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David Koes edited subsection_HIV_RT_The_best__.tex
over 8 years ago
Commit id: 4f5b18534adeb1f02b982ac658b07feab5cf6ab3
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\subsection*{HIV-RT}
Virtual screening results for HIV reverse transcriptase(HIV-RT) are shown in Figure~\ref{hivrt}. Again, the only method to outperform random selection is RDKit with an AUC of 0.57. However, FOMS displays the best early enrichment up to a true positive rate of 17\% and the shape constraint queries are statistically significant and can identify enriched subsets with the same proficiency as RDKit shape similarity.
The best SMARTS expression and approach for HIV-RT did not perform significantly better than random performance for both methods (cite table). Inhibition of HIV-RT relies on many key interactions, including a stacking interaction with Tyr188, hydrogen-bonding with Lys103, and many important hydrophobic interactions with Val and Phe residues (cite HIV). Single and double mutation varieites of HIV-RT were tested against known inhibitors, with the conserved Tyr residue mutation causing a 600 fold increase in IC50. Our original fragment pre-alignment and SMARTS expression only covered the conserved Lys interaction, however we then performed pre-alignment to the Tyr residue interaction and observed better performance (cite Table). As with most of the other targets in the MUV dataset, there are multiple interactions necessary for proper binding of an HIV-RT inhibitor. FOMS will only cover a small part of the necessary interaction.
Docking with 3DRP in smina for the active compounds from the MUV dataset were performed. This revealed 4 potential pockets to fill, with only one being covered by our fragment pre-alignment. In addition, hydrogen-bonding with Lys103 and potential stacking with Tyr188 were observed frequently. This indicates the importance of both interactions for proper HIV-RT binding. Since only one of the 4 pockets are covered by our fragment pre-alignment, it is possible that this is a contributing factor to the poor performance of FOMS.
Less specific SMARTS expressions led to higher performance for FOMS, whereas more specific SMARTS expressions led to higher performance for VAMS (cite table). (explanation for both?) %include ip figure?
