Natural Science

Natural Science

ISSN Print: 2150-4091
ISSN Online: 2150-4105
www.scirp.org/journal/ns
E-mail: ns@scirp.org
"Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis"
written by Lukasz Kurgan, Marcin J. Mizianty,
published by Natural Science, Vol.1 No.2, 2009
has been cited by the following article(s):
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[1] Tlcrys: Transfer learning based method for protein crystallization prediction
International Journal of Molecular …, 2022
[2] ATTCry: Attention-based neural network model for protein crystallization prediction
Neurocomputing, 2021
[3] XRRpred: accurate predictor of crystal structure quality from protein sequence
Bioinformatics, 2021
[4] Machine Learning Approaches to Predict Protein Crystallization Propensities
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[5] CLPred: a sequence-based protein crystallization predictor using BLSTM neural network
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[6] DHS-Crystallize: Deep-Hybrid-Sequence based method for predicting protein Crystallization
2020
[7] CLPred: A sequence-based protein crystallization pre-dictor using BLSTM neural network
2020
[8] Correlation of Combined Characters of Amino Acid and Whole Protein with Success Rate of Crystallization of Lactobacillus Proteins
2019
[9] BCrystal: An Interpretable Sequence-Based Protein Crystallization Predictor
2019
[10] Correlating Combined Features of Amino Acid and Protein with Crystallization Propensity of Proteins from Mycobacterium tuberculosis
2019
[11] Prediction of Crystallization Propensity of Proteins from Bacillus haloduran Using Various Amino Acid and Protein Features
2019
[12] DeepCrystal: a deep learning framework for sequence-based protein crystallization prediction
2018
[13] Identification and characterization of sodium and chloride-dependent gamma-aminobutyric acid (GABA) transporters from eukaryotic pathogens as a potential …
Bioinformation, 2018
[14] Taxonomic Landscape of the Dark Proteomes: Whole‐Proteome Scale Interplay Between Structural Darkness, Intrinsic Disorder, and Crystallization Propensity
Proteomics, 2018
[15] Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity
Briefings in Bioinformatics, 2017
[16] fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization
2017
[17] Caracterización molecular de los factores involucrados en la regulación de la biosíntesis de ácidos micólicos en Mycobacterium tuberculosis
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[18] Purification Propensity for Proteins from Bacillus halodurans. Enz Eng 5: 151. doi: 10.4172/2329-6674.1000151 Page 2 of 6 Volume 5• Issue 3• 1000151 Enz …
2016
[19] Purification Propensity for Proteins from Bacillus halodurans. Enz Eng 5: 151. doi: 10.4172/2329-6674.1000151 Page 2 of 6 Volume 5• Issue 3• 1000151 …
2016
[20] Cribado de las condiciones de cristalización de la Ubiquitina in silico y en el laboratorio
2016
[21] Purification Propensity for Proteins from Bacillus halodurans. Enz Eng 5: 151. doi: 10.4172/2329-6674.1000151 Page 2 of 6 Volume 5• Issue 3• 1000151 Enz …
2016
[22] Predicting Crystallization Propensity of Proteins from Arabidopsis Thaliana
Biological procedures online, 2015
[23] Statistical Analysis of Crystallization Database Links Protein Physico-Chemical Features with Crystallization Mechanisms
PloS one, 2014
[24] Protein Crystallization: Soft Matter and Chemical Physics Perspectives
2014
[25] Statistical analysis of crystallization database links protein physicochemical features with crystallization mechanisms
arXiv preprint arXiv, 2013
[26] Association of combined features of amino acid and protein withcrystallization propensity of proteins from Cytophaga Hutchinsoni
Zeitschrift für Kristallographie-Crystalline Materials, 2013
[27] 氨基酸和蛋白质的组合特征与秀丽隐杆线虫蛋白质的结晶倾向的相关分析
广西科学, 2013
[28] Computational support systems for prediction and characterization of protein crystallization outcomes
2013
[29] CRYSpred: accurate sequence-based protein crystallization propensity prediction using sequence-derived structural characteristic
Protein and peptide letters, 2012
[30] Correlating dynamic amino acid properties with success rate of crystallization of proteins from Bacteroides vulgatus
Crystal Research and Technology, 2012
[31] Predicting protein crystallizability and nucleation
Protein and peptide letters , 2012
[32] Sequence-based prediction of protein crystallization, purification and production propensity
Bioinformatics, 2011
[33] ifc2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content
Amino acids, 2011
[34] Structural protein descriptors in 1-dimension and their sequence-based predictions
Current Protein and Peptide Science, 2011
[35] iFC 2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content
2011
[36] Meta prediction of protein crystallization propensity
Biochemical and biophysical research communications , 2009
[37] De Novo Crystallization Condition Prediction with Deep Learning
Verge, M Mok, S Kang
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