Metalorian: De Novo Generation of Heavy Metal-Binding Peptides with Classifier-Guided Diffusion Sampling

Overview

Zhang and colleagues (University of Pennsylvania, Duke-NUS Medical School, Duke University) report a two-component computational platform for de novo design of heavy-metal-binding peptides: MetaLATTE, a multi-label classifier built on the ESM-2 protein language model that predicts metal-binding propensity across 14 transition/heavy metals from sequence alone, and Metalorian, a co-evolving conditional diffusion model that uses MetaLATTE as a guidance classifier to generate short metal-binding peptides (30-80 residues) with controllable length and target-metal specificity. The pipeline was validated in two stages: (i) in silico molecular-dynamics simulations comparing generated Cu- and Cd-binding peptides to wild-type metal-binding proteins, and (ii) in vitro ELISA-based binding assays for three SUMO-fusion-expressed Metalorian-generated constructs (two Cu binders and one Zn binder) against Cu- or Zn-coated microplates. The MbPA training database covers fourteen metal labels (Ag, cadmium (Cd), Co, Cu, Fe, mercury (Hg), Mn, Mo, nickel (Ni), lead (Pb), Pt, V, W, Zn). The paper is a methods-and-design contribution; there are no food, cosmetic, or human-exposure measurements. It is a bioRxiv preprint posted 2025-07-10 and not certified by peer review.

Why this matters

• It is the first published platform that uses a protein-language-model-based classifier together with a diffusion model to generate, by sequence design alone, candidate heavy-metal chelators for cytotoxic targets including Cd. Prior peptide-design work in the wiki’s chelation/bioremediation literature (Peptides Used for Heavy Metal Remediation: A Promising Approach, A bioinformatics approach to design minimal biomimetic metal-binding peptides, Studying Peptide-Metal Ion Complex Structures by Solution-State NMR) relies either on fixed natural scaffolds (metallothionein, phytochelatin), bioinformatic mining of known metal-binding fragments, or directed evolution; Metalorian extends the space to fully de novo sequence generation conditioned on metal label.
• The MbPA training-set proportions reported in Figure 1A document a severe class imbalance across heavy metals: Zn (21,423 binders) and Fe (17,805) dominate; V (6), W (10), Ag (10), Pt (11), Mo (14), Pb (90), Cd (131), Hg (149) and Co (197) are under-represented. The paper’s MetaLATTE classifier achieves AUCROC 0.86-0.99 across all 14 metals despite this imbalance, which is the load-bearing claim for using the classifier as a guidance signal. The class-imbalance numbers are useful as a structural fact about the available metal-binding-protein training data — they reflect how much primary literature exists for each metal’s binding biology, not how much exposure data exists.
• For HMI’s metal taxonomy, the paper’s experimental validation directly addresses Cu and Zn (both in vitro binders confirmed via ELISA); the in silico generation addresses Cd, Co, and Ni in addition. The discussion frames the platform as a bioremediation tool — generating peptides for environmental remediation of toxic metals at industrial-scale contaminated sites — rather than a dietary or supply-chain intervention. Relevance for the wiki is to peptide-based chelation chemistry, not to food matrices.
• The paper is methodologically transparent: model weights, code, and an inference pipeline are released at https://huggingface.co/ChatterjeeLab/MetaLATTE, https://huggingface.co/spaces/ChatterjeeLab/MetaLATTE-demo, and https://huggingface.co/ChatterjeeLab/Metalorian. This places it within reach for future external validation against larger benchmark sets and for combination with peer-reviewed peptide-chelation literature already in the wiki.

Key numbers

Training-data composition (Figure 1A; MbPA database; Methods, “Metal-Binding Protein Data Curation”; pp. 2, 7).

Bars on Figure 1A are plotted in ascending count order; x-axis (left to right) is V, Ag, W, Pt, Mo, Pb, Cd, Hg, Co, Ni, Cu, Mn, N/A, Fe, Zn with the corresponding bar-top counts 6, 10, 10, 11, 14, 90, 131, 149, 197, 829, 913, 3,839, 5,230, 17,805, 21,423.

Classifier performance (MetaLATTE; Figure 1D, Figure 2B; Results, “MetaLATTE enables accurate classification of peptide binders for cognate heavy metals”; p. 2).

Mollusk-protein test case (Methods/Results; Figure 2C; p. 4).

Metalorian generation parameters (Results; Methods; pp. 4, 9-10).

Generated-versus-wildtype property comparison (Figure 3B; Results; p. 4-5).

MD-simulation outcomes for two named generated peptides (Results, “Metalorian-generated peptides exhibit potent binding capacities in silico”; Figure 3C; p. 5).

In vitro ELISA validation (Results, “Metalorian-generated proteins bind Cu and Zn in vitro”; Figure 4; Methods, p. 13).

