Back

Targeting miRNA for Colorectal Cancer: In Silico Identification and Physics-based De Novo Modeling of Oncogenic miR-135b for Small Molecule RNA Therapy

Demir, B.; Oktay, L.; Durdagi, S.

2024-06-10 bioinformatics
10.1101/2024.06.08.598084 bioRxiv
Show abstract

Colorectal cancer is one of the leading causes of cancer deaths worldwide. In the current study, we have identified miR-135b, a microRNA to be differentially expressed in colorectal cancer, through an analysis of the differentially expressed miRNAs from the Gene Expression Omnibus database. Subsequently, the target genes associated with miR-135b were pinpointed, and pathway and functional enrichment analyses were performed to gain a comprehensive understanding of the underlying biological processes involved. A de novo three-dimensional model of its tertiary structure was developed for small-molecule targeting at the Dicer cleavage site. Dicer binds to the terminal loop region of the pre-miRNA and cleaves to generate double stranded miRNA duplex. The miRNA duplex is unwound, one of the strands, guide miRNA, is loaded into the RNA-induced silencing complex (RISC) for miRNA-mRNA target interaction and post-transcriptional gene silencing. Following results from molecular docking simulations initiated with the ChemDiv miRNA-targeted small molecule library ([~]20.000 compounds), top-scoring compounds commercial analogues were then searched within the ZINC library using SwissSimilarity. These analogues were docked to the Dicer cleavage site and their optimized docking scores were obtained. These top-scoring molecules were then subject to all-atom molecular dynamics simulations and post-simulation analyses were conducted to assess the dynamic interactions between the miRNA and the selected hit ligands.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.