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The Menu of Features that Define Primary MicroRNAs and Enable De Novo Design of MicroRNA Genes.

ABSTRACT: MicroRNAs (miRNAs) are small regulatory RNAs processed from stem-loop regions of primary transcripts (pri-miRNAs), with the choice of stem loops for initial processing largely determining what becomes a miRNA. To identify sequence and structural features influencing this choice, we determined cleavage efficiencies of >50,000 variants of three human pri-miRNAs, focusing on the regions intractable to previous high-throughput analyses. Our analyses revealed a mismatched motif in the basal stem region, a preference for maintaining or improving base pairing throughout the remainder of the stem, and a narrow stem-length preference of 35 ± 1 base pairs. Incorporating these features with previously identified features, including three primary-sequence motifs, yielded a unifying model defining mammalian pri-miRNAs in which motifs help orient processing and increase efficiency, with the presence of more motifs compensating for structural defects. This model enables generation of artificial pri-miRNAs, designed de novo, without reference to any natural sequence yet processed more efficiently than natural pri-miRNAs.

SUBMITTER: Fang W

PROVIDER: S-EPMC4613790 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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The Menu of Features that Define Primary MicroRNAs and Enable De Novo Design of MicroRNA Genes.

Fang Wenwen W Bartel David P DP

Molecular cell 20150924 1

MicroRNAs (miRNAs) are small regulatory RNAs processed from stem-loop regions of primary transcripts (pri-miRNAs), with the choice of stem loops for initial processing largely determining what becomes a miRNA. To identify sequence and structural features influencing this choice, we determined cleavage efficiencies of >50,000 variants of three human pri-miRNAs, focusing on the regions intractable to previous high-throughput analyses. Our analyses revealed a mismatched motif in the basal stem regi ...[more]

PMID: 26412306

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Project description:We have previously reported the development and evaluation of a computational program to assist in the design of hydrophobic cores of proteins. In an effort to investigate the role of core packing in protein structure, we have used this program, referred to as Repacking of Cores (ROC), to design several variants of the protein ubiquitin. Nine ubiquitin variants containing from three to eight hydrophobic core mutations were constructed, purified, and characterized in terms of their stability and their ability to adopt a uniquely folded native-like conformation. In general, designed ubiquitin variants are more stable than control variants in which the hydrophobic core was chosen randomly. However, in contrast to previous results with 434 cro, all designs are destabilized relative to the wild-type (WT) protein. This raises the possibility that beta-sheet structures have more stringent packing requirements than alpha-helical proteins. A more striking observation is that all variants, including random controls, adopt fairly well-defined conformations, regardless of their stability. This result supports conclusions from the cro studies that non-core residues contribute significantly to the conformational uniqueness of these proteins while core packing largely affects protein stability and has less impact on the nature or uniqueness of the fold. Concurrent with the above work, we used stability data on the nine ubiquitin variants to evaluate and improve the predictive ability of our core packing algorithm. Additional versions of the program were generated that differ in potential function parameters and sampling of side chain conformers. Reasonable correlations between experimental and predicted stabilities suggest the program will be useful in future studies to design variants with stabilities closer to that of the native protein. Taken together, the present study provides further clarification of the role of specific packing interactions in protein structure and stability, and demonstrates the benefit of using systematic computational methods to predict core packing arrangements for the design of proteins.

Dataset Information

The Menu of Features that Define Primary MicroRNAs and Enable De Novo Design of MicroRNA Genes.

Publications

The Menu of Features that Define Primary MicroRNAs and Enable De Novo Design of MicroRNA Genes.

Similar Datasets

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