Publications by authors named "Zichang Xu"

Whereas severe COVID-19 is often associated with elevated autoantibody titers, the underlying mechanism behind their generation has remained unclear. Here we report clonal composition and diversity of autoantibodies in humoral response to SARS-CoV-2. Immunoglobulin repertoire analysis and characterization of plasmablast-derived monoclonal antibodies uncovered clonal expansion of plasmablasts producing cardiolipin (CL)-reactive autoantibodies.

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Liquid biopsies based on peripheral blood offer a minimally invasive alternative to solid tissue biopsies for the detection of diseases, primarily cancers. However, such tests currently consider only the serum component of blood, overlooking a potentially rich source of biomarkers: adaptive immune receptors (AIRs) expressed on circulating B and T cells. Machine learning-based classifiers trained on AIRs have been reported to accurately identify not only cancers but also autoimmune and infectious diseases as well.

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Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional U-Net architectures and their transformer-integrated variants excel in automated segmentation tasks. Existing models also struggle with parameter efficiency and computational complexity, often due to the extensive use of Transformers.

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Determining antigen and epitope specificity is an essential step in the discovery of therapeutic antibodies as well as in the analysis adaptive immune responses to disease or vaccination. Despite extensive efforts, deciphering antigen specificity solely from BCR amino acid sequence remains a challenging task, requiring a combination of experimental and computational approaches. Here, we describe and experimentally validate a simple and straightforward approach for grouping antibodies that share antigen and epitope specificities based on their CDR sequence similarity.

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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.

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Structural genomics began as a global effort in the 1990s to determine the tertiary structures of all protein families as a response to large-scale genome sequencing projects. The immediate outcome was an influx of tens of thousands of protein structures, many of which had unknown functions. At the time, the value of structural genomics was controversial.

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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.

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Antibody and TCR modeling are becoming important as more and more sequence data becomes available to the public. One of the pressing questions now is how to use such data to understand adaptive immune responses to disease. Infectious disease is of particular interest because the antigens driving such responses are often known to some extent.

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Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping.

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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.

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The SARS-CoV-2 S protein is a major point of interaction between the virus and the human immune system. As a consequence, the S protein is not a static target but undergoes rapid molecular evolution. In order to more fully understand the selection pressure during evolution, we examined residue positions in the S protein that vary greatly across closely related viruses but are conserved in the subset of viruses that infect humans.

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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.

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The original version of this article contains error for some of the authors corrections were not included during correction stage.

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Enzyme engineering has been widely employed to tailor the substrate specificity and enantioselectivity of enzymes. In this study, we mutated Trp288, an unconserved residue in the small binding pocket of an acetophenone reductase from Geotrichum candidum NBRC 4597 (GcAPRD). Trp288 mutants showed substrate specificity expansion towards bulky-bulky ketones and enantioselectivity alteration which was highly dependent on the substrate substituent length.

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Aliphatic ketones, such as 2-butanone and 3-hexanone, with only one carbon difference among side chains adjacent to the carbonyl carbon are difficult to be reduced enantioselectively. In this study, we utilized an acetophenone reductase from Geotrichum candidum NBRC 4597 (GcAPRD) to reduce challenging aliphatic ketones such as 2-butanone (methyl ethyl ketone) and 3-hexanone (ethyl propyl ketone) to their corresponding (S)-alcohols with 94% ee and > 99% ee, respectively. Through crystallographic structure determination, it was suggested that residue Trp288 limit the size of the small binding pocket.

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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.

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