Nunung Yuniarti

PyPLIF: Python-based Protein-Ligand Interaction Fingerprinting

Structure-based virtual screening (SBVS) methods often rely on docking score. The docking score is an over-simplification of the actual ligand-target binding. Its capability to model and predict the actual binding reality is limited. Recently, interaction fingerprinting (IFP) has come and offered us an alternative way to model reality. IFP provides us an alternate way to examine protein-ligand interactions. The docking score indicates the approximate affinity and IFP shows the interaction specificity. IFP is a method to convert three dimensional (3D) protein-ligand interactions into one dimensional (1D) bitstrings. The bitstrings are subsequently employed to compare the protein-ligand interaction predicted by the docking tool against the reference ligand. These comparisons produce scores that can be used to enhance the quality of SBVS campaigns. However, some IFP tools are either proprietary or using a proprietary library, which limits the access to the tools and the development of customized IFP algorithm. Therefore, we have developed PyPLIF, a Python-based open source tool to analyze IFP. In this article, we describe PyPLIF and its application to enhance the quality of SBVS in order to identify antagonists for estrogen α receptor (ERα). Availability PyPLIF is freely available at http://code.google.com/p/pyplif

The importance of ARG513 as a hydrogen bond anchor to discover COX-2 inhibitors in a virtual screening campaign

Structure-based virtual screening (SBVS) protocols were developed to find cyclooxygenase-2 (COX-2) inhibitors using the Protein-Ligand ANT System (PLANTS) docking software. The directory of useful decoys (DUD) dataset for COX-2 was used to retrospectively validate the protocols; the DUD consists of 426 known inhibitors in 13289 decoys. Based on criteria used in the article describing DUD datasets, the default protocol showed poor results. However, having ARG513 as a hydrogen bond anchor increased the quality of the SBVS protocol. The modified protocol showed results that could be well considered, with a maximum enrichment factor (EFmax) value of 32.2.

Prenatal exposure to suberoylanilide hydroxamic acid perturbs corticogenesis

Suberoylanilide hydroxamic acid (SAHA) is one of the epidrugs developed for cancer treatment that works epigenetically by inhibiting histone deacetylases (HDACs). SAHA has been reported to diffuse across the placenta and found in fetal plasma in preclinical study, implying that it can influence fetus if taken by pregnant cancer patients. However, report regarding this aspect and the study of in utero HDAC inhibition by SAHA especially on fate specification of neural stem/progenitor cells within the developing mammalian cortex, is yet unavailable. Here we show that transient exposure of SAHA to mouse embryos during prominent neurogenic period resulted in an enhancement of cortical neurogenesis, which is accompanied by an increased expression of proneuronal transcription factor Neurog1. Neurogenesis was enhanced due to the increase number of proliferating Tbr2+ intermediate progenitor cells following SAHA exposure. In this relation, we observed that SAHA perturbed neonatal cortical lamination because of the increased production of Cux1+ and Satb2+ upper-layer neurons, and decreased that of Ctip2+ deep-layer neurons. Furthermore, an upper-layer neuronal lineage determinant Satb2 was also up-regulated, whereas those of deep-layer ones Fezf2 and Ctip2 were down-regulated by SAHA treatment. Taken together, our study suggests that proper regulation of HDACs is important for precise embryonic corticogenesis.

PyPLIF-ASSISTED REDOCKING INDOMETHACIN-(R)-ALPHA-ETHYL-ETHANOLAMIDE INTO CYCLOOXYGENASE-1

Identification of Protein-Ligand Interaction Fingerprints (PLIF) has been performed as the rescoring strategy to identify the best pose for the docked poses of indomethacin-(R)-α-ethyl-etanolamide (IMM) in the binding site of cyclooxygenase-1 (COX-1) from simulations using PLANTS molecular docking software version 1.2 (PLANTS1.2). Instead of using the scoring functions included in the docking software, the strategy presented in this article used external software called PyPLIF that could identify the interactions of the ligand to the amino acid residues in the binding pocket and presents them as binary bitstrings, which subsequently were compared to the interaction bitstrings of the co-crystal ligand pose. The results show that PyPLIF-assisted redocking strategy could select the correct pose much better compared to the pose selection without rescoring. Out of 1000 iterative attempts, PyPLIFassisted redocking simulations could identify 971 correct poses (more than 95%), while the redocking simulations without PyPLIF could only identify 500 correct poses (50%).These works have also provided us with the initial step of the construction of a valid Structure-Based Virtual Screening (SBVS) protocol to identify COX-1 inhibitors.