MotivationX-ray crystallography-based protein structure determination, which accounts for majority of solved structures, is characterized by relatively low success rates. One solution is to build tools which support selection of targets that are more likely to crystallize. Several in silico methods that predict propensity of diffraction-quality crystallization from protein chains were developed. We show that the quality of their predictions drops when applied to more recent crystallization trails, which calls for new solutions. We propose a novel approach that alleviates drawbacks of the existing methods by using a recent dataset and improved protocol to annotate progress along the crystallization process, by predicting the success of the entire process and steps which result in the failed attempts, and by utilizing a compact and comprehensive set of sequence-derived inputs to generate accurate predictions.ResultsThe proposed PPCpred (predictor of protein Production, Purification and Crystallization) predict propensity for production of diffraction-quality crystals, production of crystals, purification and production of the protein material. PPCpred utilizes comprehensive set of inputs based on energy and hydrophobicity indices, composition of certain amino acid types, predicted disorder, secondary structure and solvent accessibility, and content of certain buried and exposed residues. Our method significantly outperforms alignment-based predictions and several modern crystallization propensity predictors. Receiver operating characteristic (ROC) curves show that PPCpred is particularly useful for users who desire high true positive (TP) rates, i.e. low rate of mispredictions for solvable chains. Our model reveals several intuitive factors that influence the success of individual steps and the entire crystallization process, including the content of Cys, buried His and Ser, hydrophobic/hydrophilic segments and the number of predicted disordered segments.Availabilityhttp://biomine.ece.ualberta.ca/PPCpred/.Contactlkurgan@ece.ualberta.ca.

Dataset Information

Identifying protein construct variants with increased crystallization propensity--a case study.

Publications

Identifying protein construct variants with increased crystallization propensity--a case study.

Similar Datasets

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