Table 5.
Fit parameters for the HMM
| HMM n = 0 | HMM n = 1 | ||||||
|---|---|---|---|---|---|---|---|
| LQ | LS | λ | 104 λ2 | 103K | λ | 104 λ2 | 103K |
| 348 | 150 | 0.2890 ± 0.85% | 49.4722 ± 7.27% | 0.2310 ± 9.32% | 21.4600 ± 66.56% | ||
| 200 | 0.2894 ± 2.84% | 50.0796 ± 24.47% | 0.2274 ± 1.74% | 20.1017 ± 13.25% | |||
| 300 | 0.2895 ± 2.69% | 53.3472 ± 24.00% | 0.2240 ± 4.86% | 17.8934 ± 37.22% | |||
| 348 | 0.2988 ± 3.24% | 72.2356 ± 30.15% | 0.2234 ± 2.39% | 16.8704 ± 18.79% | |||
| 360 | 0.2895 ± 1.79% | 51.9056 ± 16.04% | 0.2220 ± 2.14% | 16.3757 ±16.52% | |||
| 400 | 0.2859 ± 3.49% | 48.4496 ± 31.10% | 0.2232 ± 2.40% | 17.5141 ± 18.94% | |||
| 500 | 0.2912 ± 6.63% | 54.0687 ± 61.22% | 0.2182 ± 2.39% | 14.7371 ± 19.10% | |||
| 600 | 0.2901 ± 3.38% | 51.9412 ± 31.74% | 0.2180 ± 2.59% | 14.2439 ± 20.86% | |||
| HMM n = 2 | HMM n = 3 | ||||||
| LQ | LS | λ | 104 λ2 | K | λ | 104 λ2 | K |
| 348 | 150 | 0.1968 ± 0.70% | 2.9247 ± 1.37% | 12.0400 ± 6.48% | 0.1767 ± 0.44% | 2.6797 ± 1.01% | 7.4435 ± 3.72% |
| 200 | 0.1947 ± 2.12% | 9.8704 ± 14.29% | 0.1795 ± 0.46% | 2.3586 ± 0.92% | 8.5733 ± 3.87% | ||
| 300 | 0.1937 ± 3.60% | 9.9597 ± 25.32% | 0.1863 ± 0.41% | 2.0008 ± 0.94% | 11.7859 ± 5.63% | ||
| 348 | 0.1888 ± 3.19% | 8.1338 ± 22.42% | 0.1876 ± 0.32% | 1.9328 ± 0.89% | 12.1223 ± 3.83% | ||
| 360 | 0.1926 ± 3.17% | 9.7957 ± 22.82% | 0.1853 ± 0.27% | 1.9530 ± 0.65% | 10.8640 ± 2.65% | ||
| 400 | 0.1934 ± 1.05% | 9.9321 ± 8.22% | 0.1757 ± 1.64% | 7.1756 ± 11.58% | |||
| 500 | 0.1919 ± 1.61% | 9.3630 ± 12.32% | 0.1783 ± 0.98% | 7.7945 ± 7.18% | |||
| 600 | 0.1912 ± 1.70% | 9.3303 ± 13.25% | 0.1768 ± 1.01% | 7.4165 ± 8.19% | |||
| HMM n = 4 | HMM n = 5 | ||||||
| LQ | LS | λ | 104 λ2 | 103K | λ | 104 λ2 | 103K |
| 348 | 150 | 0.1732 ± 0.47% | 2.2119 ± 1.14% | 7.4991 ± 6.08% | 0.1710 ± 0.38% | 2.0698 ± 0.92% | 8.1950 ± 3.70% |
| 200 | 0.1686 ± 0.28% | 2.1187 ± 0.72% | 6.4162 ± 3.14% | 0.1657 ± 0.39% | 1.8231 ± 1.14% | 6.9148 ± 3.82% | |
| 300 | 0.1682 ± 0.36% | 1.9635 ± 0.79% | 6.5436 ± 4.22% | 0.1599 ± 0.37% | 1.7836 ± 0.79% | 5.4451 ± 3.85% | |
| 348 | 0.1685 ± 0.35% | 1.9408 ± 0.74% | 7.3851 ± 3.34% | 0.1580 ± 0.28% | 1.7930 ± 0.68% | 5.3049 ± 2.61% | |
| 360 | 0.1678 ± 0.42% | 1.9421 ± 0.92% | 6.5775 ± 4.07% | 0.1605 ± 0.23% | 1.7481 ± 0.50% | 5.7512 ± 2.89% | |
| 400 | 0.1662 ± 0.18% | 1.9782 ± 0.40% | 6.4164 ± 2.32% | 0.1587 ± 0.28% | 1.7828 ± 0.73% | 5.4513 ± 2.57% | |
| 500 | 0.1693 ± 0.24% | 1.9047 ± 0.51% | 7.0735 ± 2.11% | 0.1587 ± 0.16% | 1.7957 ± 0.40% | 5.4770 ± 2.31% | |
| 600 | 0.1693 ± 0.17% | 1.8994 ± 0.39% | 7.1112 ± 2.06% | 0.1575 ± 0.29% | 1.8330 ± 0.58% | 5.2125 ± 2.68% | |
| HMM n = 6 | HMM n = 7 | ||||||
| LQ | LS | λ | 104 λ2 | 103K | 104 λ2 | 103K | |
| 348 | 150 | 0.1663 ± 0.49% | 2.1403 ± 1.04% | 7.9392 ± 5.83% | 0.1646 ± 0.30% | 2.1396 ± 0.65% | 8.7088 ± 4.21% |
| 200 | 0.1614 ± 0.25% | 1.7767 ± 0.65% | 6.7568 ± 2.30% | 0.1574 ± 0.41% | 1.7687 ± 1.17% | 6.5219 ± 3.81% | |
