The peptidyl-prolyl cis-trans isomerase, Pin1, acts as a unified signaling hub that is exploited in cancer to activate oncogenes and inactivate tumor suppressors, in particular through up-regulation of c-Myc target genes. However, despite considerable efforts, Pin1 has remained an elusive drug target. Here, we screened an electrophilic fragment library to discover covalent inhibitors targeting Pin1's active site nucleophile -Cys113, leading to the development of Sulfopin, a double-digit nanomolar Pin1 inhibitor. Sulfopin is highly selective for Pin1, as validated by two independent chemoproteomics methods, achieves potent cellular and in vivo target engagement, and phenocopies genetic knockout of Pin1. Although Pin1 inhibition had a modest effect on viability in cancer cell cultures, Sulfopin induced downregulation of c-Myc target genes and reduced tumor initiation and tumor progression in murine and zebrafish models of MYCN-driven neuroblastoma. Our results suggest that Sulfopin is a suitable chemical probe for assessing Pin1dependent pharmacology in cells and in vivo. Moreover, these studies indicate that Pin1 should be further investigated as a potential cancer target.

Many applications need to meet diverse requirements of a large-scale distributed user group. That challenges the current requirements engineering techniques. Crowd-based requirements engineering was proposed as an umbrella term for dealing with the requirements development in the context of the large-scale user group. However, there are still many issues. Among others, a key issue is how to merge these requirements to produce the synthesized requirements description when a set of requirements descriptions from different participants are received. Appropriate techniques are needed for supporting the requirements synthesis. Diagrams are widely used in industry to represent requirements. This paper chooses the activity diagrams and proposes a novel approach for the activity diagram synthesis which adopts the genetic algorithm to repeatedly modify a population of individual solutions toward an optimal solution. As a result, it can automatically generate a resulting diagram which combines the commonalities as many as possible while leveraging the variabilities of a set of input diagrams. The approach is featured by: 1) the labelled graph proposed as the representation of the candidate solutions during the iterative evolution; 2) the generalized entropy proposed and defined as the measurement of the solutions; 3) the genetic algorithm designed for sorting out the high-quality solution. Four cases of different scales are used to evaluate the effectiveness of the approach. The experimental results show that not only the approach gets high precision and recall but also the resulting diagram satisfies the properties of minimization and information preservation and can support the requirements traceability.