A Multi-Resolution Graph Convolution Network for Contiguous Epitope Prediction

Computational methods for predicting binding interfaces between antigens and antibodies (epitopes and paratopes) are faster and cheaper than traditional experimental structure determination methods. A sufficiently reliable computational predictor that could scale to large sets of available antibody sequence data could thus inform and expedite many biomedical pursuits, such as better understanding immune responses to vaccination and natural infection and developing better drugs and vaccines. However, current state-of-the-art predictors produce discontiguous predictions, e.g., predicting the epitope in many different spots on an antigen, even though in reality they typically comprise a single localized region. We seek to produce contiguous predicted epitopes, accounting for long-range spatial relationships between residues. We therefore build a novel Graph Convolution Network (GCN) that performs graph convolutions at multiple resolutions so as to represent and constrain long-range spatial dependencies. In evaluation on a standard epitope prediction benchmark, we see a significant boost with the multi-resolution approach compared to a previous state-of-the-art GCN predictor, with half of the test cases increasing in AUC-PR by an average of 0.15 and the other half decreasing by only 0.05. We further introduce a clustering algorithm that takes advantage of the contiguity yielded by our model, grouping the raw predictions into a small set of discrete potential epitopes. We show that within the top 3 clusters, 73% of test cases contain a cluster covering most of the actual epitope, demonstrating the utility of contiguous predictions for guiding experimental methods by yielding a small set of reasonable hypotheses for further investigation

Characterising the original anti-C5 function-blocking antibody, BB5.1, for species specificity, mode of action and interactions with C5

The implication of complement in multiple diseases over the last twenty years has fuelled interest in developing anti‐complement drugs. To date, the focus has been on C5; blocking cleavage of C5 prevents formation of two pro‐inflammatory activities, C5a anaphylatoxin and membrane attack complex. The concept of C5 blockade to inhibit inflammation dates back thirty years to the description of BB5.1, an anti‐C5 blocking monoclonal antibody raised in C5‐deficient mice. This antibody proved an invaluable tool to demonstrate complement involvement in mouse disease models and catalysed enthusiasm for anti‐complement drug development, culminating in the anti‐human C5 monoclonal antibody ecuizumab, the most successful anti‐complement drug to date, already in the clinic for several rare diseases. Despite its key role in providing proof‐of‐concept for C5 blockade, the mechanism of BB5.1 inhibition remains poorly understood. Here we characterised BB5.1 cross‐species inhibition, C5 binding affinity and chain specificity. BB5.1 efficiently inhibited C5 in mouse serum but not in human or other rodent sera; it prevented C5 cleavage and C5a generation. BB5.1 bound the C5 α‐chain with high affinity and slow off‐rate. BB5.1 complementarity determining regions were obtained and docking algorithms used to predict the likely binding interface on mouse C5

Bioinformatics tools and data resources for assay development of fluid protein biomarkers

Fluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients. Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants. While the great potential of fluid biomarkers is recognized, their implementation in routine clinical use has been slow. One major obstacle is the often unsuccessful translation of biomarker candidates from explorative high-throughput techniques to sensitive antibody-based immunoassays. In this review, we propose the incorporation of bioinformatics into the workflow of novel immunoassay development to overcome this bottleneck and thus facilitate the development of novel biomarkers towards clinical laboratory practice. Due to the rapid progress within the field of bioinformatics many freely available and easy-to-use tools and data resources exist which can aid the researcher at various stages. Current prediction methods and databases can support the selection of suitable biomarker candidates, as well as the choice of appropriate commercial affinity reagents. Additionally, we examine methods that can determine or predict the epitope - an antibody’s binding region on its antigen - and can help to make an informed choice on the immunogenic peptide used for novel antibody production. Selected use cases for biomarker candidates help illustrate the application and interpretation of the introduced tools

Improving B-cell epitope prediction and its application to global antibody-antigen docking.

MOTIVATION: Antibodies are currently the most important class of biopharmaceuticals. Development of such antibody-based drugs depends on costly and time-consuming screening campaigns. Computational techniques such as antibody-antigen docking hold the potential to facilitate the screening process by rapidly providing a list of initial poses that approximate the native complex. RESULTS: We have developed a new method to identify the epitope region on the antigen, given the structures of the antibody and the antigen-EpiPred. The method combines conformational matching of the antibody-antigen structures and a specific antibody-antigen score. We have tested the method on both a large non-redundant set of antibody-antigen complexes and on homology models of the antibodies and/or the unbound antigen structure. On a non-redundant test set, our epitope prediction method achieves 44% recall at 14% precision against 23% recall at 14% precision for a background random distribution. We use our epitope predictions to rescore the global docking results of two rigid-body docking algorithms: ZDOCK and ClusPro. In both cases including our epitope, prediction increases the number of near-native poses found among the top decoys. AVAILABILITY AND IMPLEMENTATION: Our software is available from http://www.stats.ox.ac.uk/research/proteins/resources

Improving B-cell epitope prediction and its application to global antibody-antigen docking.

MOTIVATION: Antibodies are currently the most important class of biopharmaceuticals. Development of such antibody-based drugs depends on costly and time-consuming screening campaigns. Computational techniques such as antibody-antigen docking hold the potential to facilitate the screening process by rapidly providing a list of initial poses that approximate the native complex. RESULTS: We have developed a new method to identify the epitope region on the antigen, given the structures of the antibody and the antigen-EpiPred. The method combines conformational matching of the antibody-antigen structures and a specific antibody-antigen score. We have tested the method on both a large non-redundant set of antibody-antigen complexes and on homology models of the antibodies and/or the unbound antigen structure. On a non-redundant test set, our epitope prediction method achieves 44% recall at 14% precision against 23% recall at 14% precision for a background random distribution. We use our epitope predictions to rescore the global docking results of two rigid-body docking algorithms: ZDOCK and ClusPro. In both cases including our epitope, prediction increases the number of near-native poses found among the top decoys. AVAILABILITY AND IMPLEMENTATION: Our software is available from http://www.stats.ox.ac.uk/research/proteins/resources

Vaccine Development

Vaccination is the most effective and scientifically based means of protection against infectious diseases, especially in this era of the COVID-19 pandemic. This book examines several issues related to the development of vaccines against viral, bacterial, and parasitic infections

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates

Bayesian inference of virus evolutionary models from next-generation sequencing data

There is a rich tradition in mathematical biology of modeling virus population dynamics within hosts. Such models can reproduce trends in the progression of viral infections such as HIV-1, and have also generated insights on the emergence of drug resistance and treatment strategies. Existing mathematical work has focused on the problem of predicting dynamics given model parameters. The problem of estimating model parameters from observed data has received little attention. One reason is likely the historical difficulty of obtaining high-resolution samples of virus diversity within hosts. Now, next-generation sequencing (NGS) approaches developed in the past decade can supply such data. This thesis presents two Bayesian methods that harness classical models to generate testable hypotheses from NGS datasets. The quasispecies equilibrium explains genetic variation in virus populations as a balance between mutation and selection. We use this model to infer fitness effects of individual mutations and pairs of interacting mutations. Although our method provides a high resolution and accurate picture of the fitness landscape when equilibrium holds, we demonstrate the common observation of populations with coexisting, divergent viruses is unlikely to be consistent with equilibrium. Our second statistical method estimates virus growth rates and binding affinity between viruses and antibodies using the generalized Lotka-Volterra model. Immune responses can explain coexistence of abundant virus variants and their trajectories through time. Additionally, we can draw inferences about immune escape and antibody genetic variants responsible for improved virus recognition