Scholarlink

<p>The protein inverse folding problem, which is the task of designing an amino acid sequence that will fold into a specified backbone structure, represents a fundamental challenge in <i>de novo</i> protein design. Existing computational methods, including deep learning-based approaches, often fail to simultaneously optimize accuracy, stability, efficiency, and generalizability across diverse folds. Here, we present ProtSeqGen, a deep learning model that overcomes these limitations through a multi-stage graph-based framework. ProtSeqGen encodes protein structures as local geometric graphs, explicitly models residue-level interactions using a message-passing neural network, and predicts optimal amino acids with a multi-layer perceptron. When trained on CATH 4.2 dataset and evaluated on standard and challenging benchmarks, ProtSeqGen achieved superior sequence recovery compared to numerous state-of-the-art (SOTA) methods. It also generated accurate, designable sequences for nine topologically diverse proteins, demonstrating remarkable generalization capability. These results establish ProtSeqGen as a robust and scalable solution to the protein inverse folding problem, propelling <i>de novo</i> protein design with high structural precision.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

ProtSeqGen: a novel deep learning model for protein sequence design

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

查看全文
  • Qiang Gao,
  • Zhijin Li,
  • Yang Deng,
  • Zhiwei Ji

摘要

The protein inverse folding problem, which is the task of designing an amino acid sequence that will fold into a specified backbone structure, represents a fundamental challenge in de novo protein design. Existing computational methods, including deep learning-based approaches, often fail to simultaneously optimize accuracy, stability, efficiency, and generalizability across diverse folds. Here, we present ProtSeqGen, a deep learning model that overcomes these limitations through a multi-stage graph-based framework. ProtSeqGen encodes protein structures as local geometric graphs, explicitly models residue-level interactions using a message-passing neural network, and predicts optimal amino acids with a multi-layer perceptron. When trained on CATH 4.2 dataset and evaluated on standard and challenging benchmarks, ProtSeqGen achieved superior sequence recovery compared to numerous state-of-the-art (SOTA) methods. It also generated accurate, designable sequences for nine topologically diverse proteins, demonstrating remarkable generalization capability. These results establish ProtSeqGen as a robust and scalable solution to the protein inverse folding problem, propelling de novo protein design with high structural precision.