🤖 AI Summary
This study addresses the challenge of modeling how the quantum yield of fluorescent proteins is governed by their chromophore and its local three-dimensional microenvironment, a task hindered by the difficulty of capturing region-specific physical signals with existing methods. To overcome this, the authors propose a chromophore-centered, typed 3D residue graph representation that incorporates spatial partitioning and a channel–signal–region propagation mechanism, enabling interpretable modeling of edge-specific physical signals and directly revealing wavelength-dependent interaction mechanisms. Coupled with non-identity feature filtering and ExtraTrees regression, the approach achieves a cross-validated R value of 0.772 on a benchmark set of 531 proteins, significantly outperforming current state-of-the-art methods—particularly excelling in tasks involving distantly related homologs (<50% sequence identity) and high-brightness screening.
📝 Abstract
Fluorescent protein quantum yield (QY) is governed by the mature chromophore and its three-dimensional microenvironment rather than sequence identity alone. Protein language models and emission-band averages capture global trends, but do not model how local physical signals act on specific chromophore regions.
We present a chromophore-centred mechanism graph algorithm for QY prediction. Each PDB structure is converted into a typed 3D residue graph, registered to a mature-CRO state, partitioned into phenolate, bridge and imidazolinone regions, and transformed by channel-signal-region propagation. The representation contains 121 enrichment features; after removing identity shortcuts, 52 non-identity features are used for band-specific ExtraTrees regression. Because each feature encodes a contact channel, seed signal and target CRO region, interpretation is intrinsic rather than post hoc. On a 531-protein benchmark, the method achieved the best random-CV performance among model-based baselines (R = 0.772 +/- 0.008, MAE = 0.131 +/- 0.002), exceeding Band mean (R = 0.632), ESM-C (R = 0.734) and SaProt (R = 0.731), and ranked first in bright screening (Bright P@5 = 0.704). Under homology control, the advantage was clearest in the remote bucket (<50% similarity; R = 0.697 versus 0.633, 0.575 and 0.408), with the strongest overall bright/dark Top-K screening. Stable selected features recovered band-specific mechanisms: aromatic packing and clamp asymmetry in GFP-like proteins, charge/clamp balance in Red proteins, and flexibility-risk/bulky-contact features in Far-red proteins.
Source code, feature tables and evaluation scripts are available from the first author upon request. Contact: yuchenak05@gmail.com