Human monoclonal antibodies that neutralize anthrax toxin by inhibiting heptamer assembly

A panel of human anti-anthrax protective antigen IgG1 monoclonal antibodies were evaluated to determine the mechanism of toxin neutralization. AVP-22G12, AVP-1C6 and AVP-21D9 bound to the protective antigen with picomolar affinities to distinct non-overlapping linear epitopes. Two of the antibodies neutralized the anthrax toxin by completely inhibiting the protective antigen oligomer assembly process in vitro

Fast calibrated additive quantile regression

We propose a novel framework for fitting additive quantile regression models, which provides well calibrated inference about the conditional quantiles and fast automatic estimation of the smoothing parameters, for model structures as diverse as those usable with distributional GAMs, while maintaining equivalent numerical efficiency and stability. The proposed methods are at once statistically rigorous and computationally efficient, because they are based on the general belief updating framework of Bissiri et al. (2016) to loss based inference, but compute by adapting the stable fitting methods of Wood et al. (2016). We show how the pinball loss is statistically suboptimal relative to a novel smooth generalisation, which also gives access to fast estimation methods. Further, we provide a novel calibration method for efficiently selecting the 'learning rate' balancing the loss with the smoothing priors during inference, thereby obtaining reliable quantile uncertainty estimates. Our work was motivated by a probabilistic electricity load forecasting application, used here to demonstrate the proposed approach. The methods described here are implemented by the qgam R package, available on the Comprehensive R Archive Network (CRAN)

Human anti-anthrax protective antigen neutralizing monoclonal antibodies derived from donors vaccinated with anthrax vaccine adsorbed

BACKGROUND: Potent anthrax toxin neutralizing human monoclonal antibodies were generated from peripheral blood lymphocytes obtained from Anthrax Vaccine Adsorbed (AVA) immune donors. The anti-anthrax toxin human monoclonal antibodies were evaluated for neutralization of anthrax lethal toxin in vivo in the Fisher 344 rat bolus toxin challenge model. METHODS: Human peripheral blood lymphocytes from AVA immunized donors were engrafted into severe combined immunodeficient (SCID) mice. Vaccination with anthrax protective antigen and lethal factor produced a significant increase in antigen specific human IgG in the mouse serum. The antibody producing lymphocytes were immortalized by hybridoma formation. The genes encoding the protective antibodies were rescued and stable cell lines expressing full-length human immunoglobulin were established. The antibodies were characterized by; (1) surface plasmon resonance; (2) inhibition of toxin in an in vitro mouse macrophage cell line protection assay and (3) in vivo in a Fischer 344 bolus lethal toxin challenge model. RESULTS: The range of antibodies generated were diverse with evidence of extensive hyper mutation, and all were of very high affinity for PA83~1 × 10(-10-11)M. Moreover all the antibodies were potent inhibitors of anthrax lethal toxin in vitro. A single IV dose of AVP-21D9 or AVP-22G12 was found to confer full protection with as little as 0.5× (AVP-21D9) and 1× (AVP-22G12) molar equivalence relative to the anthrax toxin in the rat challenge prophylaxis model. CONCLUSION: Here we describe a powerful technology to capture the recall antibody response to AVA vaccination and provide detailed molecular characterization of the protective human monoclonal antibodies. AVP-21D9, AVP-22G12 and AVP-1C6 protect rats from anthrax lethal toxin at low dose. Aglycosylated versions of the most potent antibodies are also protective in vivo, suggesting that lethal toxin neutralization is not Fc effector mediated. The protective effect of AVP-21D9 persists for at least one week in rats. These potent fully human anti-PA toxin-neutralizing antibodies are attractive candidates for prophylaxis and/or treatment against Anthrax Class A bioterrorism toxins

HIV envelope trimer-elicited autologous neutralizing antibodies bind a region overlapping the N332 glycan supersite

To date, immunization studies of rabbits with the BG505 SOSIP.664 HIV envelope glycoprotein trimers have revealed the 241/289 glycan hole as the dominant neutralizing antibody epitope. Here, we isolated monoclonal antibodies from a rabbit that did not exhibit glycan hole–dependent autologous serum neutralization. The antibodies did not compete with a previously isolated glycan hole–specific antibody but did compete with N332 glycan supersite broadly neutralizing antibodies. A 3.5-Å cryoEM structure of one of the antibodies in complex with the BG505 SOSIP.v5.2 trimer demonstrated that while the epitope recognized overlapped the N332 glycan supersite by contacting the GDIR motif at the base of V3, primary contacts were located in the variable V1 loop. These data suggest that strain-specific responses to V1 may interfere with broadly neutralizing responses to the N332 glycan supersite and vaccine immunogens may require engineering to minimize these off-target responses or steer them toward a more desirable pathway

Large Scale Application of Neural Network Based Semantic Role Labeling for Automated Relation Extraction from Biomedical Texts

