ALBERT

Description: Vaccines and immunotherapies depend on the ability of antibodies to sensitively and specifically recognize particular antigens and specific epitopes on those antigens. As such, detailed characterization of antibody–antigen binding provides important information to guide development. Due to the time and expense required, high-resolution structural characterization techniques are typically used sparingly and late in a development process. Here, we show that antibody–antigen binding can be characterized early in a process for whole panels of antibodies by combining experimental and computational analyses of competition between monoclonal antibodies for binding to an antigen. Experimental “epitope binning” of monoclonal antibodies uses high-throughput surface plasmon resonance to reveal which antibodies compete, while a new complementary computational analysis that we call “dock binning” evaluates antibody–antigen docking models to identify why and where they might compete, in terms of possible binding sites on the antigen. Experimental and computational characterization of the identified antigenic hotspots then enables the refinement of the competitors and their associated epitope binding regions on the antigen. While not performed at atomic resolution, this approach allows for the group-level identification of functionally related monoclonal antibodies (i.e., communities) and identification of their general binding regions on the antigen. By leveraging extensive epitope characterization data that can be readily generated both experimentally and computationally, researchers can gain broad insights into the basis for antibody–antigen recognition in wide-ranging vaccine and immunotherapy discovery and development programs.

Description: Although cancer is the leading cause of disease-related mortality in children, the relative rarity of pediatric cancers poses a significant challenge for developing novel therapeutics to further improve prognosis. Patient-derived xenograft (PDX) models, which are usually developed from high-risk tumors, are a useful platform to study molecular driver events, identify biomarkers and prioritize therapeutic agents. Here, we develop PDX for Childhood Cancer Therapeutics (PCAT), a new integrated portal for pediatric cancer PDX models. Distinct from previously reported PDX portals, PCAT is focused on pediatric cancer models and provides intuitive interfaces for querying and data mining. The current release comprises 324 models and their associated clinical and genomic data, including gene expression, mutation and copy number alteration. Importantly, PCAT curates preclinical testing results for 68 models and 79 therapeutic agents manually collected from individual agent testing studies published since 2008. To facilitate comparisons of patterns between patient tumors and PDX models, PCAT curates clinical and molecular data of patient tumors from the TARGET project. In addition, PCAT provides access to gene fusions identified in nearly 1000 TARGET samples. PCAT was built using R-shiny and MySQL. The portal can be accessed at http://pcat.zhenglab.info or http://www.pedtranscriptome.org.

Description: The use of mobile devices, especially smartphones, has become popular in recent years. There is an increasing need for cross-device interaction techniques that seamlessly integrate mobile devices and large display devices together. This paper develops a novel cross-device cursor position system that maps a mobile device’s movement on a flat surface to a cursor’s movement on a large display. The system allows a user to directly manipulate objects on a large display device through a mobile device and supports seamless cross-device data sharing without physical distance restrictions. To achieve this, we utilize sound localization to initialize the mobile device position as the starting location of a cursor on the large screen. Then, the mobile device’s movement is detected through an accelerometer and is accordingly translated to the cursor’s movement on the large display using machine learning models. In total, 63 features and 10 classifiers were employed to construct the machine learning models for movement detection. The evaluation results have demonstrated that three classifiers, in particular, gradient boosting, linear discriminant analysis (LDA), and naïve Bayes, are suitable for detecting the movement of a mobile device.