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Extensive protein and DNA backbone sampling improves structure-based specificity prediction for C2H2 zinc fingers.

Yanover C, Bradley P - Nucleic Acids Res. (2011)

Bottom Line: Sequence-specific DNA recognition by gene regulatory proteins is critical for proper cellular functioning.Here, we present a novel molecular modeling protocol for protein-DNA interfaces that borrows conformational sampling techniques from de novo protein structure prediction to generate a diverse ensemble of structural models from small fragments of related and unrelated protein-DNA complexes.The extensive conformational sampling is coupled with sequence space exploration so that binding preferences for the target protein can be inferred from the resulting optimized DNA sequences.

View Article: PubMed Central - PubMed

Affiliation: Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA.

ABSTRACT
Sequence-specific DNA recognition by gene regulatory proteins is critical for proper cellular functioning. The ability to predict the DNA binding preferences of these regulatory proteins from their amino acid sequence would greatly aid in reconstruction of their regulatory interactions. Structural modeling provides one route to such predictions: by building accurate molecular models of regulatory proteins in complex with candidate binding sites, and estimating their relative binding affinities for these sites using a suitable potential function, it should be possible to construct DNA binding profiles. Here, we present a novel molecular modeling protocol for protein-DNA interfaces that borrows conformational sampling techniques from de novo protein structure prediction to generate a diverse ensemble of structural models from small fragments of related and unrelated protein-DNA complexes. The extensive conformational sampling is coupled with sequence space exploration so that binding preferences for the target protein can be inferred from the resulting optimized DNA sequences. We apply the algorithm to predict binding profiles for a benchmark set of eleven C2H2 zinc finger transcription factors, five of known and six of unknown structure. The predicted profiles are in good agreement with experimental binding data; furthermore, examination of the modeled structures gives insight into observed binding preferences.

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Comparison to fixed-backbone specificity predictions. BLiC scores for the fragment assembly specificity predictions (green bars) are compared to scores of fixed-backbone, template-based predictions started from the target structure itself (red bars) or the template with highest sequence identity to the target (purple bars), as well as the median BLiC score for all non-self template-based predictions (blue bars).
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Figure 8: Comparison to fixed-backbone specificity predictions. BLiC scores for the fragment assembly specificity predictions (green bars) are compared to scores of fixed-backbone, template-based predictions started from the target structure itself (red bars) or the template with highest sequence identity to the target (purple bars), as well as the median BLiC score for all non-self template-based predictions (blue bars).

Mentions: To help assess the contribution of backbone flexibility in the fragment assembly protocol, we conducted a series of fixed-backbone, template-based binding specificity prediction simulations. As in the structure prediction comparison, we performed an all-against-all analysis of 9 individual ZFs, using each finger as a template for fixed-backbone specificity predictions targeted at the other fingers and itself. The results are given in Figure 8. For 6 of the 9 targets, the fragment assembly predictions (green bars) are better than any of the fixed-backbone predictions, even those based on the crystal structure of the target itself (‘self-template’), suggesting the importance of backbone flexibility in assessing the energetic cost of mutations away from the crystal structure DNA sequence. If we exclude the self-template predictions, the fragment assembly results are better in 7 of the cases (recall that we exclude the structure being predicted as well as any highly sequence-similar structures from fragment selection). None of the predictions are successful for the remaining two fingers, fingers 1 and 4 from YY1, which represent challenging targets: both are outside the highly specific portion of the binding motif [Figure 7 PFM columns 1–3 (F4) and 10–12 (F1)]; in the YY1 crystal structure, finger 1 lifts off the DNA and does not make any hydrogen bonds or other obvious specificity determining contacts to the major groove.Figure 8.


Extensive protein and DNA backbone sampling improves structure-based specificity prediction for C2H2 zinc fingers.

Yanover C, Bradley P - Nucleic Acids Res. (2011)

Comparison to fixed-backbone specificity predictions. BLiC scores for the fragment assembly specificity predictions (green bars) are compared to scores of fixed-backbone, template-based predictions started from the target structure itself (red bars) or the template with highest sequence identity to the target (purple bars), as well as the median BLiC score for all non-self template-based predictions (blue bars).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3113574&req=5

Figure 8: Comparison to fixed-backbone specificity predictions. BLiC scores for the fragment assembly specificity predictions (green bars) are compared to scores of fixed-backbone, template-based predictions started from the target structure itself (red bars) or the template with highest sequence identity to the target (purple bars), as well as the median BLiC score for all non-self template-based predictions (blue bars).
Mentions: To help assess the contribution of backbone flexibility in the fragment assembly protocol, we conducted a series of fixed-backbone, template-based binding specificity prediction simulations. As in the structure prediction comparison, we performed an all-against-all analysis of 9 individual ZFs, using each finger as a template for fixed-backbone specificity predictions targeted at the other fingers and itself. The results are given in Figure 8. For 6 of the 9 targets, the fragment assembly predictions (green bars) are better than any of the fixed-backbone predictions, even those based on the crystal structure of the target itself (‘self-template’), suggesting the importance of backbone flexibility in assessing the energetic cost of mutations away from the crystal structure DNA sequence. If we exclude the self-template predictions, the fragment assembly results are better in 7 of the cases (recall that we exclude the structure being predicted as well as any highly sequence-similar structures from fragment selection). None of the predictions are successful for the remaining two fingers, fingers 1 and 4 from YY1, which represent challenging targets: both are outside the highly specific portion of the binding motif [Figure 7 PFM columns 1–3 (F4) and 10–12 (F1)]; in the YY1 crystal structure, finger 1 lifts off the DNA and does not make any hydrogen bonds or other obvious specificity determining contacts to the major groove.Figure 8.

Bottom Line: Sequence-specific DNA recognition by gene regulatory proteins is critical for proper cellular functioning.Here, we present a novel molecular modeling protocol for protein-DNA interfaces that borrows conformational sampling techniques from de novo protein structure prediction to generate a diverse ensemble of structural models from small fragments of related and unrelated protein-DNA complexes.The extensive conformational sampling is coupled with sequence space exploration so that binding preferences for the target protein can be inferred from the resulting optimized DNA sequences.

View Article: PubMed Central - PubMed

Affiliation: Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA.

ABSTRACT
Sequence-specific DNA recognition by gene regulatory proteins is critical for proper cellular functioning. The ability to predict the DNA binding preferences of these regulatory proteins from their amino acid sequence would greatly aid in reconstruction of their regulatory interactions. Structural modeling provides one route to such predictions: by building accurate molecular models of regulatory proteins in complex with candidate binding sites, and estimating their relative binding affinities for these sites using a suitable potential function, it should be possible to construct DNA binding profiles. Here, we present a novel molecular modeling protocol for protein-DNA interfaces that borrows conformational sampling techniques from de novo protein structure prediction to generate a diverse ensemble of structural models from small fragments of related and unrelated protein-DNA complexes. The extensive conformational sampling is coupled with sequence space exploration so that binding preferences for the target protein can be inferred from the resulting optimized DNA sequences. We apply the algorithm to predict binding profiles for a benchmark set of eleven C2H2 zinc finger transcription factors, five of known and six of unknown structure. The predicted profiles are in good agreement with experimental binding data; furthermore, examination of the modeled structures gives insight into observed binding preferences.

Show MeSH