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Incorporating NOE-Derived Distances in Conformer Generation of Cyclic Peptides with Distance Geometry

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acs.figshare.com2023-06-01 更新2025-01-21 收录
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https://acs.figshare.com/articles/dataset/Incorporating_NOE-Derived_Distances_in_Conformer_Generation_of_Cyclic_Peptides_with_Distance_Geometry/18428592/1
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Nuclear magnetic resonance (NMR) data from NOESY (nuclear Overhauser enhancement spectroscopy) and ROESY (rotating frame Overhauser enhancement spectroscopy) experiments can easily be combined with distance geometry (DG) based conformer generators by modifying the molecular distance bounds matrix. In this work, we extend the modern DG based conformer generator ETKDG, which has been shown to reproduce experimental crystal structures from small molecules to large macrocycles well, to include NOE-derived interproton distances. In noeETKDG, the experimentally derived interproton distances are incorporated into the distance bounds matrix as loose upper (or lower) bounds to generate large conformer sets. Various subselection techniques can subsequently be applied to yield a conformer bundle that best reproduces the NOE data. The approach is benchmarked using a set of 24 (mostly) cyclic peptides for which NOE-derived distances as well as reference solution structures obtained by other software are available. With respect to other packages currently available, the advantages of noeETKDG are its speed and that no prior force-field parametrization is required, which is especially useful for peptides with unnatural amino acids. The resulting conformer bundles can be further processed with the use of structural refinement techniques to improve the modeling of the intramolecular nonbonded interactions. The noeETKDG code is released as a fully open-source software package available at www.github.com/rinikerlab/customETKDG.

核磁共振(NMR)谱数据,包括NOESY(核Overhauser增强光谱学)和ROESY(旋转框Overhauser增强光谱学)实验数据,可通过调整分子距离界限矩阵,轻松与基于距离几何(DG)的构象生成器相结合。在本研究中,我们扩展了现代基于DG的构象生成器ETKDG,该生成器已被证明能够从小分子到大型环状化合物准确复现实验晶体结构,并纳入了由NOE(核Overhauser效应)得到的质子间距离。在noeETKDG中,实验得到的质子间距离被纳入距离界限矩阵作为宽松的上限(或下限),以生成大量构象集。随后,可以应用各种子选择技术,以获得最佳复现NOE数据的构象束。该方法通过一套24个(主要是)环状肽的数据集进行基准测试,这些数据集提供了NOE得到的距离以及其他软件获得的参考解构象。与现有其他软件包相比,noeETKDG的优势在于其速度以及无需先前的力场参数化,这对于含有非天然氨基酸的肽特别有用。生成的构象束可以通过结构精炼技术进一步处理,以优化分子内非键合相互作用的建模。noeETKDG代码作为完全开源的软件包发布,可在www.github.com/rinikerlab/customETKDG获取。
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