Structure–Activity Relationships through Sequencing (StARTS) Defines Optimal and Suboptimal RNA Motif Targets for Small Molecules

Velagapudi, Sai Pradeep; Seedhouse, Steven J.; Disney, Matthew D.

doi:10.1002/anie.200907257

Cited by 57 publications

(90 citation statements)

References 10 publications

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“…128 A, each RNA motif has statistically significant features that contribute to binding. The features are identified by comparing the rate of occurrence in selected RNAs compared to the rate of occurrence in the RNA library from which the motif was selected.…”

Section: Figurementioning

confidence: 99%

Methods to enable the design of bioactive small molecules targeting RNA

2014

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RNA is an immensely important target for small molecule therapeutics or chemical probes of function. However, methods that identify, annotate, and optimize RNA-small molecule interactions that could enable the design of compounds that modulate RNA function are in their infancies. This review describes recent approaches that have been developed to understand and optimize RNA motif-small molecule interactions, including Structure-Activity Relationships Through Sequencing (StARTS), quantitative structure-activity relationships (QSAR), chemical similarity searching, structure-based design and docking, and molecular dynamics (MD) simulations. Case studies described include the design of small molecules targeting RNA expansions, the bacterial A-site, viral RNAs, and telomerase RNA. These approaches can be combined to afford a synergistic method to exploit the myriad of RNA targets in the transcriptome.

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Section: Figurementioning

confidence: 99%

Methods to enable the design of bioactive small molecules targeting RNA

2014

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“…Velagapudi et al 123 reported a new method called Inforna for sequence-based design of SMIR to target pre-miRs. Inforna integrated a selection-based strategy (Two-Dimensional Combinatorial Screening; 2DCS), 124 a statistical approach (Structure-Activity Relationship through Sequencing; StARTS), 125,126 and the structural information about the RNA target of interest that identified RNA motifs that positively and negatively contributed to binding. After screening and optimization, they selected three compounds for miR-96 precursor, miR-210 precursor, and miR-182 precursor, respectively.…”

Section: Small Molecule Mirna Therapeutic Agentsmentioning

confidence: 99%

Small molecules targeting microRNA for cancer therapy: Promises and obstacles

Wen

Danquah

Chaudhary

et al. 2015

Journal of Controlled Release

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Aberrant expression of miRNAs is critically implicated in cancer initiation and progression. Therapeutic approaches focused on regulating miRNAs are therefore a promising approach for treating cancer. Antisense oligonucleotides, miRNA sponges, and CRISPR/Cas9 genome editing systems are being investigated as tools for regulating miRNAs. Despite the accruing insights in the use of these tools, delivery concerns have mitigated clinical application of such systems. In contrast, little attention has been given to the potential of small molecules to modulate miRNA expression for cancer therapy. In these years, many researches proved that small molecules targeting cancer-related miRNAs might have greater potential for cancer treatment. Small molecules targeting cancer related miRNAs showed significantly promising results in different cancer models. However, there are still several obstacles hindering the progress and clinical application in this area. This review discusses the development, mechanisms and application of small molecules for modulating oncogenic miRNAs (oncomiRs). Attention has also been given to screening technologies and perspectives aimed to facilitate clinical translation for small molecule-based miRNA therapeutics.

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“…7 RNA-PSP compares features in 1 (such as a GC step) to the features in selected motifs. A Z-score (which can be converted to the corresponding two-tailed p -value) is computed, which is a measure of statistical confidence that a feature in a selected motif is truly “privileged” for binding to the small molecule (Fig.…”

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confidence: 99%

“…Previous studies have shown that RNAs with more statistically significant features that contribute positively to molecular recognition are higher affinity. 7, 8 In fact, affinity scales with the sum of the Z-scores for each statistically significant feature, or Sum Z-score. The range of Sum Z-scores for G Neo B and G Kan A are shown in Fig.…”

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confidence: 99%