{Reference Type}: Journal Article
{Title}: Artificial Intelligence-Powered Molecular Docking and Steered Molecular Dynamics for Accurate scFv Selection of Anti-CD30 Chimeric Antigen Receptors.
{Author}: Martarelli N;Capurro M;Mansour G;Jahromi RV;Stella A;Rossi R;Longetti E;Bigerna B;Gentili M;Rosseto A;Rossi R;Cencini C;Emiliani C;Martino S;Beeg M;Gobbi M;Tiacci E;Falini B;Morena F;Perriello VM;
{Journal}: Int J Mol Sci
{Volume}: 25
{Issue}: 13
{Year}: 2024 Jun 30
{Factor}: 6.208
{DOI}: 10.3390/ijms25137231
{Abstract}: 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.