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New AI framework FoldScape predicts protein structures across folding stages

Researchers have developed FoldScape, a deep learning framework capable of predicting protein conformational ensembles across various folding stages, from unfolded states to native structures. Unlike previous methods that focused solely on final conformations, FoldScape generates these ensembles by conditioning on folding progress. The framework demonstrates significant advantages over existing tools like AlphaFold 3 in terms of conformational-space coverage, particularly in metrics such as radius of gyration and fraction of native contacts. AI

IMPACT Enables more comprehensive understanding of protein dynamics beyond just final structures, potentially accelerating drug discovery and biological research.

RANK_REASON The cluster is about a new scientific paper detailing a novel AI framework for protein structure prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New AI framework FoldScape predicts protein structures across folding stages

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The cluster is about a new scientific paper detailing a novel AI framework for protein structure prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Lisheng Zhang, Qingsong Li, Zinan Xie, Yanting Yang, Qikui Yang, Peng Lu, Zhican Fu, Wentao Cui, Runtong Hou, Zhengwei Xie ·

    Predicting protein structures at any folding stage from sequence

    arXiv:2611.04287v2 Announce Type: replace-cross Abstract: Protein structure prediction has largely focused on native conformations, leaving the continuum of unfolded states, folding intermediates, and native structures difficult to access. We present FoldScape, a sequence-conditi…