Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID-19.

The COVID-19 pandemic was characterized by multiple subsequent, overlapping outbreaks, as well as extremely rapid changes in viral genomes. The information about local epidemics spread and the epidemic control measures was shared on a daily basis (number of cases and deaths) via centralized repositories. The vaccines were developed within the first year of the pandemic. New modes of monitoring and sharing of epidemic data were implemented using Internet resources. We modified the basic SEIR compartmental model to include public health measures, multiwave scenarios, and the variation of viral infectivity and transmissibility reflected by the basic reproduction number R0 of emerging viral variants. SVEIR(MH) model considers the capacity of the medical system, lockdowns, vaccination, and changes in viral reproduction rate on the epidemic spread. The developed model uses daily infection reports for assessing the epidemic dynamics, and daily changes of mobility data from mobile phone networks to assess the lockdown effectiveness. This model was deployed to six European regions Baden-Württemberg (Germany), Belgium, Czechia, Lombardy (Italy), Sweden, and Switzerland for the first 2 years of the pandemic. The correlation coefficients between observed and reported infection data showed good concordance for both years of the pandemic (ρ = 0.84-0.94 for the raw data and ρ = 0.91-0.98 for smoothed 7-day averages). The results show stability across the regions and the different epidemic waves. Optimal control of epidemic waves can be achieved by dynamically adjusting epidemic control measures in real-time. SVEIR(MH) model can simulate different scenarios and inform adjustments to the public health policies to achieve the target outcomes. Because this model is highly representative of actual epidemic situations, it can be used to assess both the public health and socioeconomic effects of the public health measures within the first 7 days of the outbreak.

Multi-AP and Test Point Accuracy of the Results in WiFi Indoor Localization.

WiFi-based indoor positioning has attracted intensive research activities. While localization accuracy is steadily improving due to the application of advanced algorithms, the factors that affect indoor localization accuracy have not been sufficiently understood. Most localization algorithms used in changing indoor spaces are Angle-of-Arrival (AoA) based, and they deploy the conventional MUSIC algorithm. The localization accuracy can be achieved by algorithm improvements or joint localization that deploys multiple Access Points (APs). We performed an experiment that assessed the Test Point (TP) accuracy and distribution of results in a complex environment. The testing space was a 290 m2 three-room environment with three APs with 38 TPs. The joint localization using three APs was performed in the same test space. We developed and implemented a new algorithm for improved accuracy of joint localization. We analyzed the statistical characteristics of the results based on each TP and show that the local space-dependent factors are the key factors for localization accuracy. The most important factors that cause errors are distance, obstacles, corner locations, the location of APs, and the angular orientation of the antenna array. Compared with the well-known SpotFi algorithm, we achieved a mean accuracy (across all TPs) improvement of 46%. The unbiased joint localization median accuracy improved by 20% as compared to the best individual localization.

TANTIGEN 2.0: a knowledge base of tumor T cell antigens and epitopes.

We previously developed TANTIGEN, a comprehensive online database cataloging more than 1000 T cell epitopes and HLA ligands from 292 tumor antigens. In TANTIGEN 2.0, we significantly expanded coverage in both immune response targets (T cell epitopes and HLA ligands) and tumor antigens. It catalogs 4,296 antigen variants from 403 unique tumor antigens and more than 1500 T cell epitopes and HLA ligands. We also included neoantigens, a class of tumor antigens generated through mutations resulting in new amino acid sequences in tumor antigens. TANTIGEN 2.0 contains validated TCR sequences specific for cognate T cell epitopes and tumor antigen gene/mRNA/protein expression information in major human cancers extracted by Human Pathology Atlas. TANTIGEN 2.0 is a rich data resource for tumor antigens and their associated epitopes and neoepitopes. It hosts a set of tailored data analytics tools tightly integrated with the data to form meaningful analysis workflows. It is freely available at http://projects.met-hilab.org/tadb .

Elemental metabolomics.

