Minireview Trapping Poly(ADP-Ribose) Polymerase

ABSTRACT Recent findings indicate that a major mechanism by which poly (ADP-ribose) polymerase (PARP) inhibitors kill cancer cells is by trapping PARP1 and PARP2 to the sites of DNA damage. The PARP enzyme-inhibitor complex "locks" onto damaged DNA and prevents DNA repair, replication, and transcription, leading to cell death. Several clinical-stage PARP inhibitors, including veliparib, rucaparib, olaparib, niraparib, and talazoparib, have been evaluated for their PARP-trapping activity. Although they display similar capacity to inhibit PARP catalytic activity, their relative abilities to trap PARP differ by several orders of magnitude, with the ability to trap PARP closely correlating with each drug's ability to kill cancer cells. In this article, we review the available data on molecular interactions between these clinical-stage PARP inhibitors and PARP proteins, and discuss how their biologic differences might be explained by the trapping mechanism. We also discuss how to use the PARP-trapping mechanism to guide the development of PARP inhibitors as a new class of cancer therapy, both for singleagent and combination treatments

Use of Proteomics Analysis for Molecular Precision Approaches in Cancer Therapy

The rapidly expanding data sets derived from genomic and transcriptomic analyses have allowed greater understanding of structural and functional network patterns within the genome resulting in a realignment of thinking within a systems biologic framework of cancer. However, insofar as spatially and temporally dynamic differential gene expression at the protein level is the mediate effector of cellular behavior and, in view of extensive post translational modification (PTM), the need for sensitive, quantitative, and high throughput proteomic analytic techniques has emerged. To circumvent the problems of tissue sample heterogeneity, laser capture microdissection (LCM) allows for the acquisition of homogeneous cell populations. Using different fluorescent dyes to label protein samples prior to gel electrophoresis, 2-D DIGE (two-dimensional differential in-gel electrophoresis) can, with reasonable sensitivity, process three protein samples on the same gel allowing for intragel relative quantification. MudPIT (multidimensional protein identification technology) is a non-gel approach exploiting the unique physical properties of charge and hydrophobicity which allows the separation of peptide mixtures as well as direct MS (mass spectrometry) and database searching. The introduction of iTRAQ (isobaric tags for relative and absolute quantification) achieves labeling of all peptides by employing an 8-plex set of amine reactive tags to derivatize peptides at the N-terminus and lysine side chains allowing for absolute quantification and assessment of PTM. These and other new laboratory technologies, along with improved bioinformatics tools, have started to make significant contributions in cancer diagnostics and treatments

Research on Discharge Sound Recognition Based on Machine Learning and Convolutional Neural Network Training Algorithm

Support Vector Machine (SVM) is a machine learning method based on statistical learning theory and structural risk minimization principle. The selection of many parameters directly affects the performance of SVM. SVM can be used as a classifier for estimating sound source position, and the anti-noise ability of the algorithm can be improved by selecting appropriate parameters. Convolutional neural network (CNN) can directly obtain effective information from the original image, omitting the processes of preprocessing, feature extraction and data reconstruction of the original image, and is highly invariant to displacement, scaling and other forms of distortion. By setting different solver parameters, network structure and the number of training samples, the results of defect recognition are compared and analyzed, and it is found that the improved Alexnet network has strong adaptive learning ability, which provides a new idea for pattern recognition in DC cable fault diagnosis