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Kyte-Doolittle algorithm

The SPLIT algorithm was optimized for predicting transmembrane a-helices by using the Kyte-Doolittle hydropathy scale to create profile of a-helix preferences. The digital version of prediction for transmembrane a-helices is designated as the TMH predictor. Predicted profile of P-strand preferences can be used to find sequence location of potential membrane-embedded or surface-attached P-strands. The score for potential membrane-attached P-strand... [Pg.413]

Figure 3 Score profiles for cxlbjarde (Figure 3A) and for cox3 parde (Figure 3B) of cytochrome oxidase from Paracoccus denitrificans [14] are obtained by substraction of turn preferences from a-helix preferences (full line). Digital predictions, as outcome of the best training procedure for the SPLIT algorithm with Kyte-Doolittle hydropathy scale (Methods), are shown as bold horizontal bars at the score level 0.5. Observed location of TMH segments are shown as bold horizontal bars at the score level 0.2. Figure 3 Score profiles for cxlbjarde (Figure 3A) and for cox3 parde (Figure 3B) of cytochrome oxidase from Paracoccus denitrificans [14] are obtained by substraction of turn preferences from a-helix preferences (full line). Digital predictions, as outcome of the best training procedure for the SPLIT algorithm with Kyte-Doolittle hydropathy scale (Methods), are shown as bold horizontal bars at the score level 0.5. Observed location of TMH segments are shown as bold horizontal bars at the score level 0.2.
Our algorithm can give partial answer to the question what attributes are optimal predictors for specific folding motifs. Kyte-Doolittle type hydropathy values and Chou-Fasman type conformational preferences are two obvious answers to the question what amino acid attributes are good predictors for majority of transmembrane helices. Indeed, three such scales MODKD, KYTDO and CPREF (Table 4), are on the very top of the list of the best amino acid scales (Table 5). Performance parameters that punish overprediction (A-j y and Qp) give advantage to hydropathy values. Modifications to the Kyte-Doolittle values in the MODKD... [Pg.438]

Two data bases of soluble proteins of known structure used to find false positive prediction results (Table I and Table II). Gaussian parameters needed for evaluation of preference functions based on the Kyte-Doolittle hydropathy scale [17] (Table III). Table with detailed prediction results for transmembrane helices in 168 integral membrane proteins (Table IV). Table with a detailed comparison of prediction results for 10 best known membrane proteins for our and three other algorithms (Table V). All these tables together with the FORTRAN 77 source code are available from the anonymous ftp server mia.os.camet.hr in the /pub/pssp directory. The anonymous login is ftp and the e-mail address is accepted as password. The list of files with short descriptions is contained in the 00index.txt file. [Pg.441]

Analyses of these proteins with the TMPredict algorithm and comparison of these outputs to the Kyte-Doolittle hydrophobicity plots indicate that these proteins are... [Pg.336]


See other pages where Kyte-Doolittle algorithm is mentioned: [Pg.434]    [Pg.434]    [Pg.420]    [Pg.432]    [Pg.439]    [Pg.132]    [Pg.152]    [Pg.271]    [Pg.216]    [Pg.403]    [Pg.132]    [Pg.411]    [Pg.139]   
See also in sourсe #XX -- [ Pg.434 ]




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