To reduce the increasing amount of time spent on literature search in the life sciences, several methods for automated knowledge extraction have been developed. Co-occurrence based approaches can deal with large text corpora like MEDLINE in an acceptable time but are not able to extract any specific type of semantic relation. Semantic relation extraction methods based on syntax trees, on the other hand, are computationally expensive and the interpretation of the generated trees is difficult. Several natural language processing (NLP) approaches for the biomedical domain exist focusing specifically on the detection of a limited set of relation types. For systems biology, generic approaches for the detection of a multitude of relation types which in addition are able to process large text corpora are needed but the number of systems meeting both requirements is very limited. We introduce the use of SENNA (“Semantic Extraction using a Neural Network Architecture”), a fast and accurate neural network based Semantic Role Labeling (SRL) program, for the large scale extraction of semantic relations from the biomedical literature. A comparison of processing times of SENNA and other SRL systems or syntactical parsers used in the biomedical domain revealed that SENNA is the fastest Proposition Bank (PropBank) conforming SRL program currently available. 89 million biomedical sentences were tagged with SENNA on a 100 node cluster within three days. The accuracy of the presented relation extraction approach was evaluated on two test sets of annotated sentences resulting in precision/recall values of 0.71/0.43. We show that the accuracy as well as processing speed of the proposed semantic relation extraction approach is sufficient for its large scale application on biomedical text. The proposed approach is highly generalizable regarding the supported relation types and appears to be especially suited for general-purpose, broad-scale text mining systems. The presented approach bridges the gap between fast, cooccurrence-based approaches lacking semantic relations and highly specialized and computationally demanding NLP approaches

Electron-Microscopy-Based Epitope Mapping Defines Specificities of Polyclonal Antibodies Elicited during HIV-1 BG505 Envelope Trimer Immunization

Characterizing polyclonal antibody responses via currently available methods is inherently complex and difficult. Mapping epitopes in an immune response is typically incomplete, which creates a barrier to fully understanding the humoral response to antigens and hinders rational vaccine design efforts. Here, we describe a method of characterizing polyclonal responses by using electron microscopy, and we applied this method to the immunization of rabbits with an HIV-1 envelope glycoprotein vaccine candidate, BG505 SOSIP.664. We detected known epitopes within the polyclonal sera and revealed how antibody responses evolved during the prime-boosting strategy to ultimately result in a neutralizing antibody response. We uncovered previously unidentified epitopes, including an epitope proximal to one recognized by human broadly neutralizing antibodies as well as potentially distracting non-neutralizing epitopes. Our method provides an efficient and semiquantitative map of epitopes that are targeted in a polyclonal antibody response and should be of widespread utility in vaccine and infection studies

Using Unsupervised Patterns to Extract Gene Regulation Relationships for Network Construction

BACKGROUND: The gene expression is usually described in the literature as a transcription factor X that regulates the target gene Y. Previously, some studies discovered gene regulations by using information from the biomedical literature and most of them require effort of human annotators to build the training dataset. Moreover, the large amount of textual knowledge recorded in the biomedical literature grows very rapidly, and the creation of manual patterns from literatures becomes more difficult. There is an increasing need to automate the process of establishing patterns. METHODOLOGY/PRINCIPAL FINDINGS: In this article, we describe an unsupervised pattern generation method called AutoPat. It is a gene expression mining system that can generate unsupervised patterns automatically from a given set of seed patterns. The high scalability and low maintenance cost of the unsupervised patterns could help our system to extract gene expression from PubMed abstracts more precisely and effectively. CONCLUSIONS/SIGNIFICANCE: Experiments on several regulators show reasonable precision and recall rates which validate AutoPat's practical applicability. The conducted regulation networks could also be built precisely and effectively. The system in this study is available at http://ikmbio.csie.ncku.edu.tw/AutoPat/

Linguistic feature analysis for protein interaction extraction

<p>Abstract</p> <p>Background</p> <p>The rapid growth of the amount of publicly available reports on biomedical experimental results has recently caused a boost of text mining approaches for protein interaction extraction. Most approaches rely implicitly or explicitly on linguistic, i.e., lexical and syntactic, data extracted from text. However, only few attempts have been made to evaluate the contribution of the different feature types. In this work, we contribute to this evaluation by studying the relative importance of deep syntactic features, i.e., grammatical relations, shallow syntactic features (part-of-speech information) and lexical features. For this purpose, we use a recently proposed approach that uses support vector machines with structured kernels.</p> <p>Results</p> <p>Our results reveal that the contribution of the different feature types varies for the different data sets on which the experiments were conducted. The smaller the training corpus compared to the test data, the more important the role of grammatical relations becomes. Moreover, deep syntactic information based classifiers prove to be more robust on heterogeneous texts where no or only limited common vocabulary is shared.</p> <p>Conclusion</p> <p>Our findings suggest that grammatical relations play an important role in the interaction extraction task. Moreover, the net advantage of adding lexical and shallow syntactic features is small related to the number of added features. This implies that efficient classifiers can be built by using only a small fraction of the features that are typically being used in recent approaches.</p