AI-Designed Peptides Take Aim at an Aggressive Liver Cancer Target

Hepatocellular carcinoma, the most common form of liver cancer, continues to rank among the deadliest malignancies worldwide, and clinicians have long sought molecular handles that could be exploited for targeted diagnosis and therapy. A team of researchers from Nanjing University, Hefei University of Technology, and Suzhou Municipal Hospital has now reported an entirely computational pipeline that designs and prioritizes short peptide molecules capable of binding glypican-3, a protein that sits prominently on the surface of liver cancer cells but is largely absent from healthy adult tissue. The study, published in the journal 3 Biotech, demonstrates how modern artificial intelligence tools for protein design can be chained together to move from a static structural database entry all the way to a shortlist of drug-like molecules, without a single wet-laboratory experiment being performed.

Glypican-3, often abbreviated as GPC3, is a member of the glypican family of cell-surface proteins that are anchored to the outer membrane by lipid chains. It has attracted intense interest in oncology because it is overexpressed in a large fraction of hepatocellular carcinomas, and its position on the extracellular surface of tumor cells makes it physically accessible to circulating drugs, imaging agents, and engineered immune cells. Because GPC3 participates in growth signaling pathways, including interactions with Wnt ligands, it is not merely a passive marker but an active participant in tumor biology. Antibodies, peptide radiopharmaceuticals, and antibody-drug conjugates directed against GPC3 are already in various stages of clinical development, which underscores both the value of the target and the appetite for better binding molecules.

The rationale for pursuing peptides rather than full antibodies is practical as much as biological. Peptides are small, chemically definable, relatively inexpensive to manufacture, and can penetrate tumor tissue more readily than large protein therapeutics. They can serve as homing devices that carry radioactive labels for positron emission tomography imaging, deliver cytotoxic payloads directly to cancer cells, or recruit immune effectors to the tumor site. However, discovering peptides that bind a chosen surface patch of a protein with high affinity and specificity has historically depended on screening enormous random libraries, an approach that is costly and frequently yields candidates with disappointing performance. Recent breakthroughs in computational protein design have opened an alternative route, and the new study is a showcase of that route applied to a clinically important cancer target.

The researchers began with a high-resolution three-dimensional structure of human glypican-3 deposited in the Protein Data Bank under the identifier 7ZAW. Rather than treating the protein as a rigid object, they first allowed it to breathe. A 100-nanosecond molecular dynamics simulation of the unbound, or apo, form of GPC3 was run using the GROMACS engine, capturing the natural fluctuations of the protein in solution. The team then clustered the resulting trajectory and selected the centroid of the most populated cluster, which accounted for 35.6 percent of the stable simulation frames, as a representative conformation for all subsequent design work. This step matters because a protein’s shape in solution can differ subtly from its crystal form, and designing against a realistic, dynamically averaged structure can improve the odds that a designed binder actually fits the real target.

Within that representative structure, the team pinpointed three residues that form the core of the intended binding site: tryptophan 260, tyrosine 264, and methionine 269. These aromatic and sulfur-containing side chains create a distinctive hydrophobic and stacking surface that a complementary peptide could grip. Selecting such coordinate-resolved hotspot residues is a common strategy in structure-guided drug design, because anchoring an interaction at energetically favorable points on a protein surface is far more effective than relying on diffuse, nonspecific contacts across a broad interface.

The generative phase of the pipeline relied on two deep-learning tools that have transformed protein engineering in recent years. RFdiffusion, developed in David Baker’s laboratory, uses a diffusion model akin to those behind modern image generators, but it operates in the space of protein backbone geometry, producing brand-new shapes that never existed in nature. The researchers instructed RFdiffusion to generate one hundred peptide backbones positioned around the identified hotspot surface of GPC3. Each backbone was then handed to ProteinMPNN, a companion neural network that designs amino acid sequences predicted to fold stably into a given backbone. The result was a nonredundant library of peptide sequences, each one a candidate molecule with a computationally specified three-dimensional shape tailored to nestle against the chosen patch of glypican-3.

