Identification of Novel Candidate Inhibitors Targeting hBCATm for Acute Liver Failure by In Silico Methods
Year: 2026
Type: preprint
Abstract: Acute liver failure (ALF) is a life-threatening disease characterized by severe metabolic dysregulation and limited treatment approaches. Human mitochondrial branched-chain aminotransferase (hBCATm), involved in branched-chain amino acid metabolism, plays a critical role as a key target in ALF treatment options. This study aimed to derivatize new molecules more effective against hBCATm, perform docking procedures, conduct structure-activity relationship (SAR) analyses, perform molecular dynamics simulations for prominent candidate molecules, and calculate MMGBSA binding free energy using a sequential workflow. In the derivatization process performed based on the molecule coded ZINC000013555421(L-0) selected from the ZINC database, different substituted groups were attached to the determined positions, first to produce drugs, and then, in an iterative cycle, 98 new candidate derivative molecules were generated using binding affinity scores obtained from the docking. Among this generated set of molecules, L-78 and L-87 molecules, showing higher affinity compared to the reference compound Azathioprine, stood out due to their positive interactions and increased hydrophobic complementarity at the hBCATm active site. The stability of the aforementioned ligands in the hBCATm active site and the fundamental interactions of the complex formed over 300 ns were characterized by MD simulations. Finally, MMGBSA binding free energy calculations were performed, supporting the finding that molecules L-78 and L-87 have better binding potential compared to Azathioprine. In conclusion, this study identified two novel molecules that showed favorable predicted binding characteristics for hBCATm, potentially relevant to ALF management using in silico methods. These computational findings warrant further in vitro and in vivo validation to confirm the therapeutic potential of the proposed leads.
Source: Research Square
Institutions: Üsküdar University, Istanbul Technical University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7202037050