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

Kang-Pettinger, Tara; Walker, Kayleigh; Brown, R. H.; Cowan, Richard; Wright, Helena; Baravalle, Roberta; Waters, Lorna C.; Muskett, Frederick W.; Bowler, Matthew W.; Sawmynaden, Kovilen; Coombs, P.J.; Carr, Mark D.; Hall, G. G.

doi:10.1016/j.jbc.2022.102769

Cited by 5 publications

(4 citation statements)

References 81 publications

(148 reference statements)

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“…14–22 Computational methods have potential to be faster and less costly, however, even advanced artificial intelligence (AI)-based AlphaFold and RoseTTAFold 23–26 have faced challenges in accurately predicting protein-protein interactions. 27–30 While success cases exist, such as the model of a PD-L1/CD80 complex, 31 large screening studies have shown a low number of highly-accurate antibody-antigen complex prediction. 32,33 Similarly, traditional computational methods often fail to accurately identify binding epitopes, sometimes producing ambiguous results that suggest binding at multiple locations (Fig.…”

Section: Introductionmentioning

confidence: 99%

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

Gomes,

Yang,

Vanella

et al. 2024

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

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

Gomes,

Yang,

Vanella

et al. 2024

Preprint

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“…In addition to whole antibodies, anti-PD-L1 VHHs have been isolated from naïve or immune llama Phage Display libraries [ 132 , 142 ]. The search for anti-PD-1/PD-L1 VHH is supported by the better tissue penetration of this small molecule, which becomes interesting for solid tumors where the diffusion of larger antibodies is limited.…”

Section: Phage Display To Select Antibodies Against Cancermentioning

confidence: 99%

Progress on Phage Display Technology: Tailoring Antibodies for Cancer Immunotherapy

França,

Studart,

Bezerra

et al. 2023

Viruses

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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.

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“…The interaction between CD80 and CTLA-4 can produce signals that inhibit the activity of T cells, and the heterodimerization of PD-L1/CD80 can effectively reduce the affinity of CTLA-4/CD80 interaction. Therefore, inducing the heterodimerization of PD-L1 and CD80 is an effective method to activate T cells [87]. Additionally, both CD80 and PD-1 act by binding to the IgV-like domain of PD-L1, so the heterodimerization of CD80/PD-L1 can effectively reduce the binding affinity between PD-L1 and PD-1, thereby removing the inhibitory effect on T cells [88].…”

Section: Pd-l1 Dimerizationmentioning

confidence: 99%

Dimerization of Transmembrane Proteins in Cancer Immunotherapy

2023

Membranes

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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.

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

Cited by 5 publications

References 81 publications

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

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

Progress on Phage Display Technology: Tailoring Antibodies for Cancer Immunotherapy

Dimerization of Transmembrane Proteins in Cancer Immunotherapy

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