Landscape of infection enhancing antibodies in COVID-19 and healthy donors.

To assess the frequency of SARS-CoV-2 infection in the general population, we searched over 64 million heavy chain antibody sequences from healthy unvaccinated, healthy BNT162b2 vaccinated and COVID-19 patient repertoires for sequences similar to 11 previously reported enhancing antibodies. Although the distribution of sequence identities was similar in all three groups of repertoires, the COVID-19 and healthy vaccinated hits were significantly more clonally expanded than healthy unvaccinated hits. Furthermore, among the tested hits, 17 out of 94 from COVID-19 and 9 out of 59 from healthy vaccinated, compared with only 2 out of 96 from healthy unvaccinated, bound to the enhancing epitope. A total of 9 of the 28 epitope-binding antibodies enhanced ACE2 receptor binding to the spike protein. Together, this study revealed that infection enhancing-like antibodies are far more frequent in COVID-19 patients or healthy vaccinated donors than in healthy unvaccinated donors, but a reservoir of potential enhancing antibodies exists in healthy donors that could potentially mature to actual enhancing antibodies upon infection.

Improved Antibody-Specific Epitope Prediction Using AlphaFold and AbAdapt.

Antibodies recognize their cognate antigens with high affinity and specificity, but the prediction of binding sites on the antigen (epitope) corresponding to a specific antibody remains a challenging problem. To address this problem, we developed AbAdapt, a pipeline that integrates antibody and antigen structural modeling with rigid docking in order to derive antibody-antigen specific features for epitope prediction. In this study, we systematically assessed the impact of integrating the state-of-the-art protein modeling method AlphaFold with the AbAdapt pipeline. By incorporating more accurate antibody models, we observed improvement in docking, paratope prediction, and prediction of antibody-specific epitopes. We further applied AbAdapt-AF in an anti-receptor binding domain (RBD) antibody complex benchmark and found AbAdapt-AF outperformed three alternative docking methods. Also, AbAdapt-AF demonstrated higher epitope prediction accuracy than other tested epitope prediction tools in the anti-RBD antibody complex benchmark. We anticipate that AbAdapt-AF will facilitate prediction of antigen-antibody interactions in a wide range of applications.

High cell surface expression and peptide binding affinity of HLA-DQA1*05:03, a susceptible allele of neuromyelitis optica spectrum disorders (NMOSD).

Neuromyelitis optica spectrum disorder (NMOSD) is a relapsing autoimmune disease characterized by the presence of pathogenic autoantibodies, anti-aquaporin 4 (AQP4) antibodies. Recently, HLA-DQA1*05:03 was shown to be significantly associated with NMOSD in a Japanese patient cohort. However, the specific mechanism by which HLA-DQA1*05:03 is associated with the development of NMOSD has yet to be elucidated. In the current study, we revealed that HLA-DQA1*05:03 exhibited significantly higher cell surface expression levels compared to other various DQA1 alleles, and that its expression strongly depended on the amino acid sequence of the α1 domain, with a preference for leucine at position 75. Moreover, in silico analysis indicated that the HLA-DQ encoded by HLA-DQA1*05:03 preferentially presents immunodominant AQP4 peptides, and that the peptide major histocompatibility complexes (pMHCs) are more energetically stable in the presence of HLA-DQA1*05:03 than other HLA-DQA1 alleles. In silico 3D structural models were also applied to investigate the validity of the energetic stability of pMHCs. Taken together, our findings indicate that HLA-DQA1*05:03 possesses a distinct property to play a pathogenic role in the development of NMOSD.

AbAdapt: an adaptive approach to predicting antibody-antigen complex structures from sequence.

Motivation: The scoring of antibody-antigen docked poses starting from unbound homology models has not been systematically optimized for a large and diverse set of input sequences. Results: To address this need, we have developed AbAdapt, a webserver that accepts antibody and antigen sequences, models their 3D structures, predicts epitope and paratope, and then docks the modeled structures using two established docking engines (Piper and Hex). Each of the key steps has been optimized by developing and training new machine-learning models. The sequences from a diverse set of 622 antibody-antigen pairs with known structure were used as inputs for leave-one-out cross-validation. The final set of cluster representatives included at least one 'Adequate' pose for 550/622 (88.4%) of the queries. The median (interquartile range) ranks of these 'Adequate' poses were 22 (5-77). Similar results were obtained on a holdout set of 100 unrelated antibody-antigen pairs. When epitopes were repredicted using docking-derived features for specific antibodies, the median ROC AUC increased from 0.679 to 0.720 in cross-validation and from 0.694 to 0.730 in the holdout set. Availability and implementation: AbAdapt and related data are available at https://sysimm.org/abadapt/. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Methods for sequence and structural analysis of B and T cell receptor repertoires.

B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.

Structural Modeling of Lymphocyte Receptors and Their Antigens.

Structural modeling plays a key role in protein function prediction on a genome-wide scale. For B and T lymphocyte receptors, the critical functional question is: which antigens and epitopes are targeted? With emerging B cell receptor (BCR) and T cell receptor (TCR) sequencing methods improving in both breadth and depth, there is a growing need for methods that can help answer this question. Since lymphocyte-antigen recognition depends on complementarity, structural modeling is likely to play an important role in understanding antigen specificity and affinity. In the case of BCRs, such modeling methods have a long history in the study and design of antibodies. However, for TCRs there are relatively few publicly available modeling tools, and, to our knowledge, none that incorporate interaction between TCRs and peptide-MHC (pMHC) complexes. Here, we provide a web-based tool, ImmuneScape ( https://sysimm.org/immune-scape/ ), to carry out TCR-pMHC modeling as a first step toward structure-based function prediction.