Biology subjects

Sulea, T.

Publications and source records attributed to Sulea, T..

3 recordsLinked to original sources

bioRxiv2024

AI-Augmented Physics-Based Docking for Antibody-Antigen Complex Prediction

Predicting the structure of antibody-antigen complexes is a challenging task with significant implications for the design of better antibody therapeutics. However, the levels of success have remained dauntingly low, particularly when high standards for model quality are required, a necessity for efficient antibody design. Artificial intelligence (AI) has significantly impacted the landscape of structure prediction for antibodies, both alone and in complex with their antigens. We utilized AI-guided antibody modeling tools to generate ensembles displaying diversity in the complementarity-determining region (CDR) and integrated those into our previously published AlphaFold2-rescored docking pipeline, a strategy called AI-augmented physics-based docking. We highlight that the quality of the ensemble is crucial for docking performance, that including too many models can be detrimental and that prioritization of models is essential for achieving good performance. In this study, we also compare docking performance with AlphaFold, the new benchmark in the field. We distinguish between two types of success tailored to specific downstream applications: 1) criteria sufficient for epitope mapping, where gross quality is adequate and can complement experimental techniques, and 2) criteria for producing higher-quality models suitable for engineering purposes. Our results robustly demonstrate the advantages of AI-augmented docking over AlphaFold2, further accentuated when higher standards in quality are imposed. Docking performance is noticeably lower than the one of AlphaFold3 in both epitope mapping and antibody design. While we observe a strong dependence on CDR-H3 length for physics-based tools on their ability to successfully predict, this helps define an applicability range where physics-based docking can be competitive to AlphaFold3.

bioinformatics↗

bioRxiv2022

Enhanced antibody-antigen structure predictionfrom molecular docking using AlphaFold2

Predicting the structure of antibody-antigen complexes has tremendous value in biomedical research but unfortunately suffers from a poor performance in real-life applications. AlphaFold2 (AF2) has provided renewed hope for improvements in the field of protein-protein docking but has shown limited success for the medically relevant class of antibody-antigen complexes due to the lack of co-evolutionary constraints. Some research groups have demonstrated the usefulness of the AF2 confidence metrics for assessing the plausibility of protein folding models. In this study, we used physics-based protein docking methods for building decoy sets consisting of low-energy docking solutions that were either geometrically close to the native structure (positives) or not (negatives). The docking models were then fed into AF2 to assess their confidence with a novel composite score based on the pLDDT and pTMscore metrics. We show benefits of the AF2 composite score for rescoring docking poses in two scenarios: (1) a more trivial experiment based on the bound conformations of the antibody and antigen backbone structures, and (2) a more realistic test employing the unbound backbone conformations of the binding partners. Docking success rates improved after AF2 rescoring with particular emphasis on early enrichment of positives at the very top of the re-ranked list of decoys. The AF2 rescoring markedly improved classification of positives and negatives in most systems. Docking models of at least medium quality present in the decoy set, but not necessarily highly ranked by docking methods, benefitted most from AF2 rescoring by experiencing large advances towards the top of the reranked list of models. These improvements, obtained without any calibration or novel methodologies, led to a notable level of performance in antibody-antigen unbound docking that was never achieved previously.

bioinformatics↗

bioRxiv2020

Fine Molecular Tuning of Chimeric Antigen Receptors through Hinge Length Optimization

BackgroundChimeric antigen receptor (CAR) technology has revolutionized the treatment of B-cell malignancies and steady progress is being made towards CAR-immunotherapies for solid tumours. Epidermal growth factor family receptors EGFR or HER2 are commonly overexpressed in cancer and represent proven targets for CAR-T therapy; given their expression in healthy tissues it is imperative that any targeting strategy consider the potential for on-target off-tumour toxicity. MethodsHerein, we utilize high-throughput CAR screening to identify novel camelid single-domain antibody CARs (sdCARs) with high EGFR-specific CAR-T response. To optimize antigenic sensitivity of this EGFR-sdCAR, we performed progressive N-terminal truncation of the human CD8 hinge domain used as a spacer in many CAR constructs. Hinge truncation resulted in decreased CAR sensitivity to EGFR and improved selectivity for EGFR-overexpressing cells over EGFR-low target cells or healthy donor derived EGFR-positive fibroblasts. To investigate the molecular mechanism of hinge truncation, we test hinge-truncated scFv-based CARs targeting membrane proximal or membrane distal domains of EGFR-family proteins, HER2 and EGFRvIII. Finally, we proceed to test hinge variant EGFR-sdCAR functionality through in vitro and in vivo assessments in primary T cells derived from multiple donors. ResultsFor CARs targeting membrane-proximal epitopes, hinge truncation by even a single amino acid provided fine control of the antigenic sensitivity, whereas CARs targeting membrane distal domains were not sensitive to even complete hinge domain removal. Hinge-modified EGFR-sdCARs showed consistent and predictable responses in Jurkat-CAR cells and primary human CAR-T cells in vitro and in vivo. ConclusionsOverall, these results indicate that membrane-proximal epitope targeting CARs can be modified through hinge length tuning for programmable antigenic sensitivity and improved tumour selectivity. O_FIG O_LINKSMALLFIG WIDTH=196 HEIGHT=200 SRC="FIGDIR/small/360925v2_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1f0db64org.highwire.dtl.DTLVardef@1d3ac43org.highwire.dtl.DTLVardef@1d270faorg.highwire.dtl.DTLVardef@f7614f_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LISingle amino acid truncations of CD8-hinge domain provide precise control of CAR antigen sensitivity C_LIO_LITruncated hinge CARs show enhanced selectivity for antigen overexpressing tumour cells and decreased activity towards healthy antigen-expressing cells C_LIO_LIEpitope location is a critical factor in determining hinge sensitivity for a CAR C_LIO_LIHinge tuning can modulate CAR-T antigenic sensivity in vitro and in vivo C_LI

immunology↗