It is well known that a linear combination of forecasts can outperform individual forecasts. The common practice, however, is to obtain a weighted average of forecasts, with the weights adding up to unity. This paper considers three alternative approaches to obtaining linear combinations. It is show
Improved pKa prediction: Combining empirical and semimicroscopic methods
β Scribed by Gernot Kieseritzky; E. W. Knapp
- Publisher
- John Wiley and Sons
- Year
- 2008
- Tongue
- English
- Weight
- 252 KB
- Volume
- 29
- Category
- Article
- ISSN
- 0192-8651
No coin nor oath required. For personal study only.
β¦ Synopsis
Abstract
Using three different methods we tried to compute 171 experimentally known p__K__~a~ values of ionizable residues from 15 different proteins and compared the accuracies of computed p__K__~a~ values in terms of the root mean square deviation (RMSD) from experiment. One method is based on a continuum electrostatic model of the protein including conformational flexibility (KBPLUS). The others are empirical approaches with PROPKA deploying physically motivated energy terms with adjustable parameters and PKAcal using an empirical function with no physical basis. PROPKA reproduced the p__K__~a~ values with highest overall accuracy. Differentiating the data set into weakly and strongly shifted experimental p__K__~a~ values, however, we found that PROPKA's accuracy is better if the p__K__~a~ values are weakly shifted but on equal footing with that of KBPLUS for more strongly shifted values. On the other hand, PKAcal reproduces strongly shifted p__K__~a~ values badly but weakly shifted values with the same accuracy as PROPKA. We tested different consensus approaches combining data from all three methods to find a general procedure for most accurate p__K__~a~ predictions. In most of the cases we found that the consensus approach reproduced experimental data with better accuracy than any of the individual methods alone. Β© 2008 Wiley Periodicals, Inc. J Comput Chem 2008
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