%0 Journal Article
%T Artificial Intelligence-Powered Molecular Docking and Steered Molecular Dynamics for Accurate scFv Selection of Anti-CD30 Chimeric Antigen Receptors.
%A Martarelli N
%A Capurro M
%A Mansour G
%A Jahromi RV
%A Stella A
%A Rossi R
%A Longetti E
%A Bigerna B
%A Gentili M
%A Rosseto A
%A Rossi R
%A Cencini C
%A Emiliani C
%A Martino S
%A Beeg M
%A Gobbi M
%A Tiacci E
%A Falini B
%A Morena F
%A Perriello VM
%J Int J Mol Sci
%V 25
%N 13
%D 2024 Jun 30
%M 39000338
%F 6.208
%R 10.3390/ijms25137231
%X 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.