Identification, binding, and structural characterization of single domain anti-PD-L1 antibodies inhibitory of immune regulatory proteins PD-1 and CD80

Unraveling the Specific Recognition Between PD-L1 and Engineered CLP002 Functionalized Gold Nanostructures: MD Simulation Studies

PD-L1 (programmed cell death ligand-1) is a protein located on the surface of regulatory cells. It has an immunosuppressive role as it binds specifically to the protein programmed cell death-1 (PD-1), a checkpoint glycoprotein, present on the surface of immune cells such as T and B lymphocytes. Many tumor cells block the immune response by overexpressing PD-L1 on their surface; therefore, targeting PD-L1 represents a powerful strategy that allows tumor localization. To determine the presence of PD-L1 in cells, the use of ad hoc functionalized peptides that bind to PD-L1 can be exploited. One of them is the peptide CLP002 (Trp-His-Arg-Ser-Tyr-Tyr-Thr-Trp-Asn-Leu-Asn-Thr), which, bound to surface-enhanced Raman scattering (SERS) gold nanostructures via a suitable linker, was shown to be highly effective in recognizing MDA-MB-231 breast cancer cells and, importantly, this recognition can be measured by SERS experiments. To characterize, on a molecular scale, the interaction between PD-L1 and peptide functionalized nanostructures, we performed molecular dynamics (MDs) simulations, studying the features of peptide monolayers bound on gold surfaces in the absence and presence of PD-L1. The results obtained allow us to explain why the nature of the linker plays a fundamental role in the binding and why a peptide carrying the same amino acids as CPL002 but with a different sequence (scrambled) is much less active than CLP002. These results open the way to an in silico evaluation of the key parameters that regulate the binding of PD-L1 useful for cancer recognition.

Gingipain regulates isoform switches of PD-L1 in macrophages infected with Porphyromonas gingivalis

Periodontal pathogen Porphyromonas gingivalis (P. gingivalis) is believed to possess immune evasion capabilities, but it remains unclear whether this immune evasion is related to host gene alternative splicing (AS). In this study, RNA-sequencing revealed significant changes in both AS landscape and transcriptomic profile of macrophages following P. gingivalis infection with/without knockout of gingipain (a unique toxic protease of P. gingivalis). P. gingivalis infection increased the PD-L1 transcripts expression and selectively upregulated a specific coding isoform that more effectively binds to PD-1 on T cells, thereby inhibiting immune function. Biological experiments also detected AS switch of PD-L1 in P. gingivalis-infected or gingipain-treated macrophages. AlphaFold 3 predictions indicated that the protein docking compatibility between PD-1 and P. gingivalis-upregulated PD-L1 isoform was over 80% higher than another coding isoform. These findings suggest that P. gingivalis employs gingipain to modulate the AS of PD-L1, facilitating immune evasion.

PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery

Prediction of protein-protein binding (PPB) affinity plays an important role in large-molecular drug discovery. Deep learning (DL) has been adopted to predict the changes of PPB binding affinities upon mutations, but there was a scarcity of studies predicting the PPB affinity itself. The major reason is the paucity of open-source dataset with PPB affinity data. To address this gap, the current study introduced a large comprehensive PPB affinity (PPB-Affinity) dataset. The PPB-Affinity dataset contains key information such as crystal structures of protein-protein complexes (with or without protein mutation patterns), PPB affinity, receptor protein chain, ligand protein chain, etc. To the best of our knowledge, this is the largest publicly available PPB affinity dataset, and we believe it will significantly advance drug discovery by streamlining the screening of potential large-molecule drugs. We also developed a deep-learning benchmark model with this dataset to predict the PPB affinity, providing a foundational comparison for the research community.

VSTM2A reverses immunosuppression in colorectal cancer by antagonizing the PD-L1/PD-1 interaction

Immunoglobulin (Ig) VSTM2A (V-set and transmembrane domain containing 2A) is a top-ranked secretory protein frequently silenced during colorectal carcinogenesis; however, its role in immune modulation remains largely unknown. Bioinformatic and immunohistochemistry analysis of human colorectal specimens and Vstm2a+/- knockout mice indicated that VSTM2A positively correlated with CD8a and immune infiltration in both physiological and pathological conditions. We then utilized liquid chromatography-mass spectrometry to pinpoint programmed death ligand 1 (PD-L1) as a membrane receptor of VSTM2A. A series of in vitro biochemistry assays further revealed the binding pattern and kinetics between VSTM2A and PD-L1 proteins through their IgV domains at a dissociation constant of 0.7-2.5 nM. Recombinant VSTM2A protein inhibited the PD-1/PD-L1 interaction and induced NFAT response element (RE) luciferase activity dose dependently. Furthermore, interleukin (IL)-2 production from DO11.10 T cells upon co-culture with mouse non-T splenocytes was upregulated in the presence of VSTM2A conditioned medium. Finally, tumor killing assay and ex vivo data from human peripheral blood mononuclear cells and autologous dendritic cell-T cell co-culture demonstrated that VSTM2A significantly enhanced immune activation via the release of granzyme B and interferon (IFN)-γ cytokines. In conclusion, our study demonstrates the tumor-extrinsic role of VSTM2A in sterically blocking the PD-L1/PD-1 interaction at a picomole to nanomole affinity, which leads to the enhanced anti-tumor effect of cytotoxic T cells.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein-protein interaction interfaces.

