De novo design of ligand binding proteins using large language models alone
1 Off-the-shelf LLMs (ChatGPT, Claude, Gemini) were used as the only “design engine” to create de novo proteins that bind Zn2+/Cu2+ or the PFAS pollutant perfluorooctanoic acid (PFOA), without providing 3D coordinates or running energy-based optimization during generation.
2 A key deliverable wasn’t just sequences: the LLMs also produced explicit, human-readable rationales (heptad-repeat patterning, helix packing, loop architecture, metal coordination preferences, and ligand-binding logic), positioning LLM output as both designs and an interpretable design hypothesis.
3 Metal-binding campaign: with an ~800-word prompt, the models were asked to design ~60-aa helix-turn-helix monomers that homodimerize into a 4-helix bundle, placing two metal sites and keeping N/C termini within ~15 Å, while avoiding copying known sequences/motifs.
4 Across 80 total sequences (10 designs per metal × 4 LLM variants), BLASTp and PDB similarity searches found no close matches, suggesting novelty; different LLMs explored distinct sequence “styles” (e.g., Gemini more repetitive/charged, Claude more diverse/asymmetric, ChatGPT-base higher N/S/T).
5 Structures were assessed post hoc using AlphaFold3 and Chai-1; predicted confidence was generally low-to-moderate (far below typical design cutoffs), yet many sequences still adopted the intended helix-turn-helix / bundle-like topology, showing that fold-level constraints can emerge from text-only reasoning.
6 Experimental validation for metal binders: 12 candidates were selected; 6 expressed and purified; 3 showed helical CD spectra and dimer-like SEC behavior. Zn2+ selectively stabilized these proteins vs Cu2+ in thermal denaturation, consistent with cognate binding.
7 One design (LMBP-11) had Zn2+ binding validated by ITC with an apparent KD ~30 µM. Notably, successful binders often deviated from the prompt (e.g., using His/Asp/Asp rather than His-only first-shell ligands), highlighting gaps between LLM-stated rationale, generated sequences, and realized 3D geometry.
8 PFOA campaign: initial prompt (<200 words, including PFOA SMILES) requested a 5-helix bundle with a hydrophobic cavity for the fluorous tail and charged loop residues for the carboxylate head, binding two PFOA molecules. First-round pentamer designs expressed and folded, but showed minimal PFOA-induced 19F NMR changes.
9 Iteration mattered: guided by α-helical barrel principles (Woolfson lab) and an expanded repeat pattern, a second round produced 6-helix bundles with higher predicted confidence and more consistent ligand placement. 2/8 tested hexamer designs (LPB-hex-175, LPB-hex-186) caused strong 19F NMR line broadening/peak-area reduction for key PFOA resonances at sub-stoichiometric protein, consistent with binding.
10 Comparison to structure-based redesign: ProteinMPNN/LigandMPNN redesigns on LLM-generated backbones yielded higher-confidence predicted models and very different sequences, implying LLMs can generate feasible but suboptimal sequences and may sample a distinct, rule-driven region of sequence space.
📜Paper:
biorxiv.org/content/10.64898…
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