THEMATICS explained

Theoretical Microscopic Anomalous Titration Curve Shapes (THEMATICS) is a computational method for predicting the biochemically active amino acids in a protein three-dimensional structure.^{[1] [2] [3]}

The method was developed by Mary Jo Ondrechen, James Clifton, and Dagmar Ringe.^[4] It is based on computed electrostatic and chemical properties of the individual amino acids in a protein structure. Specifically it identifies anomalous shapes in the theoretical titration curves of the ionizable amino acids. Biochemically active amino acids tend to have wide buffer ranges and non-sigmoidal titration patterns.

While the method predicts biochemically active amino acids successfully, it also provides input features to a machine learning predictor, Partial Order Optimum Likelihood (POOL).^{[5] [6]}

Notes and References

1. https://www.sciencedaily.com/releases/2001/10/011030225809.htm Protein Function Predicted With New "THEMATICS" Method Developed By Northeastern University & Brandeis Scientists.
2. Borman, S., From sequence to consequence. Chemical and Engineering News, 79(48): p. 31-33 (2001).
3. Ball, P., Computers spot shape clues. Nature, (2001).
4. “THEMATICS: A Simple Computational Predictor of Enzyme Function from Structure,” M.J. Ondrechen, J.G. Clifton & D. Ringe, Proc. Natl. Acad. Sci. USA 98, 12473-12478 (2001).
5. “Partial Order Optimum Likelihood (POOL): Maximum Likelihood Prediction of Active Site Residues Using 3D Structure and Sequence Properties,” W. Tong, Y. Wei, L.F. Murga, M.J. Ondrechen, and R.J. Williams, PLoS Computational Biology, 5(1): e1000266 (2009).
6. Somarowthu. Srinivas. Yang. Huyuan. Hildebrand. David G. C.. Ondrechen. Mary Jo. 2011-06-01. High-performance prediction of functional residues in proteins with machine learning and computed input features. Biopolymers. 95. 6. 390–400. 10.1002/bip.21589. 0006-3525. 21254002.