Update README.md

README.md

@@ -5,7 +5,8 @@ license: cc-by-nc-nd-4.0
 # Metalorian: *De Novo* Generation of Heavy Metal-Binding Peptides with Classifier-Guided Diffusion Sampling
 
 ## Abstract
-Heavy metal contamination is a severe and ongoing global problem that demands selective and efficient chelation strategies for environmental remediation. As a solution, we introduce **Metalorian**, a conditional diffusion model that generates *de novo* heavy metal-binding peptides, guided by **MetaLATTE**, a multi-label classifier fine-tuned to include underrepresented metal classes. Leveraging both continuous protein embeddings and discrete metal-binding constraints, Metalorian produces peptides with controllable lengths and user-defined binding specificity while preserving essential physicochemical properties. Crucially, our data augmentation strategy enables robust classification and generation even with sparse training data for certain metals. We demonstrate the effectiveness of our approach through peptides designed for copper (Cu), cadmium (Cd), and zinc (Zn), which retain key features such as net charge and hydrophobicity, while significantly reducing sequence length and molecular weight compared to known metal-binding proteins. Metalorian-generated peptides exhibit stable conformations and favorable binding energetics, as confirmed through molecular dynamics simulations. To validate binding *in vitro*, we developed a streamlined SUMO-fusion expression and cleavage system coupled with ELISA-based quantification, confirming robust Cu and Zn binding by multiple Metalorian-generated peptides. Overall, our work establishes a foundational platform for engineering heavy metal-binding peptides tailored to diverse and underrepresented targets, and highlights the potential of well-trained continuous latent spaces for diffusion-based *de novo* peptide design.
+Heavy metal contamination threatens food production, ecosystems, and human health, creating a need for chelators that remove target contaminants without depleting essential metals. Existing chelators often lack this selectivity, while natural metal-binding proteins remain difficult to repurpose because of their size, folding complexity, and metal promiscuity. To address these limitations, we introduce **Metalorian**, a conditional diffusion model that co-evolves protein language model representations and metal labels to generate *de novo* metal-binding peptides. We guide generation with **MetaLATTE**, a multi-label sequence classifier trained across 14 metal classes. MetaLATTE achieves class-specific AUCROC values ranging from 0.86 to 0.99, enabling Metalorian to generate compact Cu-, Cd-, Zn-, and Ni-binding peptides enriched in metal-coordinating residues. Isothermal titration calorimetry confirmed target-metal binding for selected designs, with buffer-corrected *K*<sub>D</sub> values of 19 nM to 0.96 μM and a 68-fold Zn-over-Ni preference for Zn-directed peptides. Finally, in an independent screen, ten chemically distinct Ni-directed designs displaced Ni from the PAR metal indicator; all ten showed stronger apparent competition for Ni over Co and nine did so relative to Zn. Together, these results connect conditional peptide generation with experimental affinity measurements and cross-metal competition screening, supporting a strategy for designing selective peptide chelators for environmental remediation and metal recovery.
+
 <div align="center">
   <img src="figures/pipeline2_new.png" alt="in silico workflow" width="600">
   <p><em>Figure 1: In silico workflow</em></p>
@@ -16,3 +17,14 @@ Heavy metal contamination is a severe and ongoing global problem that demands se
   <p><em>Figure 2: In vitro workflow</em></p>
 </div>
 
+## Code, checkpoints, and data
+
+The computational release associated with Zenodo record 22960654 is
+available in this repository under `Computational/`.
+
+See [the computational README](Computational/README.md) for setup and
+usage instructions.
+
+The archived computational release and experimental raw data are
+available at [Zenodo](https://doi.org/10.5281/zenodo.22960654).
+