Bin Ma

Plasmodium falciparum pathology is driven by the accumulation of parasite-infected erythrocytes in blood capillaries. This sequestration process is mediated by the parasite’s P. falciparum erythrocyte membrane protein 1 (PfEMP1) adhesins, which bind select endothelial cell receptors. A subset of PfEMP1 binding human endothelial protein C receptor (EPCR) through their cysteine-rich interdomain region alpha 1 (CIDRα1) domains drives the pathogenesis to severe malaria. Despite high sequence diversity among CIDRα1 domains, individuals living in malaria-endemic regions become immune to severe disease in part through acquisition of antibodies inhibiting the PfEMP1–EPCR interaction. Here, we demonstrate an approach to identify pathogen-specific human monoclonal antibodies from plasma, combining mass spectrometry analysis of antigen-purified polyclonal plasma IgG and Ig transcript sequencing. We identified a clonal family of broadly reactive and EPCR binding-inhibitory human monoclonal antibodies against CIDRα1. The antibodies, isolated from a 9-y-old child, exhibited potent inhibition of EPCR binding broadly across CIDRα1 domains as well as binding of infected erythrocytes to EPCR. Structural analysis of one antibody variant complexed with CIDRα1 revealed a shared epitope of the clonal antibody family overlapping the EPCR binding site and the epitopes of two previously identified monoclonal antibodies, C7 and C74, with similar functional patterns. However, although C7, C74, and 110-3 antibodies all depend on the same few residues conserved in CIDRα1 to retain EPCR binding, the 110-3 antibodies contact additional variable residues, reducing their breadth of reactivity across the CIDRα1 family. These data bolster the hypothesis that broadly inhibitory antibodies against severe malaria-associated PfEMP1 target similar epitopes and are commonly developed in malaria-exposed individuals.

The functional characterization of host-gut microbiome interactions remains limited by the sensitivity of current metaproteomic approaches. Here, we present uMetaP, an ultra-sensitive workflow combining advanced LC-MS technologies with an FDR-validated de novo sequencing strategy, novoMP. uMetaP markedly expands functional coverage and improves the taxonomic detection limit of the gut dark metaproteome by 5000-fold, enabling precise detection and quantification of low-abundance microbial and host proteins. Applied to a mouse model of intestinal injury, uMetaP revealed host-microbiome functional networks underlying tissue damage, beyond genomic findings. Orthogonal validation using transcriptomic data from Crohn’s disease patients confirmed key host protein alterations. Furthermore, we introduce the concept of a druggable metaproteome, mapping functional targets within the host and microbiota. By redefining the sensitivity limits of metaproteomics, uMetaP provides a highly valuable framework for advancing microbiome research and developing therapeutic strategies for microbiome-related diseases.

The antibody response to vaccination and infection is a key component of the immune response to pathogens. Sequencing of peripheral B cells may not represent the complete B cell receptor repertoire. Here we present a method for sequencing human plasma-derived polyclonal IgG using a combination of mass spectrometry and B-cell sequencing. We investigate the IgG response to the Moderna Spikevax COVID-19 vaccine. From the sequencing data of the natural polyclonal response to vaccination, we generate 12 recombinant antibodies. Six derived recombinant antibodies, including four generated with de novo protein sequencing, exhibit similar or higher binding affinities than the original natural polyclonal antibody. Neutralization tests reveal that the six antibodies possess neutralizing capabilities against the target antigen. This research provides insights into sequencing polyclonal IgG antibodies and the potential of our approach in generating recombinant antibodies with robust binding affinity and neutralization capabilities. Directly examining the circulating IgG pool is crucial due to potential misrepresentations by B-cell analysis alone.

