Abstract
BACKGROUND: Solvent accessibility (ASA) of amino acid residues is often transformed from absolute values of exposed surface area to their normalized relative values. This normalization is typically attained by assuming a highest exposure conformation based on extended state of that residue when it is surrounded by Ala or Gly on both sides i.e. Ala-X-Ala or Gly-X-Gly solvent exposed area. Exact sequence context, the folding state of the residues, and the actual environment of a folded protein, which do impose additional constraints on the highest possible (or highest observed) values of ASA, are currently ignored. Here, we analyze the statistics of these constraints and examine how the normalization of absolute ASA values using context-dependent Highest Observed ASA (HOA) instead of context-free extended state ASA (ESA) of residues can influence the performance of sequence-based prediction of solvent accessibility. Characterization of burial and exposed states of residues based on this normalization has also been shown to provide better enrichment of DNA-binding sites in exposed residues. RESULTS: We compiled the statistics of highest observed ASA (HOA) of residues in their different contexts and analyzed their distribution in all 400 possible combinations for each residue type. We observe that many trippetides are more exposed than ESA and that HOA residues are often found in turn, coil and bend conformations. On the other hand several residues are never observed in an exposure state close to ESA values. A neural networks trained with HOA-normalized data outperforms the one trained with ESA-normalized values. However, the improvements are subtle in some residues, while they are more significant in others. CONCLUSION: HOA based normalization of solvent accessibility from native structures is proposed and it shows improvement in sequence-based predictability, as well as enrichment in interface residues on surface. There may still be some difference between the highest possible ASA and highest observed ASA due to an insufficiently covered space of ASA distribution in the PDB, which limit the overall improvement in prediction to a relatively modest degree.