Task 6: Protein structure prediction from evolutionary sequence variation

Results

lab journal

Contact Prediction

<figure id="ras_score_dist">

</figure>

<figtable id="hfe_score_dist" >

</figtable>

domain	length	seq. in alignment	reference	HS pairs	TP	FP	TP -rate
Ras	160	21151	5P21	65	53	12	0.82
MHC I	174	25167	1A6Z_A	69	29	40	0.42
Ig C1-set	76	16509	1A6Z_A	15	9	6	0.6

<figure id="cm_1a6z">

</figure> <figure id="cm_hras">

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• Why are the scores of residues close in sequence amongst the highest? Why are the pairs distant in sequence (n>5) more interesting for structure prediction?

It lies in the nature of proteins, that residues that are close in sequence, are also close in structure. Consequently, they are evolutionary coupled and show covariation in the multiple sequence alignment. The pairs that are at least five residues apart in sequence, are more interesting for structure prediction, because they contain more information about the overall topology of the protein, i.e. they reduce the space of possible protein conformations more than pairs that are close in sequence.

• Look at the values, range and distribution of scores.

For the MHC I domain of HFE_HUMAN, the score distribution is shown in table <xr id="hfe_score_dist" />. The values range from -0.94 to 2.57 with the mean at -0.07. The score distribution corresponds to a slightly right skewed normal distribution, where most values are in the range of -1 to 1. Only 0.5% of scores have a value above 1. Thus, scores with a value greater than one can be considered as high scoring.

• How many of the high-scoring pairs are true or false positives? Does this correlate with the value of the score? Visualize the predicted contacts together with the crystal structure contacts in a contact map plot.

Table <xr id="hs_table"> shows, that TP-rate can range from 0.8 for Ras to 0.4 for the MHC I domain. Since the number of sequences in the multiple alignment is above 15 000 for all three domains, the TP-rate among the high scoring pairs depends not only on the number of sequences in the alignment, but also on the actual sequence at hand. As discussed in <ref name="EVfold_method"> Marks DS, Colwell LJ, Sheridan R, Hopf TA, Pagnani A, et al. (2011) Protein 3D Structure Computed from Evolutionary Sequence Variation. PLoS ONE 6(12): e28766. doi:10.1371/journal.pone.0028766 </ref> , possible confounding factors could be pyhlogenetic bias or functional constraints from interactions with other molecules.

<figure id="score_correlation">

The distribution of scores for TP and FP predicted contact pairs of MHC I is shown. Note that only high scoring pairs are shown in this plot.

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The correlation between CN score and TP/FP contact is not very good as indicated by a Pearson correlation coefficient of 0.354 and the overlap between the boxplots in figure <xr id="score_correlation">.

• Can you determine evolutionary hot spots, i.e. functionally important residues? Compare to conserved sites in the MSA. Compare with your results from task 7 (when you are working on task 7, i.e. this is a task for the future).

• Here, the DI score is given. Compare the top 50 DI and CN (from freecontact) scores. How large is the overlap (>80%)?

For RAS_HUMAN, only 20(40%) of the top 50 scores overlaped.

MHC EVFOLD: constraints: 70(40%) 113(65%) 174(100%)

imm EVFOLD

Structural Models

The structural models for Ras were calculated with three different numbers of evolutionary constraints: 76(40%), 123(65%) and 189 (100%).

<figure id="rmsd_evfold">

Comparison of Ca-RMSD values distributions between the different numbers of ECs used.

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Discussion

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