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]
Referencesedit
^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).
^“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.
thematics, this, article, multiple, issues, please, help, improve, discuss, these, issues, talk, page, learn, when, remove, these, template, messages, major, contributor, this, article, appears, have, close, connection, with, subject, require, cleanup, comply,. This article has multiple issues Please help improve it or discuss these issues on the talk page Learn how and when to remove these template messages A major contributor to this article appears to have a close connection with its subject It may require cleanup to comply with Wikipedia s content policies particularly neutral point of view Please discuss further on the talk page September 2020 Learn how and when to remove this message This article relies excessively on references to primary sources Please improve this article by adding secondary or tertiary sources Find sources THEMATICS news newspapers books scholar JSTOR September 2020 Learn how and when to remove this message Learn how and when to remove this message 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 edit Protein Function Predicted With New THEMATICS Method Developed By Northeastern University amp 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 amp 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 nbsp This computational chemistry related article is a stub You can help Wikipedia by expanding it vte Retrieved from https en wikipedia org w index php title THEMATICS amp oldid 1165864123, wikipedia, wiki, book, books, library,
