Abstract
The hydration properties of a protein are important determinants
of its structure and function. Here, modular neural networks are
employed to predict ordered hydration sites using protein sequence
information. First, secondary structure and solvent accessibility
are predicted from sequence with two separate neural networks.
These predictions are used as input together with protein
sequences for networks predicting hydration of residues, backbone
atoms and sidechains. These networks are teined with protein
crystal structures. The prediction of hydration is improved by
adding information on secondary structure and solvent
accessibility and, using actual values of these properties,
redidue hydration can be predicted to 77% accuracy with a Metthews
coefficient of 0.43. However, predicted property data with an
accuracy of 60-70% result in less than half the improvement in
predictive performance observed using the actual values. The
inclusion of property information allows a smaller squence window
to be used in the networks to predict hydration. It has a greater
impact on the accuracy of hydration site prediction for backbone
atoms than for sidechains and for non-polar than polar residues.
The networks provide insight into the mutual interdependencies
between the location of ordered water sites and the structural and
chemical characteristics of the protein residues.
Original language | English |
---|---|
Journal | Protein Engineering |
Volume | 11 |
Issue number | 1 |
Pages (from-to) | 11-19 |
ISSN | 0269-2139 |
Publication status | Published - 1998 |