In many cases the binding site of a ligand with its target is known from experiments before docking approaches are used. In this case, the search space can be limited when looking for therapeutic ligands. As a consequence, the speed of screening is enhanced. In other cases, a therapeutic ligand shows good effects on the target but no or limited structural information of that target is available, especially in the presence of the ligand. Consequently, novel strategies of how to approach this issue with existing software is of need.
Up to nine putative therapeutic ligands are used in various docking protocols, which also includes the application of different docking software, searching for unknown binding sites on the target. The target is the viral channel forming protein (viroporin) p7 of hepatitis C virus. Protein p7 consists of two transmembrane domains separated by a short loop. In its functional form it is shown to exist genotype dependent as a homo hexamer or heptamer. The protein is vital to the survival of the virus.
The discovery of bioactive small molecules is an expensive and time consuming yet central task in drug discovery. A potentially superior way to identify new drug candidates is the selective optimization of side activities (SOSA), which employs known drugs for their side-activities as lead compounds, while diminishing their original activity. Virtually every small molecule drug interacts with more than one molecular target and, thus, has side-activities.
We have observed such side-activity for the cysteinyl leukotriene receptor 1 (CysLT1R) antagonist cinalukast on the nuclear peroxisome proliferator-activated receptor α (PPARα). We chose this synthetically challenging experimental drug to study whether the application of well-established computational optimization and ranking methods can help identify the most promising variations for SOSA and reduce the synthetic efforts needed in this lead optimization concept. We employed SeeSAR to visualize the critical parts for supportive or undesirable interactions with the nuclear receptor. In a proof-of-concept study, we confirmed the suitability of the HYDE ranking for the task of compound prioritization concerning potency on PPARα and screened an automatically generated virtual library of approximately 8000 close cinalukast analogues using a self-designed KNIME® workflow with FlexX and HYDE. The top-ranking molecules from this first aspect of SOSA were then computationally studied for CysLT1R antagonism using a random forest model trained on fingerprint representations of known CysLT1R antagonists. A computationally favoured cinalukast analogue was synthesized and its in vitro profiling confirmed the predicted activity shift towards higher activation efficacy on PPARα and markedly improved selectivity over CysLT1R compared to the lead compound.
Modification of SMN2 exon 7 (E7) splicing is a validated therapeutic strategy against Spinal Muscular Atrophy (SMA). However, a target-based approach to identify small-molecule E7 splicing modifiers has not been attempted, which could reveal novel therapies with improved mechanistic insight. Here we chose as a target the stem-loop RNA structure TSL2, which overlaps with the 5' splicing site of E7. A small-molecule TSL2-binding compound, homocarbonyltopsentin (PK4C9), was identified that increases E7 splicing to therapeutic levels and rescues downstream molecular alterations in SMA cells. High-resolution NMR combined with molecular modelling revealed that PK4C9 binds to pentaloop conformations of TSL2 and promotes a shift to triloop conformations that display enhanced E7 splicing. Collectively, our study validates TSL2 as a target for small-molecule drug discovery in SMA, identifies a novel mechanism of action for an E7 splicing modifier, and sets a precedent for other splicing-mediated diseases where RNA structure could be similarly targeted.
To get you quickly going with all the new features in the latest major SeeSAR release, we will give a tutorial style webinar. We will show you the most prominent use cases of SeeSAR which help to save time in a plethora of drug discovery applications: exploring proteins, finding binding sites, placing ligands in binding sites, ideation, optimizing ligands in binding sites, improving affinity and ADME/T properties, circumnavigating difficult cores and many more.