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Can the propensity of protein crystallization be increased by using systematic screening with metals?

ABSTRACT: Protein crystallization is one of the major bottlenecks in protein structure elucidation with new strategies being constantly developed to improve the chances of crystallization. Generally, well-ordered epitopes possessing complementary surface and capable of producing stable inter-protein interactions generate a regular three-dimensional arrangement of protein molecules which eventually results in a crystal lattice. Metals, when used for crystallization, with their various coordination numbers and geometries, can generate such epitopes mediating protein oligomerization and/or establish crystal contacts. Some examples of metal-mediated oligomerization and crystallization together with our experience on metal-mediated crystallization of a putative rRNA methyltransferase from Sinorhizobium meliloti are presented. Analysis of crystal structures from protein data bank (PDB) using a non-redundant data set with a 90% identity cutoff, reveals that around 67% of proteins contain at least one metal ion, with ?14% containing combination of metal ions. Interestingly, metal containing conditions in most commercially available and popular crystallization kits generally contain only a single metal ion, with combinations of metals only in a very few conditions. Based on the results presented in this review, it appears that the crystallization screens need expansion with systematic screening of metal ions that could be crucial for stabilizing the protein structure or for establishing crystal contact and thereby aiding protein crystallization.

SUBMITTER: Hegde RP

PROVIDER: S-EPMC5563152 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Can the propensity of protein crystallization be increased by using systematic screening with metals?

Hegde Raghurama P RP Pavithra Gowribidanur C GC Dey Debayan D Almo Steven C SC Ramakumar S S Ramagopal Udupi A UA

Protein science : a publication of the Protein Society 20170629 9

Protein crystallization is one of the major bottlenecks in protein structure elucidation with new strategies being constantly developed to improve the chances of crystallization. Generally, well-ordered epitopes possessing complementary surface and capable of producing stable inter-protein interactions generate a regular three-dimensional arrangement of protein molecules which eventually results in a crystal lattice. Metals, when used for crystallization, with their various coordination numbers ...[more]

PMID: 28643473

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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.

Project description:X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed 'PredPPCrys' using the support vector machine (SVM). Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I). Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II), which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization targets of currently non-crystallizable proteins were provided as compendium data, which are anticipated to facilitate target selection and design for the worldwide structural genomics consortium. PredPPCrys is freely available at http://www.structbioinfor.org/PredPPCrys.

Project description:Existing methods for predicting protein crystallization obtain high accuracy using various types of complemented features and complex ensemble classifiers, such as support vector machine (SVM) and Random Forest classifiers. It is desirable to develop a simple and easily interpretable prediction method with informative sequence features to provide insights into protein crystallization. This study proposes an ensemble method, SCMCRYS, to predict protein crystallization, for which each classifier is built by using a scoring card method (SCM) with estimating propensity scores of p-collocated amino acid (AA) pairs (p=0 for a dipeptide). The SCM classifier determines the crystallization of a sequence according to a weighted-sum score. The weights are the composition of the p-collocated AA pairs, and the propensity scores of these AA pairs are estimated using a statistic with optimization approach. SCMCRYS predicts the crystallization using a simple voting method from a number of SCM classifiers. The experimental results show that the single SCM classifier utilizing dipeptide composition with accuracy of 73.90% is comparable to the best previously-developed SVM-based classifier, SVM_POLY (74.6%), and our proposed SVM-based classifier utilizing the same dipeptide composition (77.55%). The SCMCRYS method with accuracy of 76.1% is comparable to the state-of-the-art ensemble methods PPCpred (76.8%) and RFCRYS (80.0%), which used the SVM and Random Forest classifiers, respectively. This study also investigates mutagenesis analysis based on SCM and the result reveals the hypothesis that the mutagenesis of surface residues Ala and Cys has large and small probabilities of enhancing protein crystallizability considering the estimated scores of crystallizability and solubility, melting point, molecular weight and conformational entropy of amino acids in a generalized condition. The propensity scores of amino acids and dipeptides for estimating the protein crystallizability can aid biologists in designing mutation of surface residues to enhance protein crystallizability. The source code of SCMCRYS is available at http://iclab.life.nctu.edu.tw/SCMCRYS/.

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Can the propensity of protein crystallization be increased by using systematic screening with metals?

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Can the propensity of protein crystallization be increased by using systematic screening with metals?

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