| 300 | 0.1551 ± 0.28% | 1.5986 ± 0.80% | 5.2551 ± 3.18% | 0.1514 ± 0.26% | 1.4638 ± 0.62% | 5.0238 ± 4.34% | |
| 348 | 0.1531 ± 0.20% | 1.5993 ± 0.55% | 4.9132 ± 2.71% | 0.1482 ± 0.33% | 1.4755 ± 0.77% | 4.4535 ± 4.13% | |
| 360 | 0.1536 ± 0.34% | 1.6036 ± 1.02% | 4.9160 ± 3.41% | 0.1490 ± 0.39% | 1.4479 ± 0.93% | 4.6858 ± 3.28% | |
| 400 | 0.1537 ± 0.27% | 1.5713 ± 0.62% | 4.9524 ± 3.05% | 0.1494 ± 0.24% | 1.4328 ± 0.70% | 4.6867 ± 2.08% | |
| 500 | 0.1519 ± 0.23% | 1.6229 ± 0.67% | 4.6812 ± 2.14% | 0.1472 ± 0.29% | 1.4706 ± 0.63% | 4.2881 ± 2.50% | |
| 600 | 0.1489 ± 0.15% | 1.7148 ± 0.33% | 4.2283 ± 2.16% | 0.1460 ± 0.18% | 1.5193 ± 0.49% | 4.2679 ± 1.74% | |
| HMM n = 8 | HMM n = 9 | ||||||
| LQ | LS | λ | 104 λ2 | 103K | λ | 104 λ2 | 103K |
| 348 | 150 | 0.1595 ± 0.47% | 2.2162 ± 1.01% | 7.5355 ± 4.01% | 0.1603 ± 0.23% | 2.1517 ± 0.48% | 8.0273 ± 2.17% |
| 200 | 0.1534 ± 0.55% | 1.8019 ± 1.46% | 5.9224 ± 5.25% | 0.1508 ± 0.14% | 1.7854 ± 0.28% | 6.3535 ± 1.89% | |
| 300 | 0.1473 ± 0.47% | 1.3916 ± 1.24% | 4.8483 ± 4.01% | 0.1413 ± 0.12% | 1.4118 ± 0.35% | 4.2141 ± 1.43% | |
| 348 | 0.1458 ± 0.32% | 1.3409 ± 0.85% | 4.6141 ± 3.69% | 0.1398 ± 0.10% | 1.3281 ± 0.33% | 3.9661 ± 1.44% | |
| 360 | 0.1469 ± 0.34% | 1.2868 ± 0.90% | 4.9271 ± 2.73% | 0.1400 ± 0.16% | 1.2888 ± 0.43% | 4.0126 ± 1.79% | |
| 400 | 0.1440 ± 0.34% | 1.3591 ± 1.05% | 4.0064 ± 3.48% | 0.1382 ± 0.25% | 1.2954 ± 0.67% | 3.7257 ± 2.14% | |
| 500 | 0.1433 ± 0.29% | 1.3382 ± 0.85% | 3.9952 ± 2.70% | 0.1352 ± 0.14% | 1.3472 ± 0.42% | 3.1780 ± 1.68% | |
| 600 | 0.1416 ± 0.33% | 1.3760 ± 0.94% | 3.7782 ± 3.14% | 0.1359 ± 0.13% | 1.3399 ± 0.38% | 3.3536 ± 1.49% | |
| HMM n = 10 | HMM n = 11 | ||||||
| LQ | LS | λ | 104 λ2 | 103K | λ | 104 λ2 | 103K |
| 348 | 150 | 0.1552 ± 0.14% | 2.2225 ± 0.30% | 6.7936 ± 2.08% | 0.1455 ± 0.14% | 2.3813 ± 0.15% | 4.9660 ± 3.82% |
| 200 | 0.1459 ± 0.22% | 1.8336 ± 0.37% | 5.7585 ± 3.30% | 0.1417 ± 0.17% | 1.8428 ± 0.35% | 5.1264 ± 2.07% | |
| 300 | 0.1370 ± 0.22% | 1.4024 ± 0.56% | 3.8087 ± 1.79% | 0.1324 ± 0.27% | 1.3842 ± 0.68% | 3.2129 ± 2.79% | |
| 348 | 0.1353 ± 0.15% | 1.2962 ± 0.38% | 3.5507 ± 1.68% | 0.1316 ± 0.22% | 1.2518 ± 0.69% | 3.1546 ± 1.94% | |
| 360 | 0.1343 ± 0.13% | 1.2830 ± 0.36% | 3.4674 ± 1.39% | 0.1297 ± 0.25% | 1.2737 ± 0.52% | 2.9445 ± 2.81% | |
| 400 | 0.1334 ± 0.16% | 1.2602 ± 0.38% | 3.2164 ± 1.71% | 0.1302 ± 0.20% | 1.2160 ± 0.56% | 2.9704 ± 1.59% | |
| 500 | 0.1307 ± 0.16% | 1.3013 ± 0.46% | 2.8331 ± 1.22% | 0.1280 ± 0.30% | 1.2426 ± 0.86% | 2.7433 ± 2.73% | |
| 600 | 0.1305 ± 0.23% | 1.3097 ± 0.56% | 2.8239 ± 1.82% | 0.1257 ± 0.22% | 1.2908 ± 0.55% | 2.4921 ± 1.79% | |
The table shows the fit parameters of the score distribution Prob(S = s| # of helices = n) for 1 ≤ n ≤ 11 for LQ = 348 and different subject lengths. For entries, where λ2 is left out, a suitable fit (with a small reduced χ2 value) to the modified Gumbel distribution Eq. (15) was not possible and only the Gumbel parameters of the high probability region are shown.