Journal article
Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features
YH Zhu, J Hu, F Ge, F Li, J Song, Y Zhang, DJ Yu
Briefings in Bioinformatics | OXFORD UNIV PRESS | Published : 2021
DOI: 10.1093/bib/bbaa076
Abstract
X-ray crystallography is the major approach for determining atomic-level protein structures. Because not all proteins can be easily crystallized, accurate prediction of protein crystallization propensity provides critical help in guiding experimental design and improving the success rate of X-ray crystallography experiments. This study has developed a new machine-learning-based pipeline that uses a newly developed deep-cascade forest (DCF) model with multiple types of sequence-based features to predict protein crystallization propensity. Based on the developed pipeline, two new protein crystallization propensity predictors, denoted as DCFCrystal and MDCFCrystal, have been implemented. DCFCry..
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Grants
Awarded by Australian Research Council
Funding Acknowledgements
This work was supported by the National Natural Science Foundation of China (61772273 and 61902352), the Fundamental Research Funds for the Central Universities (No. 30918011104), the National Institute of General Medical Sciences (GM136422), the National Institute of Allergy and Infectious Diseases (AI134678), the National Science Foundation (IIS1901191), China Scholarship Council (No. 201906840041), the National Health and Medical Research Council of Australia (NHMRC) (1092262), the Australian Research Council (ARC) (LP110200333 and DP120104460) and the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (R01 AI111965).