Computational Structural Biology Lab

Source: http://www.csb.iitkgp.ac.in/applications/HotSPRing/docs/references.php Parent: http://www.csb.iitkgp.ac.in/applications/HotSPRing/main.php

Department of Bioscience and Biotechnology Indian Institute of Technology Kharagpur

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• Adams, P.D., Afonine, P.V., Bunkoczi, G., Chen, V.B., Davis, I.W., Echols, N., Headd, J.J., Hung, L.-W., Kapral, G.J., Grosse-Kunstleve, R.W. et al. (2010) PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallographica Section D, 66, 213-221.

• Barik, A., C, N., Pilla, S.P. and Bahadur, R.P. (2015) Molecular architecture of protein-RNA recognition sites. J. Biomol. Struct. Dyn., 1-14.
• Bahadur, R.P., Zacharias, M. and Janin, J. (2008) Dissecting protein-RNA recognition sites. Nucleic Acids Res., 36, 2705-2716.
• Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32.
• Hubbard SJ (1992). NACCESS: A program for calculating accessibilities. Department of Biochemistry and Molecular Biology. University College of London.
• Larkin, M.A., Blackshields, G., Brown, N.P., Chenna, R., McGettigan, P.A., McWilliam, H., Valentin, F., Wallace, I.M., Wilm, A., Lopez, R. et al. (2007) Clustal W and Clustal X version 2.0. Bioinformatics, 23, 2947-2948.
• McDonald IK and Thornton JM (1994). Satisfying hydrogen bonding potential in proteins. J. Mol. Biol. 238, 777-793.
• Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R. and Dubourg, V. (2011) Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825-2830.
• Schneider, R. and Sander, C. (1996) The HSSP database of protein structure-sequence alignments. Nucleic Acids Res., 24, 201-205.
• Shannon,C. (1948) A mathematical theory of communication. Bell Syst. Tech. J., 27, 379--423.