Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Cheng Zhu, Sen Cao, Tianfeng Shang, Jingjing Guo, An Su, Chengxi Li, Hongliang Duan
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引用次数: 0

Abstract

Cyclic peptides containing unnatural amino acids possess many excellent properties and have become promising candidates in drug discovery. Therefore, accurately predicting the 3D structures of cyclic peptides containing unnatural residues will significantly advance the development of cyclic peptide-based therapeutics. Although deep learning-based structural prediction models have made tremendous progress, these models still cannot predict the structures of cyclic peptides containing unnatural amino acids. To address this gap, we introduce a novel model, HighFold2, built upon the AlphaFold-Multimer framework. HighFold2 first extends the pre-defined rigid groups and their initial atomic coordinates from natural amino acids to unnatural amino acids, thus enabling structural prediction for these residues. Then, it incorporates an additional neural network to characterize the atom-level features of peptides, allowing for multi-scale modeling of peptide molecules while enabling the distinction between various unnatural amino acids. Besides, HighFold2 constructs a relative position encoding matrix for cyclic peptides based on different cyclization constraints. Except for training using spatial structures with unnatural amino acids, HighFold2 also parameterizes the unnatural amino acids to relax the predicted structure by energy minimization for clash elimination. Extensive empirical experiments demonstrate that HighFold2 can accurately predict the 3D structures of cyclic peptide monomers containing unnatural amino acids and their complexes with proteins, with the median RMSD for Cα reaching 1.891 Å. All these results indicate the effectiveness of HighFold2, representing a significant advancement in cyclic peptide-based drug discovery.

利用HighFold2预测含非天然氨基酸环肽的结构。
含有非天然氨基酸的环肽具有许多优良的性质,在药物开发中具有广阔的应用前景。因此,准确预测含有非天然残基的环肽的三维结构将大大促进环肽治疗的发展。尽管基于深度学习的结构预测模型已经取得了巨大的进展,但这些模型仍然不能预测含有非天然氨基酸的环肽的结构。为了解决这个问题,我们引入了一个新的模型,HighFold2,它建立在alphafold - multitimer框架之上。HighFold2首先将预定义的刚性基团及其初始原子坐标从天然氨基酸扩展到非天然氨基酸,从而实现对这些残基的结构预测。然后,它结合了一个额外的神经网络来表征肽的原子水平特征,允许多肽分子的多尺度建模,同时能够区分各种非天然氨基酸。此外,HighFold2构建了基于不同环化约束的环肽相对位置编码矩阵。除了使用含有非天然氨基酸的空间结构进行训练外,HighFold2还对非天然氨基酸进行参数化,通过能量最小化来放松预测结构,从而消除冲突。大量的实证实验表明,HighFold2可以准确预测含有非天然氨基酸的环肽单体及其与蛋白质配合物的三维结构,Cα的中位数RMSD达到1.891 Å。这些结果表明了HighFold2的有效性,代表了基于环肽的药物发现的重大进展。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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