AI-guided screen finds drug-candidate molecules that shield cells from oxygen starvation

When tissues are starved of oxygen, as happens in a heart attack, stroke, or failing kidney, cells scramble to switch on a survival program governed by a protein called hypoxia-inducible factor 1 alpha, or HIF-1α. Under normal oxygen levels, HIF-1α is rapidly flagged for destruction: an enzyme hydroxylates a specific proline on the protein, allowing the von Hippel-Lindau protein, VHL, to grab hold of HIF-1α and hand it over to the cell’s protein-disposal machinery. Block that handshake, and HIF-1α accumulates, switching on genes that help cells endure low oxygen. A team of Chinese researchers has now reported a computational strategy for finding small molecules that disrupt this interaction, and their screen surfaced a surprising hit: an approved cancer drug.

The study, published in the journal Molecular Diversity, was led by Binglin Huang and Bijuan Lin of Fujian Medical University Union Hospital, together with colleagues at Fujian Medical University and Nanjing University of Chinese Medicine. Rather than relying on the well-characterized binding groove that VHL uses to recognize hydroxylated HIF-1α, the team took a different route. They used DDMut-PPI, a graph-based deep-learning tool that predicts how mutations at a protein-protein interface affect the interaction, to nominate residues that might serve as auxiliary contact points beyond the canonical binding site. Those predicted residues were then used to build an alternative pharmacophore model, a simplified three-dimensional map of the chemical features a molecule would need to wedge itself into the interface and interfere with the HIF-1α/VHL handshake.

With that model in hand, the researchers screened a library of 24,893 molecules computationally, using docking tools including AutoDock4, which allows selective receptor flexibility during the docking calculation. Candidates that docked favorably against the alternative pharmacophore were then carried into the laboratory, where a fluorescence-polarization assay measured how well each compound competed with a labeled HIF-1α peptide for binding to VHL. Four molecules emerged with half-maximal inhibitory concentrations, or IC50 values, below 10 micromolar, a respectable starting point for compounds discovered without any structure-guided medicinal chemistry.

The standout was a compound the authors labeled Cmpd16, which turned out to be ixazomib, a proteasome inhibitor already approved for use in multiple myeloma under the trade name Ninlaro. In the fluorescence-polarization assay, Cmpd16 showed the highest affinity of the four hits, with an IC50 of 0.41 micromolar. The finding that an existing drug can bind VHL and interfere with HIF-1α recognition is notable because it opens the possibility of repurposing: a molecule with known pharmacokinetics, safety data, and manufacturing routes could, in principle, be repositioned for a completely different indication, provided the new activity holds up in more demanding biological systems.

The team then asked whether the compound actually did what a VHL inhibitor should do inside cells. In their reported assays, Cmpd16 showed no detectable loss of cell viability, an important early safety signal, and it produced a VHL-dependent pattern of stabilization affecting both HIF-1α and the hydroxylated form of the protein. In other words, when VHL’s grip is loosened, even hydroxylated HIF-1α, which would normally be destroyed regardless of oxygen levels, lingers in the cell. That is precisely the biochemical signature expected from a molecule that blocks the VHL recognition step downstream of the oxygen-sensing hydroxylases, and it mirrors the mechanism of dedicated VHL inhibitors developed over the past decade by academic and industrial groups.

To test whether this molecular effect translated into protection under stress, the researchers used an oxygen-glucose deprivation and reoxygenation model, a standard laboratory mimic of ischemia and reperfusion injury in which cells are deprived of both oxygen and nutrients and then resupplied. At a concentration of 10 micromolar, Cmpd16 improved the migration of endothelial cells and their ability to form tube-like structures, processes central to the formation of new blood vessels that resupply damaged tissue. The treated cells also showed increased levels of vascular endothelial growth factor, VEGF, and the glucose transporter GLUT1, both of which are canonical HIF-1α target genes that support angiogenesis and metabolic adaptation. At the same time, the compound was associated with reduced accumulation of reactive oxygen species and reduced levels of cleaved caspase-3, a hallmark of apoptosis, suggesting that the cells were not only adapting but genuinely surviving the insult better.