Elemental metabolomics is quantification and characterization of total concentration of chemical elements in biological samples and monitoring of their changes. Recent advances in inductively coupled plasma mass spectrometry have enabled simultaneous measurement of concentrations of > 70 elements in biological samples. In living organisms, elements interact and compete with each other for absorption and molecular interactions. They also interact with proteins and nucleotide sequences. These interactions modulate enzymatic activities and are critical for many molecular and cellular functions. Testing for concentration of > 40 elements in blood, other bodily fluids and tissues is now in routine use in advanced medical laboratories. In this article, we define the basic concepts of elemental metabolomics, summarize standards and workflows, and propose minimum information for reporting the results of an elemental metabolomics experiment. Major statistical and informatics tools for elemental metabolomics are reviewed, and examples of applications are discussed. Elemental metabolomics is emerging as an important new technology with applications in medical diagnostics, nutrition, agriculture, food science, environmental science and multiplicity of other areas.

TANTIGEN: a comprehensive database of tumor T cell antigens.

Tumor T cell antigens are both diagnostically and therapeutically valuable molecules. A large number of new peptides are examined as potential tumor epitopes each year, yet there is no infrastructure for storing and accessing the results of these experiments. We have retroactively cataloged more than 1000 tumor peptides from 368 different proteins, and implemented a web-accessible infrastructure for storing and accessing these experimental results. All peptides in TANTIGEN are labeled as one of the four categories: (1) peptides measured in vitro to bind the HLA, but not reported to elicit either in vivo or in vitro T cell response, (2) peptides found to bind the HLA and to elicit an in vitro T cell response, (3) peptides shown to elicit in vivo tumor rejection, and (4) peptides processed and naturally presented as defined by physical detection. In addition to T cell response, we also annotate peptides that are naturally processed HLA binders, e.g., peptides eluted from HLA in mass spectrometry studies. TANTIGEN provides a rich data resource for tumor-associated epitope and neoepitope discovery studies and is freely available at http://cvc.dfci.harvard.edu/tantigen/ or http://projects.met-hilab.org/tadb (mirror).

Systematic identification of personal tumor-specific neoantigens in chronic lymphocytic leukemia.

Genome sequencing has revealed a large number of shared and personal somatic mutations across human cancers. In principle, any genetic alteration affecting a protein-coding region has the potential to generate mutated peptides that are presented by surface HLA class I proteins that might be recognized by cytotoxic T cells. To test this possibility, we implemented a streamlined approach for the prediction and validation of such neoantigens derived from individual tumors and presented by patient-specific HLA alleles. We applied our computational pipeline to 91 chronic lymphocytic leukemias (CLLs) that underwent whole-exome sequencing (WES). We predicted ∼22 mutated HLA-binding peptides per leukemia (derived from ∼16 missense mutations) and experimentally confirmed HLA binding for ∼55% of such peptides. Two CLL patients that achieved long-term remission following allogeneic hematopoietic stem cell transplantation were monitored for CD8(+) T-cell responses against predicted or confirmed HLA-binding peptides. Long-lived cytotoxic T-cell responses were detected against peptides generated from personal tumor mutations in ALMS1, C6ORF89, and FNDC3B presented on tumor cells. Finally, we applied our computational pipeline to WES data (N = 2488 samples) across 13 different cancer types and estimated dozens to thousands of predicted neoantigens per individual tumor, suggesting that neoantigens are frequent in most tumors.

An adaptive genetic algorithm for selection of blood-based biomarkers for prediction of Alzheimer's disease progression.

BACKGROUND: Alzheimer's disease is a multifactorial disorder that may be diagnosed earlier using a combination of tests rather than any single test. Search algorithms and optimization techniques in combination with model evaluation techniques have been used previously to perform the selection of suitable feature sets. Previously we successfully applied GA with LR to neuropsychological data contained within the The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, to select cognitive tests for prediction of progression of AD. This research addresses an Adaptive Genetic Algorithm (AGA) in combination with LR for identifying the best biomarker combination for prediction of the progression to AD. RESULTS: The model has been explored in terms of parameter optimization to predict conversion from healthy stage to AD with high accuracy. Several feature sets were selected - the resulting prediction moddels showed higher area under the ROC values (0.83-0.89). The results has shown consistency with some of the medical research reported in literature. CONCLUSION: The AGA has proven useful in selecting the best combination of biomarkers for prediction of AD progression. The algorithm presented here is generic and can be extended to other data sets generated in projects that seek to identify combination of biomarkers or other features that are predictive of disease onset or progression.

