利用窗口多序列比对提高嵌合蛋白预测精度。

IF 4.1 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Computational and structural biotechnology journal Pub Date : 2025-07-23 eCollection Date: 2025-01-01 DOI:10.1016/j.csbj.2025.07.039
Sanketh Vedula, Alex M Bronstein, Ailie Marx
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引用次数: 0

摘要

蛋白质结构预测的关键步骤是检测共同进化的残基对,这是空间接近的信号。这些信息是从多个序列比对中收集的,并强调了Alphafold对几乎所有已知蛋白质的结构预测。有一种简单的方法可以制造出超越自然界的蛋白质,那就是将两种已知的蛋白质或蛋白质部分非自然地融合在一起。在这里,我们证明了当结构肽被添加到支架蛋白的末端时,预测的准确性显着降低。将单个肽标签的多序列比对附加到支架蛋白的多序列比对中通常可以恢复预测的准确性。这项工作表明,这种窗口多序列比对方法可以成为预测融合嵌合蛋白结构的有用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment.

A key step in protein structure prediction involves the detection of co-evolving pairs of residues, a signal for spatial proximity. This information is gleaned from multiple sequence alignment and underscores Alphafold's structure prediction for almost every known protein. A simple means to create proteins beyond those found in nature, is by unnaturally fusing together two known proteins or protein parts. Here we demonstrate that structured peptides are predicted with significantly reduced accuracy when added to the terminal ends of scaffold proteins. Appending the multiple sequence alignment for the individual peptide tags to that of the scaffold protein often restores prediction accuracy. This work suggests that this windowed multiple sequence alignment approach can be a useful tool for predicting the structure of fused, chimeric proteins.

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来源期刊
Computational and structural biotechnology journal
Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
9.30
自引率
3.30%
发文量
540
审稿时长
6 weeks
期刊介绍: Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology
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