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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[2] Bioinformatics-guided approaches to membrane protein structure determination as applied to ABC transporters
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[3] Tlcrys: Transfer learning based method for protein crystallization prediction
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[4] ATTCry: Attention-based neural network model for protein crystallization prediction
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[5] XRRpred: accurate predictor of crystal structure quality from protein sequence
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[6] Machine Learning Approaches to Predict Protein Crystallization Propensities
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[7] CLPred: a sequence-based protein crystallization predictor using BLSTM neural network
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[8] DHS-Crystallize: Deep-Hybrid-Sequence based method for predicting protein Crystallization
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[9] CLPred: A sequence-based protein crystallization pre-dictor using BLSTM neural network
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[10] Correlation of Combined Characters of Amino Acid and Whole Protein with Success Rate of Crystallization of Lactobacillus Proteins
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[11] BCrystal: An Interpretable Sequence-Based Protein Crystallization Predictor
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[12] Correlating Combined Features of Amino Acid and Protein with Crystallization Propensity of Proteins from Mycobacterium tuberculosis
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[13] Prediction of Crystallization Propensity of Proteins from Bacillus haloduran Using Various Amino Acid and Protein Features
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[14] De Novo Crystallization Condition Prediction with Deep Learning
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[15] DeepCrystal: a deep learning framework for sequence-based protein crystallization prediction
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[16] Identification and characterization of sodium and chloride-dependent gamma-aminobutyric acid (GABA) transporters from eukaryotic pathogens as a potential …
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[17] Taxonomic Landscape of the Dark Proteomes: Whole‐Proteome Scale Interplay Between Structural Darkness, Intrinsic Disorder, and Crystallization Propensity
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[18] Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity
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[19] fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization
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[20] Caracterización molecular de los factores involucrados en la regulación de la biosíntesis de ácidos micólicos en Mycobacterium tuberculosis
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[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 …
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[22] 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 …
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[23] Cribado de las condiciones de cristalización de la Ubiquitina in silico y en el laboratorio
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[24] 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 …
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[25] Predicting Crystallization Propensity of Proteins from Arabidopsis Thaliana
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[26] Statistical Analysis of Crystallization Database Links Protein Physico-Chemical Features with Crystallization Mechanisms
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[27] Protein Crystallization: Soft Matter and Chemical Physics Perspectives
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[28] Statistical analysis of crystallization database links protein physicochemical features with crystallization mechanisms
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[29] Association of combined features of amino acid and protein withcrystallization propensity of proteins from Cytophaga Hutchinsoni
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[30] 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
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[31] Computational support systems for prediction and characterization of protein crystallization outcomes
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[32] CRYSpred: accurate sequence-based protein crystallization propensity prediction using sequence-derived structural characteristic
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[33] Correlating dynamic amino acid properties with success rate of crystallization of proteins from Bacteroides vulgatus
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[34] Predicting protein crystallizability and nucleation
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[35] Sequence-based prediction of protein crystallization, purification and production propensity
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[36] ifc2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content
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[37] Structural protein descriptors in 1-dimension and their sequence-based predictions
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[38] iFC 2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content
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[39] Meta prediction of protein crystallization propensity
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