Pathway analysis and transcriptomics improve protein identification by shotgun proteomics from samples comprising small number of cells--a benchmarking study.

BACKGROUND: Proteomics research is enabled with the high-throughput technologies, but our ability to identify expressed proteome is limited in small samples. The coverage and consistency of proteome expression are critical problems in proteomics. Here, we propose pathway analysis and combination of microproteomics and transcriptomics analyses to improve mass-spectrometry protein identification from small size samples. RESULTS: Multiple proteomics runs using MCF-7 cell line detected 4,957 expressed proteins. About 80% of expressed proteins were present in MCF-7 transcripts data; highly expressed transcripts are more likely to have expressed proteins. Approximately 1,000 proteins were detected in each run of the small sample proteomics. These proteins were mapped to gene symbols and compared with gene sets representing canonical pathways, more than 4,000 genes were extracted from the enriched gene sets. The identified canonical pathways were largely overlapping between individual runs. Of identified pathways 182 were shared between three individual small sample runs. CONCLUSIONS: Current technologies enable us to directly detect 10% of expressed proteomes from small sample comprising as few as 50 cells. We used knowledge-based approaches to elucidate the missing proteome that can be verified by targeted proteomics. This knowledge-based approach includes pathway analysis and combination of gene expression and protein expression data for target prioritization. Genes present in both the enriched gene sets (canonical pathways collection) and in small sample proteomics data correspond to approximately 50% of expressed proteomes in larger sample proteomics data. In addition, 90% of targets from canonical pathways were estimated to be expressed. The comparison of proteomics and transcriptomics data, suggests that highly expressed transcripts have high probability of protein expression. However, approximately 10% of expressed proteins could not be matched with the expressed transcripts.

Bioinformatics for cancer immunotherapy target discovery.

The mechanisms of immune response to cancer have been studied extensively and great effort has been invested into harnessing the therapeutic potential of the immune system. Immunotherapies have seen significant advances in the past 20 years, but the full potential of protective and therapeutic cancer immunotherapies has yet to be fulfilled. The insufficient efficacy of existing treatments can be attributed to a number of biological and technical issues. In this review, we detail the current limitations of immunotherapy target selection and design, and review computational methods to streamline therapy target discovery in a bioinformatics analysis pipeline. We describe specialized bioinformatics tools and databases for three main bottlenecks in immunotherapy target discovery: the cataloging of potentially antigenic proteins, the identification of potential HLA binders, and the selection epitopes and co-targets for single-epitope and multi-epitope strategies. We provide examples of application to the well-known tumor antigen HER2 and suggest bioinformatics methods to ameliorate therapy resistance and ensure efficient and lasting control of tumors.

Novel myeloma-associated antigens revealed in the context of syngeneic hematopoietic stem cell transplantation.

Targets of curative donor-derived graft-versus-myeloma (GVM) responses after allogeneic hematopoietic stem cell transplantation (HSCT) remain poorly defined, partly because immunity against minor histocompatibility Ags (mHAgs) complicates the elucidation of multiple myeloma (MM)-specific targets. We hypothesized that syngeneic HSCT would facilitate the identification of GVM-associated Ags because donor immune responses in this setting should exclusively target unique tumor Ags in the absence of donor-host genetic disparities. Therefore, in the present study, we investigated the development of tumor immunity in an HLA-A0201(+) MM patient who achieved durable remission after myeloablative syngeneic HSCT. Using high-density protein microarrays to screen post-HSCT plasma, we identified 6 Ags that elicited high-titer (1:5000-1:10 000) Abs that correlated with clinical tumor regression. Two Ags (DAPK2 and PIM1) had enriched expression in primary MM tissues. Both elicited Ab responses in other MM patients after chemotherapy or HSCT (11 and 6 of 32 patients for DAPK2 and PIM1, respectively). The index patient also developed specific CD8(+) T-cell responses to HLA-A2-restricted peptides derived from DAPK2 and PIM1. Peptide-specific T cells recognized HLA-A2(+) MM-derived cell lines and primary MM tumor cells. Coordinated T- and B-cell immunity develops against MM-associated Ags after syngeneic HSCT. DAPK1 and PIM1 are promising target Ags for MM-directed immunotherapy.

Computational vaccinology and the ICoVax 2012 workshop.

