POOL server: machine learning application for functional site prediction in proteins.
CONCLUSIONS
We present an automated web server for partial order optimum likelihood (POOL), a machine learning application that combines computed electrostatic and geometric information for high-performance prediction of catalytic residues from 3D structures. Input features consist of THEMATICS electrostatics data and pocket information from ConCavity. THEMATICS measures deviation from typical, sigmoidal titration behavior to identify functionally important residues and ConCavity identifies binding pockets by analyzing the surface geometry of protein structures. Both THEMATICS and ConCavity (structure only) do not require the query protein to have any sequence or structure similarity to other proteins. Hence, POOL is applicable to proteins with novel folds and engineered proteins. As an additional option for cases where sequence homologues are available, users can include evolutionary information from INTREPID for enhanced accuracy in site prediction.


BACKGROUND
The web site is free and open to all users with no login requirements at http://www.pool.neu.edu.


BACKGROUND
m.ondrechen@neu.edu


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.
Srinivas
S
Somarowthu
Mary Jo
MJ
Ondrechen
D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D020407:Internet; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D012984:Software; D055672:Static Electricity
D011506:Proteins