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Related Experiment Video

Updated: Sep 20, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Unifying sequence-structure coding for advanced protein engineering via a multimodal diffusion transformer.

Xiaohan Lin¹, Zhenyu Chen¹, Yanheng Li¹

¹Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University Beijing 100871 China gaoyq@pku.edu.cn.

Chemical Science

|May 26, 2025

Summary

This summary is machine-generated.

This study introduces ProTokens and PT-DiT, a novel approach for protein engineering. By integrating sequence and structure data, it streamlines the design of functional proteins.

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Area of Science:

Computational biology
Protein engineering
Machine learning

Background:

Protein engineering requires integrated sequence-structure data for designing novel proteins.
Existing methods often rely solely on sequence or structure, limiting success rates.

Purpose of the Study:

To develop a unified strategy for protein engineering by integrating sequence and structure information.
To introduce ProTokens and PT-DiT for enhanced protein design.

Main Methods:

Developed ProTokens, machine-learned representations of amino acids from structural databases using self-supervised learning.
Built PT-DiT, a multimodal diffusion transformer model integrating sequence and structure into a unified representation.

Main Results:

ProTokens provide a compact, information-rich representation bridging sequence and structure.
PT-DiT enables protein engineering in a joint sequence-structure space, streamlining design.
The model facilitates encoding 3D folds, contextual design, and directed evolution.

Conclusions:

PT-DiT offers a unified solution for *in silico* protein engineering.
Leveraging both sequence and structure insights enables the realization of functional protein design.