Structural coordinates: A novel approach to predict protein backbone conformation

被引:3
|
作者
Milchevskaya, Vladislava [1 ]
Nikitin, Alexei M. [2 ]
Lukshin, Sergey A. [2 ]
Filatov, Ivan V. [3 ]
Kravatsky, Yuri V. [2 ]
Tumanyan, Vladimir G. [2 ]
Esipova, Natalia G. [2 ]
Milchevskiy, Yury V. [2 ]
机构
[1] Univ Cologne, Fac Med, Inst Med Stat & Bioinformat, Cologne, Germany
[2] Engelhardt Inst Mol Biol, Moscow, Russia
[3] Moscow Inst Phys & Technol, Dolgoprudnyi, Russia
来源
PLOS ONE | 2021年 / 16卷 / 05期
基金
俄罗斯基础研究基金会;
关键词
RELATE; 2; SETS; SECONDARY STRUCTURE; ROTATION;
D O I
10.1371/journal.pone.0239793
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Motivation Local protein structure is usually described via classifying each peptide to a unique class from a set of pre-defined structures. These classifications may differ in the number of structural classes, the length of peptides, or class attribution criteria. Most methods that predict the local structure of a protein from its sequence first rely on some classification and only then proceed to the 3D conformation assessment. However, most classification methods rely on homologous proteins' existence, unavoidably lose information by attributing a peptide to a single class or suffer from a suboptimal choice of the representative classes. Results To alleviate the above challenges, we propose a method that constructs a peptide's structural representation from the sequence, reflecting its similarity to several basic representative structures. For 5-mer peptides and 16 representative structures, we achieved the Q16 classification accuracy of 67.9%, which is higher than what is currently reported in the literature. Our prediction method does not utilize information about protein homologues but relies only on the amino acids' physicochemical properties and the resolved structures' statistics. We also show that the 3D coordinates of a peptide can be uniquely recovered from its structural coordinates, and show the required conditions under various geometric constraints.
引用
收藏
页数:16
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