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鱼鹰:具有集成、灵活性和可验证算法的蛋白质设计

OSPREY: protein design with ensembles, flexibility, and provable algorithms.

作者信息

Gainza Pablo, Roberts Kyle E, Georgiev Ivelin, Lilien Ryan H, Keedy Daniel A, Chen Cheng-Yu, Reza Faisal, Anderson Amy C, Richardson David C, Richardson Jane S, Donald Bruce R

机构信息

Department of Computer Science, Duke University, Durham, North Carolina, USA.

出版信息

Methods Enzymol. 2013;523:87-107. doi: 10.1016/B978-0-12-394292-0.00005-9.

Abstract

UNLABELLED

We have developed a suite of protein redesign algorithms that improves realistic in silico modeling of proteins. These algorithms are based on three characteristics that make them unique: (1) improved flexibility of the protein backbone, protein side-chains, and ligand to accurately capture the conformational changes that are induced by mutations to the protein sequence; (2) modeling of proteins and ligands as ensembles of low-energy structures to better approximate binding affinity; and (3) a globally optimal protein design search, guaranteeing that the computational predictions are optimal with respect to the input model. Here, we illustrate the importance of these three characteristics. We then describe OSPREY, a protein redesign suite that implements our protein design algorithms. OSPREY has been used prospectively, with experimental validation, in several biomedically relevant settings. We show in detail how OSPREY has been used to predict resistance mutations and explain why improved flexibility, ensembles, and provability are essential for this application.

AVAILABILITY

OSPREY is free and open source under a Lesser GPL license. The latest version is OSPREY 2.0. The program, user manual, and source code are available at www.cs.duke.edu/donaldlab/software.php.

CONTACT

osprey@cs.duke.edu.

摘要

未标注

我们开发了一套蛋白质重新设计算法,可改进蛋白质的真实计算机模拟。这些算法基于三个使其独特的特性:(1)提高蛋白质主链、蛋白质侧链和配体的灵活性,以准确捕捉由蛋白质序列突变引起的构象变化;(2)将蛋白质和配体建模为低能量结构的集合,以更好地近似结合亲和力;(3)进行全局最优蛋白质设计搜索,确保计算预测相对于输入模型是最优的。在此,我们阐述这三个特性的重要性。然后我们描述了OSPREY,一个实现我们蛋白质设计算法的蛋白质重新设计套件。OSPREY已在多个与生物医学相关的场景中进行了前瞻性使用,并经过实验验证。我们详细展示了OSPREY如何用于预测抗性突变,并解释了为何改进的灵活性、集合和可证性对于此应用至关重要。

可用性

OSPREY根据较小通用公共许可证(Lesser GPL license)免费开源。最新版本是OSPREY 2.0。该程序、用户手册和源代码可在www.cs.duke.edu/donaldlab/software.php获取。

联系方式

osprey@cs.duke.edu

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本文引用的文献

2
Computational design of a PDZ domain peptide inhibitor that rescues CFTR activity.
PLoS Comput Biol. 2012;8(4):e1002477. doi: 10.1371/journal.pcbi.1002477. Epub 2012 Apr 19.
3
Protein design using continuous rotamers.
PLoS Comput Biol. 2012 Jan;8(1):e1002335. doi: 10.1371/journal.pcbi.1002335. Epub 2012 Jan 12.
4
Protein loop closure using orientational restraints from NMR data.
Proteins. 2012 Feb;80(2):433-53. doi: 10.1002/prot.23207. Epub 2011 Dec 13.
5
A Bayesian approach for determining protein side-chain rotamer conformations using unassigned NOE data.
J Comput Biol. 2011 Nov;18(11):1661-79. doi: 10.1089/cmb.2011.0172. Epub 2011 Oct 4.
6
Protein side-chain resonance assignment and NOE assignment using RDC-defined backbones without TOCSY data.
J Biomol NMR. 2011 Aug;50(4):371-95. doi: 10.1007/s10858-011-9522-4. Epub 2011 Jun 25.
7
The dead-end elimination theorem and its use in protein side-chain positioning.
Nature. 1992 Apr 9;356(6369):539-42. doi: 10.1038/356539a0.
8
Predicting resistance mutations using protein design algorithms.
Proc Natl Acad Sci U S A. 2010 Aug 3;107(31):13707-12. doi: 10.1073/pnas.1002162107. Epub 2010 Jul 19.
9
CHARMM: the biomolecular simulation program.
J Comput Chem. 2009 Jul 30;30(10):1545-614. doi: 10.1002/jcc.21287.
10
Computational structure-based redesign of enzyme activity.
Proc Natl Acad Sci U S A. 2009 Mar 10;106(10):3764-9. doi: 10.1073/pnas.0900266106. Epub 2009 Feb 19.

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