Artificial Intelligence-Powered Molecular Docking and Steered Molecular Dynamics for Accurate scFv Selection of Anti-CD30 Chimeric Antigen Receptors

Autor: Nico Martarelli, Michela Capurro, Gizem Mansour, Ramina Vossoughi Jahromi, Arianna Stella, Roberta Rossi, Emanuele Longetti, Barbara Bigerna, Marco Gentili, Ariele Rosseto, Riccardo Rossi, Chiara Cencini, Carla Emiliani, Sabata Martino, Marten Beeg, Marco Gobbi, Enrico Tiacci, Brunangelo Falini, Francesco Morena, Vincenzo Maria Perriello
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: International Journal of Molecular Sciences, Vol 25, Iss 13, p 7231 (2024)
Druh dokumentu: article
ISSN: 1422-0067
1661-6596
DOI: 10.3390/ijms25137231
Popis: Chimeric antigen receptor (CAR) T cells represent a revolutionary immunotherapy that allows specific tumor recognition by a unique single-chain fragment variable (scFv) derived from monoclonal antibodies (mAbs). scFv selection is consequently a fundamental step for CAR construction, to ensure accurate and effective CAR signaling toward tumor antigen binding. However, conventional in vitro and in vivo biological approaches to compare different scFv-derived CARs are expensive and labor-intensive. With the aim to predict the finest scFv binding before CAR-T cell engineering, we performed artificial intelligence (AI)-guided molecular docking and steered molecular dynamics analysis of different anti-CD30 mAb clones. Virtual computational scFv screening showed comparable results to surface plasmon resonance (SPR) and functional CAR-T cell in vitro and in vivo assays, respectively, in terms of binding capacity and anti-tumor efficacy. The proposed fast and low-cost in silico analysis has the potential to advance the development of novel CAR constructs, with a substantial impact on reducing time, costs, and the need for laboratory animal use.
Databáze: Directory of Open Access Journals
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