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Elbasir, A., Mall, R., Kunji, K., Rawi, R., Islam, Z., Chuang, G.Y., Kolatkar, P.R. and Bensmail, H. (2019) BCrystal: An Interpretable Sequence-Based Protein Crystallization Predictor. Bioinformatics, btz762.
https://doi.org/10.1093/bioinformatics/btz762

has been cited by the following article:

• TITLE: Prediction of Crystallization Propensity of Proteins from Bacillus haloduran Using Various Amino Acid and Protein Features

  AUTHORS: Shaomin Yan, Guang Wu

  KEYWORDS: Protein Feature, Bacillus haloduran, Protein Crystallization

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.12 No.12, December 10, 2019

  ABSTRACT: Correct prediction of propensity of crystallization of proteins is important for cost- and time-saving in determination of 3-demensional structures because one can focus to crystallize the proteins whose propensity is high through predictions instead of choosing proteins randomly. However, so far this job has yet to accomplish although huge efforts have been made over years, because it is still extremely hard to find an intrinsic feature in a protein to directly relate to the propensity of crystallization of the given protein. Despite of this difficulty, efforts are never stopped in testing of known features in amino acids and proteins versus the propensity of crystallization of proteins from various sources. In this study, the comparison of the features, which were developed by us, with the features from well-known resource for the prediction of propensity of crystallization of proteins from Bacillus haloduran was conducted. In particular, the propensity of crystallization of proteins is considered as a yes-no event, so 185 crystallized proteins and 270 uncrystallized proteins from B. haloduran were classified as yes-no events. Each of 540 amino-acid features including the features developed by us was coupled with these yes-no events using logistic regression and neural network. The results once again demonstrated that the predictions using the features developed by us are relatively better than the predictions using any of 540 amino-acid features.