Year: 2026
Type: preprint
Abstract: The transition to sustainable energy relies heavily on rare earth elements (REEs), yet their separation remains a critical industrial bottleneck due to their nearly identical physicochemical properties. The discovery of lanmodulin (LanM), a highly selective REE-binding protein, has inspired bio-based separation strategies; however, predicting binding affinity between different LanM variants and REEs remains a significant computational challenge. Here, we present LANGER, a multimodal deep learning framework for predicting REE protein binding affinity (Kd) by synchronously integrating global evolutionary context, continuous ionic chemistry, and local three-dimensional coordinate geometry. Our architecture combines ESM2 protein sequence embeddings, REE physicochemical descriptions, and geometric graph learning (GGL) features derived from topologies extracted from protein-ion complexes. Using a comprehensive dataset of 616 evaluated LanM homologs against 15 REE ions, we demonstrate the robustness of this framework across two rigorous evaluation settings. In a cluster-based validation analysis designed to test extrapolation to held-out LanM sequence clusters, LANGER achieved a root mean square error (RMSE) of 0.212 and a concordance index (CI) of 0.757. More importantly, in a rigorous evaluation protocol that completely excludes individual ions from the training phase, the fully integrated multimodal framework achieved high predictive accuracy (RMSE = 0.119, Pearson correlation coefficient r = 0.860, CI = 0.800), demonstrating its unique ability to transfer learned physicochemical trends to unseen lanthanide elements. This work establishes a scalable, computationally accurate structure recognition platform for the discovery and design of data-driven REEselective proteins.
Source: ChemRxiv
Institution: University of Tennessee at Knoxville
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W7213943481