In the version of this caption initially published, the cover artwork was credited to Erin Dewalt, based on imagery from the author, rather than stating that it was created by Michael B. Battles and the design was by Erin Dewalt. The error has been corrected in the HTML and PDF versions of the caption. ERRATUM In the version of this article initially published, the genus name 'Mycoplasma' was incorrectly used in place of the correct 'Mycobacterium'. The error has been corrected in the HTML and PDF versions of the article. ERRATUM npg

No abstract

Advanced ovarian cancers are a leading cause of cancer-related death in women. Such cancers are currently treated with surgery and chemotherapy which is often temporarily successful but exhibits a high rate of relapse after which treatment options are few. Here we assess the responses of a panel of patient-derived ovarian cancer xenografts (PDXs) to 19 mono and combination therapies, including small molecules and antibody-drug conjugates. The PDX panel aimed to mimic the heterogeneity of disease observed in patients, and exhibited a distribution of responsiveness to standard of care chemotherapy similar to human clinical data. Three monotherapies and one drug combination were found to be active in different subsets of PDXs. By analyzing gene expression data we identified gene expression biomarkers predictive of responsiveness to each of three novel targeted therapy regimens.While no single treatment had as high a response rate as chemotherapy, nearly 90% of PDXs were eligible for and responded to at least one biomarker-guided treatment, including tumors resistant to standard chemotherapy. Biomarker frequency was similar in human patients, suggesting the possibility of a new therapeutic approach to ovarian cancer and demonstrating the potential power of PDX-based trials in broadening the reach of precision cancer medicine..

In the version of this caption initially published, the cover artwork was credited to Erin Dewalt, based on imagery from the author, rather than stating that it was created by Michael B. Battles and the design was by Erin Dewalt. The error has been corrected in the HTML and PDF versions of the caption. ERRATUMIn the version of this article originally published online, the schematic for the construct in Figure 4a was incorrect. A corrected figure has been provided in the HTML and PDF versions of the article.

Proteins execute various activities required by biological cells. Further, they structurally support and promote important biochemical reactions which functionally are sparked by active-sites. Active-sites are regions where reactions and binding events take place directly; they foster protein purpose. Describing functional relationships depends on factors that incorporate sequence, structure, and the biochemical properties of amino acids that form proteins. Our approach to active-site description is computational, and many other approaches utilizing available protein data fall short of ideal. Successful recognition of functional interactions is crucial to advancements in protein annotation and the bioinformatics field at large. This research outlines our Multiple Structure Torsion Angle Alignment (msTALI) as a suitable strategy for addressing active-site identification by comparing results to other existing methods. Specifically, we address the precision of msTALI across three protein families. Our target proteins are PDBIDs 1A2B, 1B4V, 1B8S, 1COY, 1CXZ, 3COX, 1D7E, 1DPF, 1F9I, 1FTN, 1IJH, 1KOU, 1NWZ, 2PHY, and 1SIC.