FoldRate: A Web-Server for Predicting Protein Folding Rates from Primary Sequence. With the avalanche of gene products in the postgenomic age, the gap between newly found protein sequences and the knowledge of their 3D (three dimensional) structures is becoming increasingly wide. It is highly desired to de- velop a method by which one can predict the folding rates of proteins based on their amino acid sequence information alone. To address this problem, an ensemble predictor, called FoldRate, was developed by fusing the folding-correlated features that can be either directly obtained or easily derived from the sequences of proteins. It was demonstrated by the jackknife cross-validation on a benchmark dataset constructed recently that FoldRate is at least comparable with or even better than the existing methods that, however, need both the sequence and 3D structure information for predicting the folding rate. As a user-friendly web-server, FoldRate is freely accessible to the public at, by which one can get the desired result for a query protein sequence in around 30 seconds.

References in zbMATH (referenced in 15 articles )

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  1. Marrero-Ponce, Yovani; Teran, Julio E.; Contreras-Torres, Ernesto; García-Jacas, César R.; Perez-Castillo, Yunierkis; Cubillan, Nestor; Peréz-Giménez, Facundo; Valdés-Martini, José R.: LEGO-based generalized set of two linear algebraic 3D bio-macro-molecular descriptors: theory and validation by QSARs (2020)
  2. Jia, Jianhua; Li, Xiaoyan; Qiu, Wangren; Xiao, Xuan; Chou, Kuo-Chen: iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (2019)
  3. Chiu, Jimmy Ka Ho; Dillon, Tharam S.; Chen, Yi-Ping Phoebe: Large-scale frequent stem pattern mining in RNA families (2018)
  4. Yang, Lei; Wang, Shiyuan; Zhou, Meng; Chen, Xiaowen; Zuo, Yongchun; Lv, Yingli: Characterization of BioPlex network by topological properties (2016)
  5. Qi, Zhao-Hui; Li, Ling; Zhang, Zhi-Meng; Qi, Xiao-Qin: Self-similarity analysis of eubacteria genome based on weighted graph (2011)
  6. Roterman, I.; Konieczny, L.; Jurkowski, W.; Prymula, K.; Banach, M.: Two-intermediate model to characterize the structure of fast-folding proteins (2011)
  7. Xie, Guosen; Mo, Zhongxi: Three 3D graphical representations of DNA primary sequences based on the classifications of DNA bases and their applications (2011)
  8. Zhou, Guo-Ping: The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism (2011)
  9. Huang, Wei; Zhang, Jianmin; Wang, Yurong; Huang, Dan: A simple method to analyze the similarity of biological sequences based on the fuzzy theory (2010)
  10. Nanni, Loris; Shi, Jian-Yu; Brahnam, Sheryl; Lumini, Alessandra: Protein classification using texture descriptors extracted from the protein backbone image (2010)
  11. Wu, Zhi-Cheng; Xiao, Xuan; Chou, Kuo-Chen: 2D-MH: a web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids (2010)
  12. Yang, Lianping; Zhang, Xiangde; Wang, Tianming: The Burrows-Wheeler similarity distribution between biological sequences based on Burrows-Wheeler transform (2010)
  13. Zhang, Ning; Duan, Guangyou; Gao, Shan; Ruan, Jishou; Zhang, Tao: Prediction of the parallel/antiparallel orientation of beta-strands using amino acid pairing preferences and support vector machines (2010)
  14. González-Díaz, Humberto; Pérez-Montoto, L. G.; Duardo-Sanchez, A.; Paniagua, E.; Vázquez-Prieto, S.; Vilas, R.; Dea-Ayuela, M. A.; Bolas-Fernández, F.; Munteanu, C. R.; Dorado, J.; Costas, J.; Ubeira, F. M.: Generalized lattice graphs for 2D-visualization of biological information (2009)
  15. Vilar, Santiago; González-Díaz, Humberto; Santana, Lourdes; Uriarte, Eugenio: A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer (2009)