基于靶标结构的环肽分子计算设计

王凡灏; 来鲁华; 张长胜

doi:10.12211/2096-8280.2023-006

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基于靶标结构的环肽分子计算设计

特约评述 | 更新时间：2025-03-20

- 基于靶标结构的环肽分子计算设计
- Target structure based computational design of cyclic peptides
- 合成生物学 2023年4卷第3期页码：551-570
- 作者机构：
  
  1.北京大学前沿交叉学科研究院定量生物学中心，北京 100871
  2.北京大学化学与分子工程学院，北京分子科学国家研究中心，北京 100871
  3.北京大学-清华大学生命科学联合中心，北京 100871
- 作者简介：
  
  [ "王凡灏（1998—），男，博士研究生。研究方向为基于靶标结构的多肽药物分子设计。 E-mail：wangfh2020@stu.pku.edu.cn" ]
  [ "张长胜（1981—），男，副研究员。研究方向为蛋白质设计、计算结构生物学。 E-mail：changshengzhang@pku.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(21977007)
- DOI：10.12211/2096-8280.2023-006
  中图分类号： Q816
- 收稿：2023-01-12，
  
  修回：2023-02-23，
  
  纸质出版：2023-06-30
- 稿件说明：
移动端阅览
王凡灏，来鲁华，张长胜. 基于靶标结构的环肽分子计算设计［J］. 合成生物学， 2023， 4（3）： 551-570

WANG Fanhao， LAI Luhua， ZHANG Changsheng. Target structure based computational design of cyclic peptides［J］. Synthetic Biology Journal， 2023， 4（3）： 551-570
王凡灏，来鲁华，张长胜. 基于靶标结构的环肽分子计算设计［J］. 合成生物学， 2023， 4（3）： 551-570 DOI： 10.12211/2096-8280.2023-006.

WANG Fanhao， LAI Luhua， ZHANG Changsheng. Target structure based computational design of cyclic peptides［J］. Synthetic Biology Journal， 2023， 4（3）： 551-570 DOI： 10.12211/2096-8280.2023-006.

摘要

环肽在调控蛋白质-蛋白质相互作用方面具有独特的优势，在新药研发领域受到了越来越多的关注。蛋白质相互作用界面一般较大而平坦，相较于小分子化合物，环肽分子更容易获得与这些靶标位点结合的高亲和力和高特异性。相较于线性多肽或蛋白质，环肽结构一般具有更大的骨架刚性，更难被酶降解，从而在代谢上更稳定，而且环肽更易于通过修饰改造增加跨膜活性，从而结合细胞内的靶标蛋白。结构数据和结构建模方法是开发基于靶标结构计算设计环肽药物的基础。本文分析了蛋白质结构数据库中环肽与靶标蛋白结合情况，介绍了目前环肽构象生成或结构预测的四类主要算法；总结了基于靶标结构计算设计环肽分子的主要方法，包括基于分子对接的虚拟筛选方法、借助于动力学模拟的设计方法、从头生成的设计方法以及具有跨膜活性的环肽设计方法；并展望了数据驱动的机器学习方法在环肽设计领域中的可能应用以及未来环肽药物分子开发的可能方向。

Abstract

Cyclic peptides (macrocycles) possess head-to-tail cyclic or partially cyclized substructures

which have received more and more attention in developing new drugs recently

since they have unique advantages in regulating protein-protein interactions (PPIs). Comparing to small-molecule compounds

it is easier to design cyclic peptide molecules that bind to target sites with high affinity and specificity

due to the broad and flat interfaces of PPIs and their large surfaces. Moreover

cyclic peptides are generally more rigid and difficult for digestion by proteases than their linear counterparts

making them more stable than linear peptides or proteins. Meanwhile

cyclic peptides are easier for modifications to increase transmembrane activity

targeting intracellular proteins through conformation adaptation or chemical modifications. 3D structure data and structure modeling technics are basis for designing structure based cyclic-peptide drugs. In this revie

we assess the structures of cyclic peptides and target proteins available in protein structure database (PDB). Then

we review the algorithms of conformation generation or structure prediction for cyclic peptides

including homologous modeling

secondary structure prediction and optimization

backbone torsion sampling

and distance geometry method. We also summarize progress in target structure based computational design for cyclic peptides

including structure-based virtual screening

molecular dynamic simulation aided methods

de novo

design algorithms

and the transmembrane cyclic peptide design. However

more generalized structure-based

de novo

design algorithms remains to be further explored

and methods to adopt unnatural amino acids or chemical modifications are also needs to be developed. It's worth noting that

with the increase of data for cyclic peptide 3D structures

the data-driven machine learning method may provide a more promising solution for improving the efficiency and effectiveness of structure based cyclic peptide

de novo

design and conformation generation to develop cyclic peptide drugs in the future.

关键词

Keywords

references

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