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Quantitative analysis of RNA-protein interactions on a massively parallel array reveals biophysical and evolutionary landscapes.

Buenrostro JD, Araya CL, Chircus LM, Layton CJ, Chang HY, Snyder MP, Greenleaf WJ - Nat. Biotechnol. (2014)

Bottom Line: RNA-protein interactions drive fundamental biological processes and are targets for molecular engineering, yet quantitative and comprehensive understanding of the sequence determinants of affinity remains limited.By analyzing the biophysical constraints and modeling mutational paths describing the molecular evolution of MS2 from low- to high-affinity hairpins, we quantify widespread molecular epistasis and a long-hypothesized, structure-dependent preference for G:U base pairs over C:A intermediates in evolutionary trajectories.Our results suggest that quantitative analysis of RNA on a massively parallel array (RNA-MaP) provides generalizable insight into the biophysical basis and evolutionary consequences of sequence-function relationships.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Genetics, Stanford University School of Medicine, Stanford, California, USA. [2] Program in Epithelial Biology and the Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, USA. [3] These authors contributed equally to this work.

ABSTRACT
RNA-protein interactions drive fundamental biological processes and are targets for molecular engineering, yet quantitative and comprehensive understanding of the sequence determinants of affinity remains limited. Here we repurpose a high-throughput sequencing instrument to quantitatively measure binding and dissociation of a fluorescently labeled protein to >10(7) RNA targets generated on a flow cell surface by in situ transcription and intermolecular tethering of RNA to DNA. Studying the MS2 coat protein, we decompose the binding energy contributions from primary and secondary RNA structure, and observe that differences in affinity are often driven by sequence-specific changes in both association and dissociation rates. By analyzing the biophysical constraints and modeling mutational paths describing the molecular evolution of MS2 from low- to high-affinity hairpins, we quantify widespread molecular epistasis and a long-hypothesized, structure-dependent preference for G:U base pairs over C:A intermediates in evolutionary trajectories. Our results suggest that quantitative analysis of RNA on a massively parallel array (RNA-MaP) provides generalizable insight into the biophysical basis and evolutionary consequences of sequence-function relationships.

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Sequence-specific contributions of association and dissociation rates to binding affinity(a) Fractional contribution of dissociation rates for 31 single and 289 double mutants with measurable affinities and dissociation rates. Positions at the base of the hairpin are highlighted. (b) Δlog(koff) and (c) Δlog(kon) at the base of the hairpin. M2 = number of qualityfiltered double mutants. (d) Distribution of fractional contributions of association (blue, μ=0.57) and dissociation (red, μ=0.43) rates to −ΔΔG for all measured mutants (N=3,029).
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Figure 4: Sequence-specific contributions of association and dissociation rates to binding affinity(a) Fractional contribution of dissociation rates for 31 single and 289 double mutants with measurable affinities and dissociation rates. Positions at the base of the hairpin are highlighted. (b) Δlog(koff) and (c) Δlog(kon) at the base of the hairpin. M2 = number of qualityfiltered double mutants. (d) Distribution of fractional contributions of association (blue, μ=0.57) and dissociation (red, μ=0.43) rates to −ΔΔG for all measured mutants (N=3,029).

Mentions: We sought to quantify how changes in association and dissociation rates contribute to measured −ΔΔG values for all mutants with measurable kinetic data. We calculated the energetic contributions to −ΔΔG from changes in dissociation rates , and inferred the contribution from changes in association rates, . Because Δlog(koff) + Δlog(kon) = −ΔΔG, we treated these parameters as pseudo-energies. Using this decomposition, we examined the fractional contribution of change in dissociation rates to −ΔΔG across single and double mutants (Fig. 4a). At the base of the hairpin, only a small fraction of −ΔΔG measurements are explained by dissociation rate changes. This small effect suggests that mutations at these positions modulate association rates, possibly by causing fraying of the hairpin and/or allowing competition with alternate RNA structures, thereby reducing the per-collision probability of productive binding (see Supplementary Discussion). This interpretation is reinforced by examining Δlog(koff) and Δlog(kon) in this region (Fig. 4b, c). Dissociation rates change little while inferred association rates remain similar to that of the consensus sequence only for structures that maintain base-pairing through compensating mutations. Across all measured variants, we observe a significant population of structures with −ΔΔG driven by association rates (Fig. 4d; P < 2.2 × 10−16, Wilcoxon signed rank test, μ = 0.5). These results suggest the kinetic drivers of observed affinity changes are position-specific and often operate through modulating association rates, likely by changing hairpin stability.


