The SPS score is calculated assuming a two state model (buried and exposed) and each amino acid of the motif gets a fraction score (between 0 and 1) representing the probability of being in that state. For each non-wildcard residue of the motif, the greater of the two probabilities is considered and a normalized probability of the motif being in the exposed state is calulated as the SPS score of the motif.

Just because a motif is on the surface does not infer function. Some motifs may require a specific structure. Furthermore, this algorithm has a ~75% prediction accuracy for individual amino acids, thus has an inherent prediction of false positive surface residues.

In order to be a functional, the motif, or at least a part of it must be exposed to solvent. Thus, motifs predictions that are buried are likely false positive.

This surface prediction algorithm has a 75% accuracy when prediction of surface residues in 215 proteins with known crystal structures (Naderi-Manesh, [2001] Proteins 32:452-59).