Computational vaccinology or vaccine informatics is an interdisciplinary field that addresses scientific and clinical questions in vaccinology using computational and informatics approaches. Computational vaccinology overlaps with many other fields such as immunoinformatics, reverse vaccinology, postlicensure vaccine research, vaccinomics, literature mining, and systems vaccinology. The second ISV Pre-conference Computational Vaccinology Workshop (ICoVax 2012) was held on October 13, 2013 in Shanghai, China. A number of topics were presented in the workshop, including allergen predictions, prediction of linear T cell epitopes and functional conformational epitopes, prediction of protein-ligand binding regions, vaccine design using reverse vaccinology, and case studies in computational vaccinology. Although a significant progress has been made to date, a number of challenges still exist in the field. This Editorial provides a list of major challenges for the future of computational vaccinology and identifies developing themes that will expand and evolve over the next few years.

Literature classification for semi-automated updating of biological knowledgebases.

BACKGROUND: As the output of biological assays increase in resolution and volume, the body of specialized biological data, such as functional annotations of gene and protein sequences, enables extraction of higher-level knowledge needed for practical application in bioinformatics. Whereas common types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature. RESULTS: We defined and applied a machine learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens. Abstracts from PubMed were downloaded and classified as either "relevant" or "irrelevant" for database update. Training and five-fold cross-validation of a k-NN classifier on 310 abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature. CONCLUSION: We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining and machine learning. The addition of such data will aid in the transition of biological databases to knowledgebases.

Identification of human leucocyte antigen (HLA)-A*0201-restricted cytotoxic T lymphocyte epitopes derived from HLA-DOß as a novel target for multiple myeloma.

Despite the recent development of effective therapeutic agents against multiple myeloma (MM), new therapeutic approaches, including immunotherapies, remain to be developed. Here we identified novel human leucocyte antigen (HLA)-A*0201 (HLA-A2)-restricted cytotoxic T lymphocyte (CTL) epitopes from a B cell specific molecule HLA-DOß (DOB) as a potential target for MM. By DNA microarray analysis, the HLA-DOB expression in MM cells was significantly higher than that in normal plasma cells. Twenty-five peptides were predicted to bind to HLA-A2 from the amino acid sequence of HLA-DOB. When screened for the immunogenicity in HLA-A2-transgenic mice immunized with HLA-DOB cDNA, 4 peptides were substantially immunogenic. By mass spectrometry analysis of peptides eluted from HLA-A2-immunoprecipitates of MM cell lines, only two epitopes, HLA-DOB232-240 (FLLGLIFLL) and HLA-DOB185-193 (VMLEMTPEL), were confirmed for their physical presence on cell surface. When healthy donor blood was repeatedly stimulated in vitro with these two peptides and assessed by antigen-specific γ-interferon secretion, HLA-DOB232-240 was more immunogenic than HLA-DOB185-193 . Additionally, the HLA-DOB232-240 -specific CTLs, but not the HLA-DOB185-193 -specific CTLs, displayed an major histocompatibility complex class I-restricted reactivity against MM cell lines expressing both HLA-A2 and HLA-DOB. Taken together, based on the physical presence on tumour cell surface and high immunogenicity, HLA-DOB232-240 might be useful for developing a novel immunotherapy against MM.

West Nile virus T-cell ligand sequences shared with other flaviviruses: a multitude of variant sequences as potential altered peptide ligands.

Phylogenetic relatedness and cocirculation of several major human pathogen flaviviruses are recognized as a possible cause of deleterious immune responses to mixed infection or immunization and call for a greater understanding of the inter-Flavivirus protein homologies. This study focused on the identification of human leukocyte antigen (HLA)-restricted West Nile virus (WNV) T-cell ligands and characterization of their distribution in reported sequence data of WNV and other flaviviruses. H-2-deficient mice transgenic for either A2, A24, B7, DR2, DR3, or DR4 HLA alleles were immunized with overlapping peptides of the WNV proteome, and peptide-specific T-cell activation was measured by gamma interferon (IFN-γ) enzyme-linked immunosorbent spot (ELISpot) assays. Approximately 30% (137) of the WNV proteome peptides were identified as HLA-restricted T-cell ligands. The majority of these ligands were conserved in ∼≥88% of analyzed WNV sequences. Notably, only 51 were WNV specific, and the remaining 86, chiefly of E, NS3, and NS5, shared an identity of nine or more consecutive amino acids with sequences of 64 other flaviviruses, including several major human pathogens. Many of the shared ligands had an incidence of >50% in the analyzed sequences of one or more of six major flaviviruses. The multitude of WNV sequences shared with other flaviviruses as interspecies variants highlights the possible hazard of defective T-cell activation by altered peptide ligands in the event of dual exposure to WNV and other flaviviruses, by either infection or immunization. The data suggest the possible preferred use of sequences that are pathogen specific with minimum interspecies sequence homology for the design of Flavivirus vaccines.

