Virtual screening identifies a PIN1 inhibitor with possible antiovarian cancer effects

Abstract

Peptidyl‐prolyl cis–trans isomerase, NIMA‐interacting 1 (PIN1) is a peptidyl‐prolyl isomerase that binds phospho‐Ser/Thr‐Pro motifs in proteins and catalyzes the cis–trans isomerization of proline peptide bonds. PIN1 is overexpressed in several cancers including high‐grade serous ovarian cancer. Since few therapies are effective against this cancer, PIN1 could be a therapeutic target but effective PIN1 inhibitors are lacking. To identify molecules with in vivo inhibitory effects on PIN1, we used consensus docking to model existing PIN1‐ligand X‐ray structures and to screen a chemical database for candidate inhibitors. Ten molecules were selected and tested in cellular assays, leading to the identification of VS10 that bound and inhibited PIN1. VS10 treatment reduced the viability of ovarian cancer cell lines by inducing proteasomal PIN1 degradation, without effects on PIN1 transcription, and also reduced the levels of downstream targets β‐catenin, cyclin D1, and pSer473‐Akt. VS10 is a selective PIN1 inhibitor that may offer new opportunities for treating PIN1‐overexpressing tumors.

Application of MM-PBSA Methods in Virtual Screening

“…Despite the availability of different types of scoring functions that estimate ligand binding affinities based on different methods [3,4], their ability to discriminate active ligands from inactive compounds for a certain target receptor is often not sufficiently satisfying, even because it is not possible to know a priori the screening performance of a scoring function in ranking ligands of a specific target. For this reason, different strategies have been used in the attempt to overcome these limitations, such as: a) a thorough preliminary evaluation of various docking and scoring methods in order to select the most suitable for the target of interest [5,6]; b) development of target-specific scoring functions [7][8][9]; c) inclusion of ligand-based and pharmacophoric elements within the docking algorithm [10,11]; d) combination of different rescoring methods into a consensus scoring approach [12][13][14], and even combination of different binding poses predicted for each compound by multiple docking methods in order to rank the ligands based on the number of docking procedures predicting the same pose (consensus docking) [15][16][17][18][19]. Moreover, the rapid development and improvement of machine learning and deep learning techniques has recently offered new possibilities in the field of receptor-based drug design; in fact, novel tools and strategies for the binding-affinity prediction and ranking of docked compounds based on random forest models [20], neural networks [21], and other algorithms have emerged in the last few years, often demonstrating an improved performance with respect to classic scoring functions [22].…”

Extensive Reliability Evaluation of Docking-Based Target-Fishing Strategies

“…Moreover, it is worth noting that a high standard deviation (SD), namely a large variability of ranking position values, was observed for every tested docking procedure, indicating that the obtained results were spread out over a wide range of values (Figure 1). This may be ascribed to the intrinsic variability of the docking results in terms of docking poses and scores that are produced by single methods for different ligands and targets, as already observed in our previous validation analyses of docking procedures across different targets [26,27,28].…”

Strategies for Delivery of siRNAs to Ovarian Cancer Cells

“…The net result is the activation of oncogenes and inactivation of tumor suppressor genes in cancer cells. In OC (HGSOC), PIN1 is overexpressed and when knocked down or chemically inhibited, OC cell death is induced [88,89]. The fact that E2F1 regulates PIN1 transcription and that both genes are involved in OC make this molecular circuit an interesting target for novel therapeutic approaches in OC.…”