Local Flexibility Aids Protein Multiple Structure Alignment局部柔度艾滋蛋白质多结构比对

1、remote homology detection of beta-structural motifs using random fields matt menke, tuftsbonnie berger, mitlenore cowen, tufts ismb 3dsig 2010july 10, 2010inferring structural similarity from homology is hard at the scop superfamily/fold levelprofile hmms hmm is trained from sequence alignment of kn

2、own structures but: cannot capture pariwise long-range beta-sheet interactions! hmms cannot capture statistical preferences from residues close in space but far, and a variable distance apart in seq.pectate lyase c (yoder et al. 1993)look at just pairs or generalize to markov random fields only look

3、 at pairs: generalize to markov random fieldsliu et al. 2009 zhao et al. 2010 menke et al. 2010 (this work) b3t2b2b1bradley, cowen, menke, king, berger, pnas, 2001, 98:26, 14,819-14,824 ; cowen, bradley, menke, king, berger (2002), j comp biol, 9, 261-276lets look at what this would mean for propell

4、er foldsgoal: capture hmm sequence information and pairwise information in beta-structural motifs at the same time! scop (http:/scop.mrc-lmb.cam.ac.uk/scopstructural motifs using random fieldssmurfstructural motifs using random fieldscan we getthe benefitof pairwisecorrelationswithout having to thro

5、w awayall sequence info?the template is learned from solved structures in the pdbthe template is learned from solved structures in the pdb:aligned with matt digression: matt structural alignment program menke, berger, cowen, (plos combio 2008)specifically designed to align more distant homologsafp c

6、haining using dynamic programming with “translations and twists” (flexibility) the template is learned from solved structures in the pdb:aligned with matt two beta tables are learned from amphapathic beta sheets that are not propellers from solved structures in the pdb.acdefghiklmnpqrstvwya0.780.180

7、.140.150.590.700.061.060.020.050.110.030.170.27c30.050.280.030.340.070.020.010.030.020.050.080.390.100.10d0.140.030.030.020.110.010.160.050.070.010.050.080.030.03e0.150.060.060.050.200.100.570.080.100.020.080.150.190.

8、250.570.050.18f0.560.660.610.101.060.080.050.150.050.130.43g0.7080.610.580.100.770.030.070.170.090.240.311.270.180.48h0.060.050.000.040.130.020.130.040.050.010.010.020.060.090.230.030.07i1.060.280.110.401.060.770.132.270.102.210.

9、380.140.050.290.130.260.452.560.180.42k0.070.030.010.100.050.070.020.100.030.160.030.040.000.050.010.050.050.170.020.10l1.190.340.160.560.480.180.060.330.180.290.362.640.250.50m0.170.070.050.040.380.030.480.100.010.010.030.040.060.070.490.080.06n0.120.020.070.100.

10、080.230.050.140.040.180.010.050.010.050.080.040.08p0.050.010.010.020.050.070.010.050.000.060.010.010.010.010.010.020.020.090.020.04q0.110.030.050.010.290.050.330.030.050.010.040.070.050.13r0.080.020.080.150.080.090.020.130.010.180.040.060.010.080.040.050.070.160.0

11、20.07s0.220.050.040.060.260.050.290.060.120.020.170.090.080.09t0.250.020.310.090.450.050.360.070.160.020.170.040.030.11v1.530.390.160.571.351.270.232.560.172.640.490.180.090.270.160.290.443.740.230.64w30.030.180.020.250.080.040.0

12、20.050.020.080.030.230.050.05y0.270.100.030.180.430.480.070.420.100.500.060.080.040.130.070.090.110.640.050.10acdefghiklmnpqrstvwya0.270.04 0.13 0.28 0.22 0.18 0.11 0.31 0.23 0.38 0.06 0.11 0.06 0.13 0.22 0.28 0.37 0.49 0.06 0.25c0.040.08 0.05 0.07 0.04 0.03 0.03 0.04 0.07 0.04 0.02 0.06 0.01 0.08 0

13、.11 0.05 0.06 0.10 0.04 0.09d0.130.05 0.09 0.13 0.09 0.08 0.13 0.08 0.71 0.12 0.06 0.22 0.03 0.15 0.50 0.36 0.41 0.24 0.02 0.12e0.280.07 0.13 0.43 0.31 0.15 0.21 0.43 1.92 0.50 0.14 0.28 0.10 0.25 1.49 0.60 1.01 0.63 0.09 0.32f0.220.04 0.09 0.31 0.23 0.16 0.12 0.34 0.28 0.32 0.12 0.14 0.06 0.19 0.29

14、 0.27 0.34 0.38 0.13 0.33g0.180.03 0.08 0.15 0.16 0.08 0.06 0.15 0.16 0.15 0.06 0.08 0.05 0.10 0.15 0.14 0.17 0.21 0.03 0.19h0.110.03 0.13 0.21 0.12 0.06 0.06 0.08 0.25 0.12 0.04 0.10 0.07 0.11 0.14 0.19 0.20 0.21 0.05 0.14i0.310.04 0.08 0.43 0.34 0.15 0.08 0.48 0.57 0.32 0.10 0.14 0.07 0.28 0.43 0.