Methods (brief)

Datasets. Metal-binding-protein training data drawn primarily from the MbPA database, restricted to fourteen transition/heavy metals (Ag, Cd, Co, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Pt, V, W, Zn); labels with fewer than 6 protein samples were excluded. Negative (non-binding) examples drawn from the Mpbipred database. Three benchmark test sets used: a stratified validation split from a stage-2 contaminated set, a non-overlapping MetalPDB extract, and a newly-sequenced mollusk dataset from Calatayud et al. 2021.

MetaLATTE classifier. Two-stage fine-tuning on top of ESM-2-650M (last two transformer layers unfrozen) with attention-pooling layer and rotary position embeddings before the classification head. Stage 1: class-balanced focal loss + F1 loss + cross-entropy reconstruction loss on the balanced multi-label task. Stage 2: triplet loss with adaptive margins over synthetically-mutated (alanine-swap and BLOSUM62-substituted) “semi-hard” and “hard” negatives, to enforce site-specific sensitivity. Triplet sampling progresses from easy to hard negatives during training. Stage 1 trained for up to 30 epochs (early stopping on validation macro-F1); Stage 2 trained ~15 epochs. Hardware: 32 GPU-hours on a 7× NVIDIA A6000 DGX server (350 GB shared VRAM) for stage 1; 56 GPU-hours on the same hardware for stage 2.

Metalorian generator. Co-evolving conditional diffusion (CoDi-style; Lee et al. 2023) over two coupled processes: a continuous diffusion over MetaLATTE’s ESM-2 embeddings (x_C ∈ ℝ^(B×L×1280)) and a discrete multinomial diffusion over a one-hot metal label vector (x_D ∈ ℝ^(B×15), 14 metals plus N/A) using a TabularUnet backbone. Bidirectional conditioning between the two processes at every timestep. Joint loss combines per-process diffusion losses with two contrastive (triplet) terms over matched/mismatched (embedding, label) pairs. Trained on 7 × A100 GPUs at batch size 140, learning rate 2e-4 (AdamW), PyTorch Lightning. Sampling supports controllable peptide length via masking.

Sampling strategies. Progressive Verification Sampling for well-represented metal classes (Zn, Fe, Mn): standard diffusion sampling in phase one followed by phase-two verification timesteps where predictor confidence (MetaLATTE P(x_t)[y_target]) and discrete-label alignment (argmax(x_t^D) = y_target) must agree. Gradient-Guided Sampling for under-represented metals (Cd, Pb, Pt, V, W, Ag): extension of classifier guidance (Dhariwal & Nichol 2021) with dynamic scaling — guidance scale λ doubles when predictor confidence falls below threshold τ.

MD simulations. AlphaFold3 (2024.08.19 build) initial structures; AutoDock VINA docking for metal-ion placement; AMBER ToolS 24 system prep with MCPB.py and GAMESS-US for bonded/nonbonded parameters and RESP charge derivation. Most transition metals used the 6-31G(d,p)/LANL2TZ basis set; Cd used SBKJC-ECP for tighter convergence on its larger atomic radius. TIP3P water with Na⁺/Cl⁻ at 0.155 mM [sic — the preprint reads “0.155 mM,” but physiological PBS ionic strength is 155 mM NaCl; likely a typo in the manuscript]; AMBER19SB protein + GAFF other atoms; GROMACS 5.0 workflow (energy minimization → NVT → NPT → 2.5 ns production NPT at 300 K, 1 bar, c-rescale barostat). gmx_MMPBSA for binding free energy; PyEMMA for TICA conformational analysis.

SUMO-fusion expression and ELISA validation. Constructs designed with N-terminal 6xHis-SUMO tag and modified TEV protease cleavage site (vector derived from Chen et al. 2023a); Gibson Assembly with NEB enzymes; Sanger-verified inserts; transformation into BL21(DE3); IPTG induction (1 mM at OD₆₀₀ 0.6-0.8); Ni-NTA affinity purification; TEV cleavage and reverse His-purification; biotinylation with EZ-Link Micro Sulfo-NHS-Biotinylation Kit; SDS-PAGE and anti-His Western blot for purity confirmation. Sandwich ELISA: blocked Cu- or Zn-coated bioWORLD microplates incubated with serial dilutions of biotinylated constructs (10 µg mL⁻¹ → 0.0001 µg mL⁻¹) in PBS-T; detection via HRP-streptavidin (1:20,000) and TMB; absorbance at 450 nm read on a Promega GloMax Discover plate reader; IC₅₀ fit by four-parameter sigmoid (GraphPad Prism v10).

Implications

Microbiome: Not applicable. No microbiota or microbial-community measurements; bacterial work is limited to E. coli BL21(DE3) as an expression host.

Related evidence

• Cadmium
• Copper
• Zinc
• Cobalt
• Nickel

Update history

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