Because the initial hit came from a computational model rather than from a crystal structure of a ligand bound in the interface, the authors took care to interrogate how plausible their predicted binding mode really was. They ran three independently initialized molecular dynamics simulations of 200 nanoseconds each using the Desmond engine. Across the replicas, the simulations showed recurring contacts between the ligand and two residues, Pro99 and His110, in VHL, but the overall dynamics of both the protein and the ligand varied from replica to replica. The authors are explicit about what this means: the simulations support a computationally plausible orientation for Cmpd16 rather than a unique, well-defined one. That honesty matters, because molecular dynamics can be seductive, and overstating a single binding pose from a handful of trajectories is a well-known pitfall in computational drug discovery.

The team also compared two selected ten-compound panels to probe whether their deep-learning-informed pharmacophore enriched for active molecules more effectively than a conventional approach. The comparison, analyzed with a two-sided Fisher’s exact test, yielded a p-value of 0.0867, which falls short of conventional statistical significance. The authors describe this analysis as exploratory, and they further caution that the causal role of the predicted interface residues remains unproven until experimental mutagenesis is performed. These caveats are worth emphasizing: the study demonstrates a viable discovery pipeline and a promising hit, but the mechanistic details of where and how the compound binds VHL are still hypotheses awaiting direct experimental confirmation.

The broader context makes the work interesting on several fronts. The HIF pathway has long been a drug-discovery target, though most clinical success has come from a different angle: inhibitors of the prolyl hydroxylase enzymes, such as daprodustat and enarodustat, which are approved or in late-stage development for anemia in chronic kidney disease. Those drugs prevent HIF-1α from being tagged in the first place. Directly blocking the VHL-HIF interaction is a more recent strategy, and potent chemical probes with nanomolar affinities have been reported, largely built around a conserved hydroxyproline mimetic scaffold. The structural diversity of known VHL ligands has remained narrow, which is exactly the gap the new study set out to address by looking for chemically distinct molecules through an alternative interface model. Finding an approved proteasome inhibitor within that chemically distinct set is an unexpected twist, since ixazomib was designed to inhibit the proteasome’s catalytic activity, not to bind an E3 ubiquitin ligase.

There is also a pleasing irony in the biology. VHL ligands were originally developed largely as tools for targeted protein degradation, forming the binding arm of PROTAC molecules that recruit VHL to destroy disease-causing proteins. Here, the same interaction is being blocked for the opposite purpose: to prevent VHL from destroying a protein the body needs during oxygen crisis. Whether Cmpd16, or molecules inspired by it, can eventually protect tissues in living models of ischemia remains to be seen, and the distance from an endothelial-cell dish to a patient’s heart or kidney is considerable. But the study offers a concrete example of how deep-learning predictions of interface residues can reshape the search space in virtual screening, and it adds a repurposing candidate, and a set of testable residue hypotheses, to a pathway whose pharmacological importance continues to grow. The next steps, mutagenesis of the predicted contact residues, co-crystallography or cryo-EM of the ligand bound to VHL, and validation in animal models of ischemic injury, will determine whether this computational detour leads to a genuinely new way of protecting cells when oxygen runs out.

Subject of Research: Computational discovery of small-molecule inhibitors of the HIF-1α/VHL protein-protein interaction for protection of cells under hypoxic stress

Article Title: Computational discovery of HIF-1α/VHL protein–protein interaction inhibitors for hypoxic cell protection

Article References: Computational discovery of HIF-1α/VHL protein–protein interaction inhibitors for hypoxic cell protection. (n.d.). https://doi.org/10.1007/s11030-026-11706-z

Image Credits: AI Generated

DOI: 10.1007/s11030-026-11706-z

Keywords: HIF-1α, VHL, protein-protein interaction, virtual screening, deep learning, ixazomib, hypoxia, ischemia, molecular dynamics, drug repurposing, endothelial cells, pharmacophore