Hui Li, Andrew D. Robertson and Jan H. Jensen developed a very fast
empirical method for protein pKa prediction and rationalization
(PROTEINS: Structure, Function and Bioinformatics 61:704-21, 2005). The
desolvation effects and intra-protein interactions, which cause variations
in pKa values of protein ionizable groups, are empirically
related to the positions and chemical nature of the groups proximate to
the pKa sites. The Authors developed a program (PROPKA) to
automatically predict pKa values based on these empirical
relationships within a couple of seconds. Unusual pKa values at
buried active sites, which are among the most interesting protein pKa
values, are predicted very well with the empirical method.
2. How it works
The user send the molecule to the server that automatically converts it to PDB 2.2 thanks to VEGA. The output is passed to PROPKA and its output is converted to the HTML format and sent to the user.
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program; if not, write to the Free SoftwareFoundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA.
Jan H. Jensen
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