Application of Aptamer-SERS Nanotags for Unveiling the PD-L1 Immunomarker Progression Correlated to the Cell Metabolic Bioprocess

In recent years, the expression and progression of programmed cell death ligand 1 (PD-L1) as an immunomarker in the context of a cell metabolic environment has gained significant attention in cancer research. However, intercellular bioprocesses that control the dynamics of PD-L1 have been largely unexplored. This study aimed to explore the cell metabolic states and conditions that govern dynamic variations of PD-L1 within the cell metabolic environment using an aptamer-based surface-enhanced Raman scattering (SERS) approach. The aptamer-SERS technique offers a sensitive, rapid, and powerful analytical tool for targeted and nondestructive detection of an immunomarker with high sensitivity and specificity. By combining aptamer-SERS with cell state profiling, we investigated the modulation in PD-L1 expression under different metabolic states, including glucose deprivation, metabolic coenzyme activity, and altered time/concentration-based cytokine availability. The most intriguing features in our findings include the cell-specific responses, cell differentiation by revealing distinct patterns, and dynamics of PD-L1 in different cell lines. Additionally, the time-dependent variations in PD-L1 expression, coupled with the dose-dependent relationship between glucose concentration and PD-L1 levels, underscore the complex interplay between immune checkpoint regulation and cellular metabolism. Therefore, this work demonstrates the advantages of using highly-sensitive and specific aptamer-SERS nanotags for investigating the immune checkpoint dynamics and related metabolic bioprocess.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure is a long standing goal with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost intensive. Computational methods have potential to accelerate epitope predictions, however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologs. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2 Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis alone each proved ineffectual in differentiating between two putative binding models referred to as parallel and perpendicular. However, our integrated approach based on dynamic network analysis showed that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols including cross linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Our research highlights the potential of deploying dynamic network analysis to refine AI-based structure predictions for precise predictions of protein-protein interaction interfaces.

Progress on Phage Display Technology: Tailoring Antibodies for Cancer Immunotherapy

The search for innovative anti-cancer drugs remains a challenge. Over the past three decades, antibodies have emerged as an essential asset in successful cancer therapy. The major obstacle in developing anti-cancer antibodies is the need for non-immunogenic antibodies against human antigens. This unique requirement highlights a disadvantage to using traditional hybridoma technology and thus demands alternative approaches, such as humanizing murine monoclonal antibodies. To overcome these hurdles, human monoclonal antibodies can be obtained directly from Phage Display libraries, a groundbreaking tool for antibody selection. These libraries consist of genetically engineered viruses, or phages, which can exhibit antibody fragments, such as scFv or Fab on their capsid. This innovation allows the in vitro selection of novel molecules directed towards cancer antigens. As foreseen when Phage Display was first described, nowadays, several Phage Display-derived antibodies have entered clinical settings or are undergoing clinical evaluation. This comprehensive review unveils the remarkable progress in this field and the possibilities of using clever strategies for phage selection and tailoring the refinement of antibodies aimed at increasingly specific targets. Moreover, the use of selected antibodies in cutting-edge formats is discussed, such as CAR (chimeric antigen receptor) in CAR T-cell therapy or ADC (antibody drug conjugate), amplifying the spectrum of potential therapeutic avenues.

Dimerization of Transmembrane Proteins in Cancer Immunotherapy

Transmembrane proteins (TMEMs) are integrated membrane proteins that span the entire lipid bilayer and are permanently anchored to it. TMEMs participate in various cellular processes. Some TMEMs usually exist and perform their physiological functions as dimers rather than monomers. TMEM dimerization is associated with various physiological functions, such as the regulation of enzyme activity, signal transduction, and cancer immunotherapy. In this review, we focus on the dimerization of transmembrane proteins in cancer immunotherapy. This review is divided into three parts. First, the structures and functions of several TMEMs related to tumor immunity are introduced. Second, the characteristics and functions of several typical TMEM dimerization processes are analyzed. Finally, the application of the regulation of TMEM dimerization in cancer immunotherapy is introduced.