Protocol for Classification Single-Cell PBMC Types from Pathological Samples Using Supervised Machine Learning.

Peripheral blood mononuclear cells (PBMC) are mixed subpopulations of blood cells composed of five cell types. PBMC are widely used in the study of the immune system, infectious diseases, cancer, and vaccine development. Single-cell transcriptomics (SCT) allows the labeling of cell types by gene expression patterns from biological samples. Classifying cells into cell types and states is essential for single-cell analyses, especially in the classification of diseases and the assessment of therapeutic interventions, and for many secondary analyses. Most of the classification of cell types from SCT data use unsupervised clustering or a combination of unsupervised and supervised methods including manual correction. In this chapter, we describe a protocol that uses supervised machine learning (ML) methods with SCT data for the classification of PBMC cell types in samples representing pathological states. This protocol has three parts: (1) data preprocessing, (2) labeling of reference PBMC SCT datasets and training supervised ML models, and (3) labeling new PBMC datasets from disease samples. This protocol enables building classification models that are of high accuracy and efficiency. Our example focuses on 10× Genomics technology but applies to datasets from other SCT platforms.

InCoB2012 Conference: from biological data to knowledge to technological breakthroughs.

Ten years ago when Asia-Pacific Bioinformatics Network held the first International Conference on Bioinformatics (InCoB) in Bangkok its theme was North-South Networking. At that time InCoB aimed to provide biologists and bioinformatics researchers in the Asia-Pacific region a forum to meet, interact with, and disseminate knowledge about the burgeoning field of bioinformatics. Meanwhile InCoB has evolved into a major regional bioinformatics conference that attracts not only talented and established scientists from the region but increasingly also from East Asia, North America and Europe. Since 2006 InCoB yielded 114 articles in BMC Bioinformatics supplement issues that have been cited nearly 1,000 times to date. In part, these developments reflect the success of bioinformatics education and continuous efforts to integrate and utilize bioinformatics in biotechnology and biosciences in the Asia-Pacific region. A cross-section of research leading from biological data to knowledge and to technological applications, the InCoB2012 theme, is introduced in this editorial. Other highlights included sessions organized by the Pan-Asian Pacific Genome Initiative and a Machine Learning in Immunology competition. InCoB2013 is scheduled for September 18-21, 2013 at Suzhou, China.

Ultra-high resolution HLA genotyping and allele discovery by highly multiplexed cDNA amplicon pyrosequencing.

BACKGROUND: High-resolution HLA genotyping is a critical diagnostic and research assay. Current methods rarely achieve unambiguous high-resolution typing without making population-specific frequency inferences due to a lack of locus coverage and difficulty in exon-phase matching. Achieving high-resolution typing is also becoming more challenging with traditional methods as the database of known HLA alleles increases. RESULTS: We designed a cDNA amplicon-based pyrosequencing method to capture 94% of the HLA class I open-reading-frame with only two amplicons per sample, and an analogous method for class II HLA genes, with a primary focus on sequencing the DRB loci. We present a novel Galaxy server-based analysis workflow for determining genotype. During assay validation, we performed two GS Junior sequencing runs to determine the accuracy of the HLA class I amplicons and DRB amplicon at different levels of multiplexing. When 116 amplicons were multiplexed, we unambiguously resolved 99%of class I alleles to four- or six-digit resolution, as well as 100% unambiguous DRB calls. The second experiment, with 271 multiplexed amplicons, missed some alleles, but generated high-resolution, concordant typing for 93% of class I alleles, and 96% for DRB1 alleles. In a third, preliminary experiment we attempted to sequence novel amplicons for other class II loci with mixed success. CONCLUSIONS: The presented assay is higher-throughput and higher-resolution than existing HLA genotyping methods, and suitable for allele discovery or large cohort sampling. The validated class I and DRB primers successfully generated unambiguously high-resolution genotypes, while further work is needed to validate additional class II genotyping amplicons.

