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this remains an open question. The adequacy of our current database can be approached in a more straightforward way. The conformations of over 40,000 amino acids in approximately 200 distinct protein structures are known. These distribute between a-structure (~ 30 percent), b-structure (~ 30 percent), and turns or coils (~ 40 percent). The likelihood that alanine will appear in an a-helix ( L_a (Ala) ) can be calculated easily from this data set.

Even the 400 amino acid doublet propensities, which reflect the conditional probability that an alanine will occur in an a-helix contingent on the amino acid type of the neighboring residue, can be usefully approximated. However, the current data set is not adequate to provide information about the 8,000 triplet amino acid preferences. Moreover, it is not clear that the triplet interaction preferences will be the sum of three doublet interactions or that complete triplet preferences adequately define the conformational preferences of amino acids. Additional protein crystal structures and studies on model peptide systems will help in overcoming these limitations.

In 1974, a landmark paper on protein secondary structure prediction was published by Chou and Fasman (1974). Working with a much smaller protein database, the authors calculated the secondary structure propensities of each amino acid, for example,

From this information, residues were classified as helix formers (P_a ³ 1.05), intermediate (0.70 £ P_a < 1.05), and helix breakers ( P_a < 0.70 ). Local clusters of helix formers defined helical nucleation sites. These nuclei were extended toward the N- and C-termini following rules based on the aggregate P_a 's. Although no computer algorithm accompanied the initial work, the method was sufficiently simple that it could be applied by hand. The accuracy of this algorithm (that is, the