15、30 0.32 0.59 0.07 0.40k0.230.07 0.71 1.92 0.28 0.16 0.25 0.57 0.63 0.38 0.15 0.46 0.08 0.42 0.33 0.70 1.17 0.71 0.22 0.52l0.380.04 0.12 0.50 0.32 0.15 0.12 0.32 0.38 0.48 0.10 0.15 0.12 0.23 0.36 0.26 0.34 0.62 0.07 0.39m 0.060.02 0.06 0.14 0.12 0.06 0.04 0.10 0.15 0.10 0.12 0.09 0.04 0.08 0.10 0.12

16、 0.14 0.10 0.02 0.08n0.110.06 0.22 0.28 0.14 0.08 0.10 0.14 0.46 0.15 0.09 0.38 0.09 0.22 0.25 0.48 0.49 0.27 0.05 0.18p0.060.01 0.03 0.10 0.06 0.05 0.07 0.07 0.08 0.12 0.04 0.09 0.02 0.06 0.07 0.07 0.13 0.13 0.02 0.16q0.130.08 0.15 0.25 0.19 0.10 0.11 0.28 0.42 0.23 0.08 0.22 0.06 0.24 0.32 0.28 0.

17、48 0.26 0.03 0.16r0.220.11 0.50 1.49 0.29 0.15 0.14 0.43 0.33 0.36 0.10 0.25 0.07 0.32 0.36 0.47 0.68 0.72 0.11 0.30s0.280.05 0.36 0.60 0.27 0.14 0.19 0.30 0.70 0.26 0.12 0.48 0.07 0.28 0.47 0.91 0.88 0.50 0.06 0.27t0.370.06 0.41 1.01 0.34 0.17 0.20 0.32 1.17 0.34 0.14 0.49 0.13 0.48 0.68 0.88 1.60

18、0.82 0.07 0.27v0.490.10 0.24 0.63 0.38 0.21 0.21 0.59 0.71 0.62 0.10 0.27 0.13 0.26 0.72 0.50 0.82 0.87 0.21 0.64w 0.060.04 0.02 0.09 0.13 0.03 0.05 0.07 0.22 0.07 0.02 0.05 0.02 0.03 0.11 0.06 0.07 0.21 0.02 0.13y0.250.09 0.12 0.32 0.33 0.19 0.14 0.40 0.52 0.39 0.08 0.18 0.16 0.16 0.30 0.27 0.27 0.

19、64 0.13 0.38buried residueexposed residue/propellers/si/computing a score sequences are scored by computing their best “threading” or “parse” against the template as a sum of hmm(score) + pairwise(score) no longer polynomial time (multi-dimensional dynamic programming) tractabl

20、e on propellers because paired beta-strands dont interleave too muchlets look at what this would mean for propeller foldslets look at what this would mean for propeller folds training set for hmm score: leave-superfamily-out cross validation training set for pairwise score: amphapathic beta-sheets f

21、rom non-propellersresults on propellers6-bladed 7-bladedtneghmmersmurfhmmersmurf 97% 52 80 80 87 96% 56 80 80 87 95% 64 80 87 93 94% 68 84 90 93 93% 68 84 90 93 92% 68 88 90 97 91% 68 92 90 97 90% 68 92 93100results on propellers note that this is “6 (or 7)” bladed propeller versus non-propeller dis

22、tinguishing the number of blades in the propeller seems to be a much harder problem.different propeller closures1jof 2trc so: what new sequences fold into propellers? we predict a double propeller motif in the n-terminal region of a hybrid 2-component sensor protein. what are these proteins? first f

23、ound in a benign bacteria in human gut. may be involved in adapting to changes in diet/efficiently processing different sugars found in other bacterial species: help sense and adapt to environmental changes. big stretch (i am not a biologist): help to study human obesity epidemic? popular domains hi

24、ska histidine kinase domain ggdef adenylyl cyclase signalling domain spoiie sporulation domain gaf domain pas domain hatpase domainspecies distribution distinguishing number of blades the automatic smurf consensus 7-bladed template only learns 6 blades. sequence motifs are similar the same pfam moti

25、f occurs in propellers with different numbers of blades the fix: throw out propellers with a “funky” 7th blade by hand and build a new template. now 6-bladed propellers dont like the 7-bladed template double propellers we found are probably 7-7 (but 7-6 is also plausible).predict propellers with smurf! accepts sequences in fasta format 6,7,8-bladed templates, as well as all 9 double-propeller template/propellers/sipairwise tables long list of predicted propeller sequ