CRYSTALP2: sequence-based protein crystallization propensity prediction

January 2009
BMC Structural Biology;2009, Vol. 9, p50
Academic Journal
No abstract available.


Related Articles

  • SVMCRYS: An SVM Approach for the Prediction of Protein Crystallization Propensity from Protein Sequence. Kandaswamy, Krishna Kumar; Pugalenthi, Ganesan; Suganthan, P. N.; Gangal, Rajeev // Protein & Peptide Letters;Apr2010, Vol. 17 Issue 4, p423 

    X-ray crystallography is the most widely used method for protein 3-dimensional structure determination. Selection of target protein that can yield high quality crystal for X-ray crystallography is a challenging task. Prediction of protein crystallization propensity from sequence information is...

  • Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis. Kurgan, L.; Mizianty, M. J. // Natural Science;Sep2009, Vol. 1 Issue 2, p93 

    Structural genomics (SG) is an international effort that aims at solving three-dimensional shapes of important biological macro-molecules with primary focus on proteins. One of the main bottlenecks in SG is the ability to produce dif-fraction quality crystals for X-ray crystallography based...

  • ParCrys: a Parzen window density estimation approach to protein crystallization propensity prediction. Ian M. Overton; Gianandrea Padovani; Mark A. Girolami; Geoffrey J. Barton // Bioinformatics;Apr2008, Vol. 24 Issue 7, p901 

    The ability to rank proteins by their likely success in crystallization is useful in current Structural Biology efforts and in particular in high-throughput Structural Genomics initiatives. We present ParCrys, a Parzen Window approach to estimate a proteins propensity to produce...

  • SOLpro: accurate sequence-based prediction of protein solubility. Magnan, Christophe N.; Randall, Arlo; Baldi, Pierre // Bioinformatics;Sep2009, Vol. 25 Issue 17, p2200 

    Motivation: Protein insolubility is a major obstacle for many experimental studies. A sequence-based prediction method able to accurately predict the propensity of a protein to be soluble on overexpression could be used, for instance, to prioritize targets in large-scale proteomics projects and...

  • Predicting Crystallization Propensity of Proteins from Arabidopsis Thaliana. Shaomin Yan; Guang Wu // Biological Procedures Online;11/23/2015, Vol. 17, p1 

    Background: Many studies have correlated characteristics of amino acids with crystallization propensity, as part of the effort to determine the factors that affect the propensity of protein crystallization. However, these characteristics are constant; that is, the encoded amino acid sequences...

  • Understanding the physical properties that control protein crystallization by analysis of large-scale experimental data. Price II, W Nicholson; Chen, Yang; Handelman, Samuel K; Neely, Helen; Manor, Philip; Karlin, Richard; Nair, Rajesh; Liu, Jinfeng; Baran, Michael; Everett, John; Tong, Saichiu N; Forouhar, Farhad; Swaminathan, Swarup S; Acton, Thomas; Xiao, Rong; Luft, Joseph R; Lauricella, Angela; DeTitta, George T; Rost, Burkhard; Montelione, Gaetano T // Nature Biotechnology;Jan2009, Vol. 27 Issue 1, p51 

    Crystallization is the most serious bottleneck in high-throughput protein-structure determination by diffraction methods. We have used data mining of the large-scale experimental results of the Northeast Structural Genomics Consortium and experimental folding studies to characterize the...

  • Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Fernandez-Escamilla, Ana-Maria; Rousseau, Frederic; Schymkowitz, Joost; Serrano, Luis // Nature Biotechnology;Oct2004, Vol. 22 Issue 10, p1302 

    We have developed a statistical mechanics algorithm, TANGO, to predict protein aggregation. TANGO is based on the physico-chemical principles ofß-sheet formation, extended by the assumption that the core regions of an aggregate are fully buried. Our algorithm accurately predicts the...

  • Combining sequence-based prediction methods and circular dichroism and infrared spectroscopic data to improve protein secondary structure determinations. Lees, Jonathan G.; Janes, Robert W. // BMC Bioinformatics;2008, Vol. 9, Special section p1 

    Background: A number of sequence-based methods exist for protein secondary structure prediction. Protein secondary structures can also be determined experimentally from circular dichroism, and infrared spectroscopic data using empirical analysis methods. It has been proposed that comparable...

  • Sequence-based prediction of protein crystallization, purification and production propensity. Mizianty, Marcin J.; Kurgan, Lukasz // Bioinformatics;Jul2011, Vol. 27 Issue 13, pi24 

    Motivation: X-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...

  • SCMCRYS: Predicting Protein Crystallization Using an Ensemble Scoring Card Method with Estimating Propensity Scores of P-Collocated Amino Acid Pairs. Charoenkwan, Phasit; Shoombuatong, Watshara; Lee, Hua-Chin; Chaijaruwanich, Jeerayut; Huang, Hui-Ling; Ho, Shinn-Ying // PLoS ONE;Sep2013, Vol. 8 Issue 9, p1 

    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...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics