Novel HDAC8 inhibitors: A multi-computational approach
Abnormal HDAC function triggers irregular gene transcription that hampers the essential cellular activities leading to tumour activation and progression. HDAC inhibition has, therefore, been reported as a potential target for cancer treatment. In the present study, a sequential computational framework was carried out to discover newer lead compounds, namely HDAC8 inhibitors for cancer therapy. Pharmacophoric hypotheses were generated based on hydroxamic acid derivatives reported earlier for HDAC inhibition. The model AAADR.122, demonstrated statistical significance (r2 = 0.93, Q2 = 0.81) and proved robust on validation with a cross-validated correlation coefficient of 0.89. It was utilized to arrive at novel hits through a virtual screening workflow. The specificity of the process was enhanced further by analysing the crucial interactions of the ligands with key catalytic residues, achieved by induced fit docking (PDB ID: 1T64). On assessment, the filtered leads displayed optimal drug like features. Investigations using density functional theory (DFT) also facilitated the recognition of molecular spots in the leads beneficial for HDAC8 interaction. Overall, two leads were proposed for HDAC8 inhibition with potential anti-cancer activity.
Publisher URL: http://www.tandfonline.com/doi/full/10.1080/1062936X.2017.1375978
Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.
Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.