Identification of Novel Heptosyltransferase I (HepI) Candidate Hits Through Integrated Computational Approaches: MD-Informed Pharmacophore Modeling, Virtual Screening, and Molecular Docking

Heptosyltransferase I (HepI) catalyzes the transfer of L-glycero-D-manno-heptose from ADP-heptose (ADPH) to the inner Kdo2-lipid A core during lipopolysaccharide (LPS) biosynthesis. This process is essential for outer membrane (OM) integrity in Gram-negative bacteria. As such, HepI represents a promising target for the development of effective antibacterial agents. In this study, an integrated computational approach was employed to identify HepI inhibitor candidates. Four optimized pharmacophore models were subjected to virtual screening against a small-molecule library containing approximately six million compounds prefiltered for physicochemical properties favorable for Gram-negative penetration, followed by molecular docking using MOE, GNINA rescoring, and ADME/drug-likeness profiling to support candidate prioritization. Pharmacophore screening yielded 5, 113, 7146, and 510 unique hits for queries 1–4, respectively. Several compounds showed favorable MOE S-scores compared to the reference ligand AFH (S = −7.83 kcal mol−1), including ZINC000022082637 and ZINC000064971626, with scores of −9.09 and −8.99 kcal mol−1, respectively. GNINA rescoring further showed supportive affinity profiles for selected candidates, including ZINC000019761596, ZINC000001380743, ZINC000014122165, and ZINC000009252534. This approach identified chemically diverse HepI inhibitor candidates from a large Gram-negative-focused compound library. The selected hits showed physicochemical and drug-likeness profiles useful for computational prioritization. As no biochemical or microbiological validation was performed in the present study, these compounds should be regarded as computationally prioritized candidates rather than experimentally confirmed HepI inhibitors.