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

• Published in: International journal of molecular sciences Vol. 25; no. 13; p. 7231
• Main Authors: Martarelli, Nico, Capurro, Michela, Mansour, Gizem, Jahromi, Ramina Vossoughi, Stella, Arianna, Rossi, Roberta, Longetti, Emanuele, Bigerna, Barbara, Gentili, Marco, Rosseto, Ariele, Rossi, Riccardo, Cencini, Chiara, Emiliani, Carla, Martino, Sabata, Beeg, Marten, Gobbi, Marco, Tiacci, Enrico, Falini, Brunangelo, Morena, Francesco, Perriello, Vincenzo Maria
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01-07-2024
• Subjects: Animals; Antigens; Artificial Intelligence; artificial intelligence (AI); chimeric antigen T cells (CAR-T); coarse grained umbrella sampling (CG-US); Flow cytometry; Hodgkin’s lymphoma; Humans; Hydrogen bonds; Immunotherapy; Ki-1 Antigen - immunology; Ki-1 Antigen - metabolism; Lymphocytes; Lymphoma; Machine learning; Medical research; Mice; molecular docking (MD); Molecular Docking Simulation; Molecular dynamics; Molecular Dynamics Simulation; Monoclonal antibodies; Protein Binding; Proteins; Receptors, Chimeric Antigen - genetics; Receptors, Chimeric Antigen - immunology; Receptors, Chimeric Antigen - metabolism; Single-Chain Antibodies - chemistry; Single-Chain Antibodies - genetics; Single-Chain Antibodies - immunology; steered molecular dynamics (SMD); Surface Plasmon Resonance; T cells; California; CD30; artificial intelligence (AI); Hodgkin’s lymphoma; molecular docking (MD); chimeric antigen T cells (CAR-T); coarse grained umbrella sampling (CG-US); steered molecular dynamics (SMD)
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms25137231

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.