RESUMO
The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM's representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWING's generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.
RESUMO
The pivotal role of T cell responses has been well studied in both protective and destructive scenarios. T cells recognize peptide epitopes presented on Human Leukocyte Antigens (HLA) through their surface T cell receptors (TCR). Advances in single-cell RNA sequencing have identified millions of TCRs, but only a minuscule fraction of them have known epitopes. Recently, cell-based T cell antigen discovery platforms have emerged onto the landscape. Here, Jin and colleagues, report a novel antigen discovery platform called Tsyn-seq that relies on sequencing TCR-peptide-HLA-induced synapses for genome-wide epitope screening. See related article by Jin et al., p. 530 (3).
Assuntos
Receptores de Antígenos de Linfócitos T , Linfócitos T , Humanos , Receptores de Antígenos de Linfócitos T/genética , Receptores de Antígenos de Linfócitos T/imunologia , Linfócitos T/imunologia , Epitopos de Linfócito T/imunologia , Sinapses Imunológicas/imunologia , Antígenos HLA/genética , Antígenos HLA/imunologia , Sequenciamento de Nucleotídeos em Larga EscalaRESUMO
CD4+ T cells recognize peptide antigens presented on class II major histocompatibility complex (MHC-II) molecules to carry out their function. The remarkable diversity of T cell receptor sequences and lack of antigen discovery approaches for MHC-II make profiling the specificities of CD4+ T cells challenging. We have expanded our platform of signaling and antigen-presenting bifunctional receptors to encode MHC-II molecules presenting covalently linked peptides (SABR-IIs) for CD4+ T cell antigen discovery. SABR-IIs can present epitopes to CD4+ T cells and induce signaling upon their recognition, allowing a readable output. Furthermore, the SABR-II design is modular in signaling and deployment to T cells and B cells. Here, we demonstrate that SABR-IIs libraries presenting endogenous and non-contiguous epitopes can be used for antigen discovery in the context of type 1 diabetes. SABR-II libraries provide a rapid, flexible, scalable and versatile approach for de novo identification of CD4+ T cell ligands from single-cell RNA sequencing data using experimental and computational approaches.
Assuntos
Linfócitos T CD4-Positivos , Epitopos de Linfócito T , Antígenos de Histocompatibilidade Classe II , Linfócitos T CD4-Positivos/imunologia , Epitopos de Linfócito T/imunologia , Animais , Antígenos de Histocompatibilidade Classe II/imunologia , Antígenos de Histocompatibilidade Classe II/química , Camundongos , Humanos , Diabetes Mellitus Tipo 1/imunologia , Peptídeos/imunologia , Peptídeos/química , Apresentação de Antígeno/imunologia , Receptores de Antígenos de Linfócitos T/imunologia , Camundongos Endogâmicos NOD , Análise de Célula Única/métodosRESUMO
Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.