Structure of thymidylate kinase from Ehrlichia chaffeensis

A 2.15 Å resolution apo structure of thymidylate kinase from E. chaffeensis is reported

High-throughput protein production and purification at the Seattle Structural Genomics Center for Infectious Disease

An overview of the standard SSGCID protein-purification protocol is given and success rates and cleavage alternatives are discussed

Modeling Immune Checkpoint Inhibitor Efficacy in Syngeneic Mouse Tumors in an Ex Vivo Immuno-Oncology Dynamic Environment

The immune checkpoint blockade represents a revolution in cancer therapy, with the potential to increase survival for many patients for whom current treatments are not effective. However, response rates to current immune checkpoint inhibitors vary widely between patients and different types of cancer, and the mechanisms underlying these varied responses are poorly understood. Insights into the antitumor activities of checkpoint inhibitors are often obtained using syngeneic mouse models, which provide an in vivo preclinical basis for predicting efficacy in human clinical trials. Efforts to establish in vitro syngeneic mouse equivalents, which could increase throughput and permit real-time evaluation of lymphocyte infiltration and tumor killing, have been hampered by difficulties in recapitulating the tumor microenvironment in laboratory systems. Here, we describe a multiplex in vitro system that overcomes many of the deficiencies seen in current static histocultures, which we applied to the evaluation of checkpoint blockade in tumors derived from syngeneic mouse models. Our system enables both precision-controlled perfusion across biopsied tumor fragments and the introduction of checkpoint-inhibited tumor-infiltrating lymphocytes in a single experiment. Through real-time high-resolution confocal imaging and analytics, we demonstrated excellent correlations between in vivo syngeneic mouse and in vitro tumor biopsy responses to checkpoint inhibitors, suggesting the use of this platform for higher throughput evaluation of checkpoint efficacy as a tool for drug development

Massively parallel de novo protein design for targeted therapeutics

De novo protein design holds promise for creating small stable proteins with shapes customized to bind therapeutic targets. We describe a massively parallel approach for designing, manufacturing and screening mini-protein binders, integrating large-scale computational design, oligonucleotide synthesis, yeast display screening and next-generation sequencing. We designed and tested 22,660 mini-proteins of 37-43 residues that target influenza haemagglutinin and botulinum neurotoxin B, along with 6,286 control sequences to probe contributions to folding and binding, and identified 2,618 high-affinity binders. Comparison of the binding and non-binding design sets, which are two orders of magnitude larger than any previously investigated, enabled the evaluation and improvement of the computational model. Biophysical characterization of a subset of the binder designs showed that they are extremely stable and, unlike antibodies, do not lose activity after exposure to high temperatures. The designs elicit little or no immune response and provide potent prophylactic and therapeutic protection against influenza, even after extensive repeated dosing

Predicting missing biomarker data in a longitudinal study of Alzheimer disease

Objective:To investigate predictors of missing data in a longitudinal study of Alzheimer disease (AD).Methods:The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a clinic-based, multicenter, longitudinal study with blood, CSF, PET, and MRI scans repeatedly measured in 229 participants with normal cognition (NC), 397 with mild cognitive impairment (MCI), and 193 with mild AD during 2005–2007. We used univariate and multivariable logistic regression models to examine the associations between baseline demographic/clinical features and loss of biomarker follow-ups in ADNI.Results:CSF studies tended to recruit and retain patients with MCI with more AD-like features, including lower levels of baseline CSF Aβ42. Depression was the major predictor for MCI dropouts, while family history of AD kept more patients with AD enrolled in PET and MRI studies. Poor cognitive performance was associated with loss of follow-up in most biomarker studies, even among NC participants. The presence of vascular risk factors seemed more critical than cognitive function for predicting dropouts in AD.Conclusion:The missing data are not missing completely at random in ADNI and likely conditional on certain features in addition to cognitive function. Missing data predictors vary across biomarkers and even MCI and AD groups do not share the same missing data pattern. Understanding the missing data structure may help in the design of future longitudinal studies and clinical trials in AD

Predicting missing biomarker data in a longitudinal study of Alzheimer disease

Objective:To investigate predictors of missing data in a longitudinal study of Alzheimer disease (AD).Methods:The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a clinic-based, multicenter, longitudinal study with blood, CSF, PET, and MRI scans repeatedly measured in 229 participants with normal cognition (NC), 397 with mild cognitive impairment (MCI), and 193 with mild AD during 2005–2007. We used univariate and multivariable logistic regression models to examine the associations between baseline demographic/clinical features and loss of biomarker follow-ups in ADNI.Results:CSF studies tended to recruit and retain patients with MCI with more AD-like features, including lower levels of baseline CSF Aβ42. Depression was the major predictor for MCI dropouts, while family history of AD kept more patients with AD enrolled in PET and MRI studies. Poor cognitive performance was associated with loss of follow-up in most biomarker studies, even among NC participants. The presence of vascular risk factors seemed more critical than cognitive function for predicting dropouts in AD.Conclusion:The missing data are not missing completely at random in ADNI and likely conditional on certain features in addition to cognitive function. Missing data predictors vary across biomarkers and even MCI and AD groups do not share the same missing data pattern. Understanding the missing data structure may help in the design of future longitudinal studies and clinical trials in AD