Quantitative analysis of RNA-protein interactions on a massively parallel array reveals biophysical and evolutionary landscapes.

Buenrostro JD, Araya CL, Chircus LM, Layton CJ, Chang HY, Snyder MP, Greenleaf WJ - Nat. Biotechnol. (2014)

Sequence-specific contributions of association and dissociation rates to binding affinity(a) Fractional contribution of dissociation rates for 31 single and 289 double mutants with measurable affinities and dissociation rates. Positions at the base of the hairpin are highlighted. (b) Δlog(koff) and (c) Δlog(kon) at the base of the hairpin. M2 = number of qualityfiltered double mutants. (d) Distribution of fractional contributions of association (blue, μ=0.57) and dissociation (red, μ=0.43) rates to −ΔΔG for all measured mutants (N=3,029).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4414031&req=5

Figure 4: Sequence-specific contributions of association and dissociation rates to binding affinity(a) Fractional contribution of dissociation rates for 31 single and 289 double mutants with measurable affinities and dissociation rates. Positions at the base of the hairpin are highlighted. (b) Δlog(koff) and (c) Δlog(kon) at the base of the hairpin. M2 = number of qualityfiltered double mutants. (d) Distribution of fractional contributions of association (blue, μ=0.57) and dissociation (red, μ=0.43) rates to −ΔΔG for all measured mutants (N=3,029).
Mentions: We sought to quantify how changes in association and dissociation rates contribute to measured −ΔΔG values for all mutants with measurable kinetic data. We calculated the energetic contributions to −ΔΔG from changes in dissociation rates , and inferred the contribution from changes in association rates, . Because Δlog(koff) + Δlog(kon) = −ΔΔG, we treated these parameters as pseudo-energies. Using this decomposition, we examined the fractional contribution of change in dissociation rates to −ΔΔG across single and double mutants (Fig. 4a). At the base of the hairpin, only a small fraction of −ΔΔG measurements are explained by dissociation rate changes. This small effect suggests that mutations at these positions modulate association rates, possibly by causing fraying of the hairpin and/or allowing competition with alternate RNA structures, thereby reducing the per-collision probability of productive binding (see Supplementary Discussion). This interpretation is reinforced by examining Δlog(koff) and Δlog(kon) in this region (Fig. 4b, c). Dissociation rates change little while inferred association rates remain similar to that of the consensus sequence only for structures that maintain base-pairing through compensating mutations. Across all measured variants, we observe a significant population of structures with −ΔΔG driven by association rates (Fig. 4d; P < 2.2 × 10−16, Wilcoxon signed rank test, μ = 0.5). These results suggest the kinetic drivers of observed affinity changes are position-specific and often operate through modulating association rates, likely by changing hairpin stability.

Bottom Line: RNA-protein interactions drive fundamental biological processes and are targets for molecular engineering, yet quantitative and comprehensive understanding of the sequence determinants of affinity remains limited.By analyzing the biophysical constraints and modeling mutational paths describing the molecular evolution of MS2 from low- to high-affinity hairpins, we quantify widespread molecular epistasis and a long-hypothesized, structure-dependent preference for G:U base pairs over C:A intermediates in evolutionary trajectories.Our results suggest that quantitative analysis of RNA on a massively parallel array (RNA-MaP) provides generalizable insight into the biophysical basis and evolutionary consequences of sequence-function relationships.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Genetics, Stanford University School of Medicine, Stanford, California, USA. [2] Program in Epithelial Biology and the Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, USA. [3] These authors contributed equally to this work.

ABSTRACT
RNA-protein interactions drive fundamental biological processes and are targets for molecular engineering, yet quantitative and comprehensive understanding of the sequence determinants of affinity remains limited. Here we repurpose a high-throughput sequencing instrument to quantitatively measure binding and dissociation of a fluorescently labeled protein to >10(7) RNA targets generated on a flow cell surface by in situ transcription and intermolecular tethering of RNA to DNA. Studying the MS2 coat protein, we decompose the binding energy contributions from primary and secondary RNA structure, and observe that differences in affinity are often driven by sequence-specific changes in both association and dissociation rates. By analyzing the biophysical constraints and modeling mutational paths describing the molecular evolution of MS2 from low- to high-affinity hairpins, we quantify widespread molecular epistasis and a long-hypothesized, structure-dependent preference for G:U base pairs over C:A intermediates in evolutionary trajectories. Our results suggest that quantitative analysis of RNA on a massively parallel array (RNA-MaP) provides generalizable insight into the biophysical basis and evolutionary consequences of sequence-function relationships.

Show MeSH
Related in: MedlinePlus