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A protein-RNA specificity code enables targeted activation of an endogenous human transcript.

Campbell ZT, Valley CT, Wickens M - Nat. Struct. Mol. Biol. (2014)

Bottom Line: PUF proteins are an attractive platform for that purpose because they bind specific single-stranded RNA sequences by using short repeated modules, each contributing three amino acids that contact an RNA base.The resulting specificity code reveals the RNA binding preferences of natural proteins and enables the design of new specificities.Our study provides a guide for rational design of engineered mRNA control, including translational stimulation.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA.

ABSTRACT
Programmable protein scaffolds that target DNA are invaluable tools for genome engineering and designer control of transcription. RNA manipulation provides broad new opportunities for control, including changes in translation. PUF proteins are an attractive platform for that purpose because they bind specific single-stranded RNA sequences by using short repeated modules, each contributing three amino acids that contact an RNA base. Here, we identified the specificities of natural and designed combinations of those three amino acids, using a large randomized RNA library. The resulting specificity code reveals the RNA binding preferences of natural proteins and enables the design of new specificities. Using the code and a translational activation domain, we designed a protein that targets endogenous cyclin B1 mRNA in human cells, increasing sensitivity to chemotherapeutic drugs. Our study provides a guide for rational design of engineered mRNA control, including translational stimulation.

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Prediction and distribution of specificity in nature(A) Predictions of specificity in vitro. Prediction of specificity of uncharacterized PUF proteins from Dictyostelium dictyostelium. Motifs depicting specificities were generated from primary sequence (predicted) and compared to experimentally determined motifs by SEQRS (observed). (B) Prediction of and occupancy in vivo. Frequency plots represent predicted specificity from TRM data, a previously described SEQRS analysis of PUM2, an in vivo binding motif derived from photo-crosslinking, or a mock where G bases were replaced with C bases 4,8. (C) The distribution of specificity in natural PUF proteins. The black line denotes a smoothed fit to the observed data. Both TRM repeats and RNA bases have been subjected to extensive mutagenesis in prior work for Puf3p, FBF-2, and Puf4p 19. That data is compiled in the right panel (“Relative mutability”), and reveals the average tolerance of the base or TRM to substitution for each repeat.
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Figure 3: Prediction and distribution of specificity in nature(A) Predictions of specificity in vitro. Prediction of specificity of uncharacterized PUF proteins from Dictyostelium dictyostelium. Motifs depicting specificities were generated from primary sequence (predicted) and compared to experimentally determined motifs by SEQRS (observed). (B) Prediction of and occupancy in vivo. Frequency plots represent predicted specificity from TRM data, a previously described SEQRS analysis of PUM2, an in vivo binding motif derived from photo-crosslinking, or a mock where G bases were replaced with C bases 4,8. (C) The distribution of specificity in natural PUF proteins. The black line denotes a smoothed fit to the observed data. Both TRM repeats and RNA bases have been subjected to extensive mutagenesis in prior work for Puf3p, FBF-2, and Puf4p 19. That data is compiled in the right panel (“Relative mutability”), and reveals the average tolerance of the base or TRM to substitution for each repeat.

Mentions: The TRM specificity code provides RNA-binding preferences for the majority of naturally occurring TRMs (Fig. 1B). We used these data to predict the specificities of two PUF proteins from the slime mold Dictyostelium discoideum (Supplementary Fig. 5), and compared the predicted consensus elements to experimentally determined motifs from SEQRS (Fig. 3A). The in silico predictions correlated well. For example, a single cysteine to threonine mutation in Repeat 3 of PufA versus PufB altered specificity from A to U, as predicted from the code. An “extra” nucleotide is present at position 5 of the DdPufA site. This is likely due to base flipping in which a base is extruded from the binding surface of the protein 9,19,25. Sites of base flipping are not yet predictable computationally (see Discussion).


A protein-RNA specificity code enables targeted activation of an endogenous human transcript.

Campbell ZT, Valley CT, Wickens M - Nat. Struct. Mol. Biol. (2014)

Prediction and distribution of specificity in nature(A) Predictions of specificity in vitro. Prediction of specificity of uncharacterized PUF proteins from Dictyostelium dictyostelium. Motifs depicting specificities were generated from primary sequence (predicted) and compared to experimentally determined motifs by SEQRS (observed). (B) Prediction of and occupancy in vivo. Frequency plots represent predicted specificity from TRM data, a previously described SEQRS analysis of PUM2, an in vivo binding motif derived from photo-crosslinking, or a mock where G bases were replaced with C bases 4,8. (C) The distribution of specificity in natural PUF proteins. The black line denotes a smoothed fit to the observed data. Both TRM repeats and RNA bases have been subjected to extensive mutagenesis in prior work for Puf3p, FBF-2, and Puf4p 19. That data is compiled in the right panel (“Relative mutability”), and reveals the average tolerance of the base or TRM to substitution for each repeat.
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Related In: Results  -  Collection

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Figure 3: Prediction and distribution of specificity in nature(A) Predictions of specificity in vitro. Prediction of specificity of uncharacterized PUF proteins from Dictyostelium dictyostelium. Motifs depicting specificities were generated from primary sequence (predicted) and compared to experimentally determined motifs by SEQRS (observed). (B) Prediction of and occupancy in vivo. Frequency plots represent predicted specificity from TRM data, a previously described SEQRS analysis of PUM2, an in vivo binding motif derived from photo-crosslinking, or a mock where G bases were replaced with C bases 4,8. (C) The distribution of specificity in natural PUF proteins. The black line denotes a smoothed fit to the observed data. Both TRM repeats and RNA bases have been subjected to extensive mutagenesis in prior work for Puf3p, FBF-2, and Puf4p 19. That data is compiled in the right panel (“Relative mutability”), and reveals the average tolerance of the base or TRM to substitution for each repeat.
Mentions: The TRM specificity code provides RNA-binding preferences for the majority of naturally occurring TRMs (Fig. 1B). We used these data to predict the specificities of two PUF proteins from the slime mold Dictyostelium discoideum (Supplementary Fig. 5), and compared the predicted consensus elements to experimentally determined motifs from SEQRS (Fig. 3A). The in silico predictions correlated well. For example, a single cysteine to threonine mutation in Repeat 3 of PufA versus PufB altered specificity from A to U, as predicted from the code. An “extra” nucleotide is present at position 5 of the DdPufA site. This is likely due to base flipping in which a base is extruded from the binding surface of the protein 9,19,25. Sites of base flipping are not yet predictable computationally (see Discussion).

Bottom Line: PUF proteins are an attractive platform for that purpose because they bind specific single-stranded RNA sequences by using short repeated modules, each contributing three amino acids that contact an RNA base.The resulting specificity code reveals the RNA binding preferences of natural proteins and enables the design of new specificities.Our study provides a guide for rational design of engineered mRNA control, including translational stimulation.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA.

ABSTRACT
Programmable protein scaffolds that target DNA are invaluable tools for genome engineering and designer control of transcription. RNA manipulation provides broad new opportunities for control, including changes in translation. PUF proteins are an attractive platform for that purpose because they bind specific single-stranded RNA sequences by using short repeated modules, each contributing three amino acids that contact an RNA base. Here, we identified the specificities of natural and designed combinations of those three amino acids, using a large randomized RNA library. The resulting specificity code reveals the RNA binding preferences of natural proteins and enables the design of new specificities. Using the code and a translational activation domain, we designed a protein that targets endogenous cyclin B1 mRNA in human cells, increasing sensitivity to chemotherapeutic drugs. Our study provides a guide for rational design of engineered mRNA control, including translational stimulation.

Show MeSH