Major Histocompatibility Complex (MHC) molecules play a critical role in the immune system by presenting peptides on the cell surface for recognition by T-cells. Tumor cells often produce MHC peptides with amino acid mutations, known as neoantigens, which evade T-cell recognition, leading to rapid tumor growth. In immunotherapies such as TCR-T and CAR-T, identifying these mutated MHC peptide sequences is crucial. Current mass spectrometry-based peptide identification methods primarily rely on database searching, which fails to detect mutated peptides not present in human databases. In this paper, we propose a novel workflow called NeoMS, designed to efficiently identify both non-mutated and mutated MHC-I peptides from mass spectrometry data. NeoMS utilizes a tagging algorithm to generate an expanded sequence database that includes potential mutated proteins for each sample. Furthermore, it employs a machine learning-based scoring function for each peptide-spectrum match (PSM) to maximize search sensitivity. Finally, a rigorous target-decoy approach is implemented to control the false discovery rates (FDR) of the peptides with and without mutations separately. Experimental results for regular peptides demonstrate that NeoMS outperforms four benchmark methods. For mutated peptides, NeoMS successfully identifies hundreds of high-quality mutated peptides in a melanoma-associated sample, with their validity confirmed by further studies.

We present a method for sequencing polyclonal IgG enriched from human plasma, employing a combination of de novo sequencing, proteomics, bioinformatics, protein separation, sequencing, and peptide separations. Our study analyzes a single patient’s IgG antibody response triggered by the Moderna Spikevax mRNA COVID-19 vaccine. From the sequencing data of the natural polyclonal response to vaccination, we generated 12 recombinant antibodies. Six derived recombinant antibodies, including four generated with de novo sequencing, exhibited similar or higher binding affinities than the original natural polyclonal antibody. Our neutralization tests revealed that the six antibodies possess neutralizing capabilities against the target antigen. This research provides insights into sequencing polyclonal IgG antibodies while highlighting the effectiveness and potential of our approach in generating recombinant antibodies with robust binding affinity and neutralization capabilities. Our proposed approach is an advancement in characterizing the IgG response by directly investigating the circulating pool of IgG without relying exclusively on the B-cell repertoire or population. This is crucial as the B-cell analysis may not accurately represent the circulating antibodies. Interestingly, a large proportion (80 to 90%) of the human antibody sequences generated against SARS-CoV-2 in the literature have been derived solely from B-cell analysis. Therefore, the ability to offer a different perspective is crucial in gaining a comprehensive understanding of the IgG response. Significance Statement: We investigate human IgG targeting the receptor binding domain using de novo proteomics. The peripheral B-cell repertoire may not adequately cover all the circulating IgG for human IgG sequencing. Our approach overcomes this limitation by using a de novo protein sequencing on top of standard proteomics. We obtained distinct de novo sequences, showcasing our method’s potential. The recombinant proteins we generate possess traits comparable to or surpassing the naturally occurring polyclonal antibodies (pAbs). This study highlights similarities and differences between IgG populations in blood and circulating B-cells, which is crucial for future biologics development.

Cancer remains a significant global health challenge, responsible for millions of deaths annually. Addressing this issue necessitates the discovery of novel anti-cancer drugs. Anti-cancer peptides (ACPs), with their unique ability to selectively target cancer cells, offer new hope in discovering low side-effect anti-cancer drugs. However, the process of discovering novel ACPs is both time-consuming and costly. Therefore, there is an urgent need for a computational method that can predict whether a given peptide is an ACP and classify its specific functional types. In this paper, we introduce DUO-ACP, a model serving dual roles in ACP prediction: identification and functional type classification. DUO-ACP employs two embedding modules to acquire knowledge about global protein features and local ACP characteristics, complemented by a prediction module. When assessed on two publicly available datasets for each task, DUO-ACP surpasses all existing methods, achieving outstanding results: an ACP identification accuracy of 89.5% and a Macro-averaged AUC of 88.6% in ACP functional type classification. We further interpret the contribution of each part of our model, including the two types of embeddings as well as ensemble learning. On a new curated dataset, the prediction results of DUO-ACP closely match existing literature, highlighting DUO-ACP's generalization capabilities on previously unseen data and displaying the potential capability of discovering novel ACP. The source code of DUO-ACP is publicly available on GitHub ( https://github.com/waterlooms/DUO-ACP )