THEMATICS

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]

References

^ Protein Function Predicted With New "THEMATICS" Method Developed By Northeastern University & Brandeis Scientists. ScienceDaily, (2001).
^ Borman, S., From sequence to consequence. Chemical and Engineering News, 79(48): p. 31-33 (2001).
^ Ball, P., Computers spot shape clues. Nature, (2001).
^ “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). PMID 11606719
^ “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). PMID 9148270
^ 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. doi:10.1002/bip.21589. ISSN 0006-3525. PMID 21254002.

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[one-1] Protein Function Predicted With New "THEMATICS" Method Developed By Northeastern University & Brandeis Scientists. ScienceDaily, (2001).

[two-2] Borman, S., From sequence to consequence. Chemical and Engineering News, 79(48): p. 31-33 (2001).

[three-3] Ball, P., Computers spot shape clues. Nature, (2001).

[four-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). PMID 11606719

[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). PMID 9148270

[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. doi:10.1002/bip.21589. ISSN 0006-3525. PMID 21254002.

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