Prediction of protein secondary structure from amino acid sequence |
| |
Authors: | Jen Tsi Yang |
| |
Institution: | (1) Cardiovascular Research Institute, University of California, 94143-0130 San Francisco, California |
| |
Abstract: | The conformational parametersP
k
for each amino acid species (j=1–20) of sequential peptides in proteins are presented as the product ofP
i,k
, wherei is the number of the sequential residues in thekth conformational state (k= -helix, -sheet, -turn, or unordered structure). Since the average parameter for ann-residue segment is related to the average probability of finding the segment in the kth state, it becomes a geometric mean of (P
k
)av= (P
i,k
)
1/n
with amino acid residuei increasing from 1 ton. We then used ln(Pk)av to convert a multiplicative process to a summation, i.e., ln(P
k
)
av
=(1/n) P
i,k
(i=1 ton) for ease of operation. However, this is unlike the popular Chou-Fasman algorithm, which has the flaw of using the arithmetic mean for relative probabilities. The Chou-Fasman algorithm happens to be close to our calculations in many cases mainly because the difference between theirP
k
and our InP
k
is nearly constant for about one-half of the 20 amino acids. When stronger conformation formers and breakers exist, the difference become larger and the prediction at the N- and C-terminal -helix or -sheet could differ. If the average conformational parameters of the overlapping segments of any two states are too close for a unique solution, our calculations could lead to a different prediction. |
| |
Keywords: | Chou-Fasman algorithm protein primary structure secondary structure prediction |
本文献已被 SpringerLink 等数据库收录! |
|