Investigating the structural changes in amino acids conformations interacting with a toxic effector molecule within the Hcp1 tail/tube complexes of the type VI secretion system using artificial intelligence and deep learning platform

Background: The primary objective of this study is to understand how a putative toxic effector of the type VI secretion system (T6SS) in Acinetobacter baumannii triggers the contraction of the Hcp1 nanotube through the application of an Artificial Intelligence (AI) and Deep Learning (DL) framework. Furthermore, the virtual assessment of components associated with this secretory system was also investigated. Methods: The amino acid sequences of T6SS components were retrieved from the GenBank database. AI software such as AlphaFold2, and neural DL software like Rosetta-Fold were used to generate the 3D structures of T6SS protein components. The Rosetta Packer (DLP) program was employed to identify the side chains of the amino acids involved in binding to the effector. A backbone-dependent rotamer library for amino acids side chains was developed based on the Dunbrack rotag package. Results: Through the machine learning AI system, it has been found that each specific effector molecule binds specifically toa particular set of amino acids (in this case; Lys, Phe, Arg, and His) within the Hcp1 monohexameric ring by H-bound. This interaction induces a rotameric shift in the dihedral angles (Φ/Ψ) of the aforementioned amino acids' side chains launching the contraction of the Hcp1 tail/tube complex and injection effector molecule to the prey cell. Furthermore, TssB/C, TssM, and ClpV ATPase T6SS components are essential for the propulsion of the effector molecule. Conclusion: This study reveals the mechanism by which the effector molecule induces changes in the conformational of the amino acid side chain from the apo to the hollo state along the Hcp1 tail, resulting in the contraction and propulsion of the substrate into the target cell.