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New graph algorithm predicts fluorescent protein quantum yield with high accuracy

Researchers have developed a novel algorithm that utilizes 3D mechanism graphs to predict the quantum yield of fluorescent proteins. This method focuses on the chromophore and its immediate environment, moving beyond sequence-based predictions. The algorithm achieved superior performance on a benchmark dataset, outperforming existing models like ESM-C and SaProt, particularly in predicting yields for proteins with low sequence similarity to known structures. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces a new graph-based approach for protein property prediction, potentially improving accuracy over existing language models.

RANK_REASON This is a research paper detailing a new algorithm for a scientific prediction task.

Read on arXiv cs.LG →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Yuchen Xiong, Swee Keong Yeap, Steven Aw Yoong Kit ·

    Edge-specific signal propagation on mature chromophore-region 3D mechanism graphs for fluorescent protein quantum-yield prediction

    arXiv:2605.06644v1 Announce Type: new 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, b…

  2. arXiv cs.LG TIER_1 · Steven Aw Yoong Kit ·

    Edge-specific signal propagation on mature chromophore-region 3D mechanism graphs for fluorescent protein quantum-yield prediction

    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 o…