With thousands of theoretical candidates in hand, the team needed to rank them, and they employed a sequence-level prediction model called PepPAP for the first screening pass. The authors are careful to note that the PepPAP output was treated strictly as a relative computational prediction value, not as an experimentally measured binding free energy, an important methodological caveat that distinguishes predicted affinity from physical reality. The top 120 sequences ranked by PepPAP were then subjected to Rosetta FlexPepDock, a physics-based refinement protocol that models the detailed atomic interface between a peptide and its receptor. FlexPepDock generated 600 receptor-peptide poses across the candidate set, and 565 of those poses satisfied the geometry and contact criteria the team had imposed, indicating that the diffusion-generated backbones were largely compatible with realistic binding geometries.

Binding affinity alone does not make a drug candidate, so the pipeline incorporated two additional filters drawn from different corners of bioinformatics. The top 30 docked peptides were evaluated with Protein-Sol, a web tool that predicts the solubility of a protein or peptide from its sequence, and with ToxinPred 3.0, a machine-learning predictor of peptide toxicity. These screens address two of the most common reasons promising molecules fail downstream: poor behavior in aqueous biological environments and unintended harmful interactions with healthy tissue. Twenty-one of the thirty peptides passed both filters, and an integrated ranking across docking energy, predicted affinity, solubility, and toxicity condensed the field to five final priority candidates.

The energetics of those five finalists are encouraging on paper. Their Rosetta interface energies ranged from −20.459 to −18.255 Rosetta Energy Units, values that reflect substantially favorable computed interactions at the peptide-GPC3 interface. The authors are explicit that these molecules remain computationally prioritized candidates that await experimental validation, and that caution is well placed in a field where previous GPC3-targeting peptides have faltered on close inspection. A recent independent characterization of TJ12P2, a published glypican-3-binding peptide, found a lack of the specificity and potency originally claimed, a sobering reminder that computational or early-stage promise must be confirmed rigorously in the laboratory before a candidate can be taken seriously.

Even so, the significance of the workflow lies in its efficiency and its transparency. The entire study was conducted with publicly available or academically accessible software, including GROMACS, RFdiffusion, ProteinMPNN, PepPAP, Rosetta FlexPepDock, Protein-Sol, and ToxinPred3.0, meaning that other research groups could reproduce the approach against different targets at minimal cost. It consumed no animal subjects, no human materials, and only publicly deposited structural data, which is why the authors report that ethical approval was not required. If experimental binding assays, cellular studies, and ultimately animal imaging experiments confirm that any of the five candidates grips glypican-3 selectively on liver cancer cells, the peptides could be developed into targeted imaging probes, drug-delivery vehicles, or components of theranostic platforms for hepatocellular carcinoma. Even before that, the study adds to a fast-growing body of evidence that diffusion-based generative design, coupled with physics-based refinement and multilayered filtering, can compress years of trial-and-error ligand discovery into a rational, replicable computational procedure, and it offers a template that could be pointed at surface targets in many other cancers in short order.

Subject of Research: Computational de novo design of glypican-3-targeting peptides for hepatocellular carcinoma

Article Title: Structure-based development of Glypican-3-targeting peptide candidates for hepatocellular carcinoma

Article References: Zhang, H., Qiu, J., Cheng, L., Li, C., Xu, J., Qin, Z., & Gao, Y. (2026). Structure-based development of Glypican-3-targeting peptide candidates for hepatocellular carcinoma. 3 Biotech, 16(11), Article 461. https://doi.org/10.1007/s13205-026-05092-4

Image Credits: AI Generated

DOI: 10.1007/s13205-026-05092-4

Keywords: glypican-3, hepatocellular carcinoma, peptide design, RFdiffusion, ProteinMPNN, molecular docking, Rosetta FlexPepDock, molecular dynamics, drug discovery, liver cancer, artificial intelligence, computational biology