在 OSPREY 中设计新的非简约肽结合剂的方案。

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Henry Childs, Nathan Guerin, Pei Zhou, Bruce R Donald
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引用次数: 0

摘要

D肽是典型L肽的镜像,具有众多生物学优势,是有效的治疗药物。本文详细介绍了如何使用基于 OSPREY 的最新算法 DexDesign 从新设计这些 D 肽。基于OSPREY物理模型的DexDesign可以精确地模拟能变反射操作,从而从L肽模板生成D肽支架。由于 D 肽:L 蛋白结构数据稀缺,DexDesign 调用几何散列算法 "三级集合代表加速搜索法 "作为子程序,生成合成结构数据集。DexDesign 利用新的用户界面实现了混合手性设计,还利用三种新的设计技术缩小了构象和序列搜索空间:最小柔性集、反丙氨酸扫描和基于 K* 的突变扫描。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Protocol for Designing De Novo Noncanonical Peptide Binders in OSPREY.

D-peptides, the mirror image of canonical L-peptides, offer numerous biological advantages that make them effective therapeutics. This article details how to use DexDesign, the newest OSPREY-based algorithm, for designing these D-peptides de novo. OSPREY physics-based models precisely mimic energy-equivariant reflection operations, enabling the generation of D-peptide scaffolds from L-peptide templates. Due to the scarcity of D-peptide:L-protein structural data, DexDesign calls a geometric hashing algorithm, Method of Accelerated Search for Tertiary Ensemble Representatives, as a subroutine to produce a synthetic structural dataset. DexDesign enables mixed-chirality designs with a new user interface and also reduces the conformation and sequence search space using three new design techniques: Minimum Flexible Set, Inverse Alanine Scanning, and K*-based Mutational Scanning.

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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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