Transcriptionally abundant major histocompatibility complex class I alleles are fundamental to nonhuman primate simian immunodeficiency virus-specific CD8+ T cell responses.

Simian immunodeficiency virus (SIV)-infected macaques are the preferred animal model for human immunodeficiency virus (HIV) vaccines that elicit CD8(+) T cell responses. Unlike humans, whose CD8(+) T cell responses are restricted by a maximum of six HLA class I alleles, macaques express up to 20 distinct major histocompatibility complex class I (MHC-I) sequences. Interestingly, only a subset of macaque MHC-I sequences are transcriptionally abundant in peripheral blood lymphocytes. We hypothesized that highly transcribed MHC-I sequences are principally responsible for restricting SIV-specific CD8(+) T cell responses. To examine this hypothesis, we measured SIV-specific CD8(+) T cell responses in MHC-I homozygous Mauritian cynomolgus macaques. Each of eight CD8(+) T cell responses defined by full-proteome gamma interferon (IFN-γ) enzyme-linked immunospot (ELISPOT) assay were restricted by four of the five transcripts that are transcriptionally abundant (>1% of total MHC-I transcripts in peripheral blood lymphocytes). The five transcriptionally rare transcripts shared by these animals did not restrict any detectable CD8(+) T cell responses. Further, seven CD8(+) T cell responses were defined by identifying peptide binding motifs of the three most frequent MHC-I transcripts on the M3 haplotype. Combined, these results suggest that transcriptionally abundant MHC-I transcripts are principally responsible for restricting SIV-specific CD8(+) T cell responses. Thus, only a subset of the thousands of known MHC-I alleles in macaques should be prioritized for CD8(+) T cell epitope characterization.

Optimizing vaccine development.

Optimizing the development of modern molecular vaccines requires a complex series of interdisciplinary efforts involving basic scientists, immunologists, molecular biologists, clinical vaccinologists, bioinformaticians and epidemiologists. This review summarizes some of the major issues that must be carefully considered. The intent of the authors is to briefly describe key components of the development process to give the reader an overview of the challenges faced from vaccine concept to vaccine delivery. Every vaccine requires unique features based on the biology of the pathogen, the nature of the disease and the target population for vaccination. This review presents general concepts relevant for the design and development of ideal vaccines protective against diverse pathogens.

Rare variants and HLA haplotypes associated in patients with neuromyelitis optica spectrum disorders.

Neuromyelitis optica spectrum disorders (NMOSD) are rare, debilitating autoimmune diseases of the central nervous system. Many NMOSD patients have antibodies to Aquaporin-4 (AQP4). Prior studies show associations of NMOSD with individual Human Leukocyte Antigen (HLA) alleles and with mutations in the complement pathway and potassium channels. HLA allele associations with NMOSD are inconsistent between populations, suggesting complex relationships between the identified alleles and risk of disease. We used a retrospective case-control approach to identify contributing genetic variants in patients who met the diagnostic criteria for NMOSD and their unaffected family members. Potentially deleterious variants identified in NMOSD patients were compared to members of their families who do not have the disease and to existing databases of human genetic variation. HLA sequences from patients from Belgrade, Serbia, were compared to the frequency of HLA haplotypes in the general population in Belgrade. We analyzed exome sequencing on 40 NMOSD patients and identified rare inherited variants in the complement pathway and potassium channel genes. Haplotype analysis further detected two haplotypes, HLA-A*01, B*08, DRB1*03 and HLA-A*01, B*08, C*07, DRB1*03, DQB1*02, which were more prevalent in NMOSD patients than in unaffected individuals. In silico modeling indicates that HLA molecules within these haplotypes are predicted to bind AQP4 at several sites, potentially contributing to the development of autoimmunity. Our results point to possible autoimmune and neurodegenerative mechanisms that cause NMOSD, and can be used to investigate potential NMOSD drug targets.