To improve the predictions of binding residues with DNA, RNA, carbohydrate, and peptide via multiple-task deep neural networks

Sun, Zhe; Zheng, Shuangjia; Zhao, Huiying; Niu, Zhangming; Lu, Yutong; Pan, Yi; Yang, Yuedong

doi:10.1101/2020.02.11.943571

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…or structural (e.g., solvent-accessible surface area, secondary structure type, etc.) descriptors fed into a machine learning model to score each amino acid for belonging to a binding site. − End-to-end methods operate directly with protein sequences , or structures by taking advantage of deep learning methods capable of learning features during training. With a continuously growing amount of structural data, it becomes possible to develop more robust methods using end-to-end deep learning approaches that work directly with spatial structures of protein complexes.…”

Section: Introductionmentioning

confidence: 99%

Protein–Peptide Binding Site Detection Using 3D Convolutional Neural Networks

Kozlovskii

Popov

2021

J. Chem. Inf. Model.

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Peptides and peptide-based molecules represent a promising therapeutic modality targeting intracellular protein−protein interactions, potentially combining the beneficial properties of biologics and small-molecule drugs. Protein−peptide complexes occupy a unique niche of interaction interfaces with respect to protein−protein and protein− small molecule complexes. Protein−peptide binding site identification resembles image object detection, a field that had been revolutionalized with computer vision techniques. We present a new protein−peptide binding site detection method called BiteNet Pp by harnessing the power of 3D convolutional neural network. Our method employs a tensor-based representation of spatial protein structures, which is fed to 3D convolutional neural network, resulting in probability scores and coordinates of the binding "hot spots" in the input structures. We used the domain adaptation technique to fine-tune model trained on protein−small molecule complexes using a manually curated set of protein−peptide structures. BiteNet Pp consistently outperforms existing state-of-the-art methods in the independent test benchmark. It takes less than a second to analyze a single-protein structure, making BiteNet Pp suitable for the large-scale analysis of protein− peptide binding sites.

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Section: Introductionmentioning

confidence: 99%

Protein–Peptide Binding Site Detection Using 3D Convolutional Neural Networks

Kozlovskii

Popov

2021

J. Chem. Inf. Model.

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AlphaFold2-aware protein-DNA binding site prediction using graph transformer

Yuan

Chen

Rao

et al. 2021

Preprint

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MotivationProtein-DNA interactions play crucial roles in the biological systems, and identifying protein-DNA binding sites is the first step for mechanistic understanding of various biological activities (such as transcription and repair) and designing novel drugs. How to accurately identify DNA-binding residues from only protein sequence remains a challenging task. Currently, most existing sequence-based methods only consider contextual features of the sequential neighbors, which are limited to capture spatial information.ResultsBased on the recent breakthrough in protein structure prediction by AlphaFold2, we propose an accurate predictor, GraphSite, for identifying DNA-binding residues based on the structural models predicted by AlphaFold2. Here, we convert the binding site prediction problem into a graph node classification task and employ a transformerbased variant model to take the protein structural information into account. By leveraging predicted protein structures and graph transformer, GraphSite substantially improves over the latest sequence-based and structure-based methods. The algorithm was further confirmed on the independent test set of 196 proteins, where GraphSite surpasses the state-of-the-art structure-based method by 12.3% in AUPR and 9.3% in MCC, respectively.Contactyangyd25@mail.sysu.edu.cn

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Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning

Yuan

Chen

Wang

et al. 2022

Preprint

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More than one-third of the proteins contain metal ions in the Protein Data Bank. Correct identification of metal ion-binding residues is important for understanding protein functions and designing novel drugs. Due to the small size and high versatility of metal ions, it remains challenging to computationally predict their binding sites from protein sequence. Existing sequence-based methods are of low accuracy due to the lack of structural information, and time-consuming owing to the usage of multi-sequence alignment. Here, we propose LMetalSite, an alignment-free sequence-based predictor for binding sites of the four most frequently seen metal ions (Zn2+, Ca2+, Mg2+ and Mn2+). LMetalSite leverages the pretrained language model to rapidly generate informative sequence representations and employs transformer to capture long-range dependencies. Multi-task learning is adopted to compensate for the scarcity of training data and capture the intrinsic similarities between different metal ions. LMetalSite was shown to surpass state-of-the-art structure-based methods by more than 19.7%, 14.4%, 36.8%, and 12.6% in AUPR on the four independent tests, respectively. Further analyses indicated that the self-attention modules are effective to learn the structural contexts of residues from protein sequence.

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To improve the predictions of binding residues with DNA, RNA, carbohydrate, and peptide via multiple-task deep neural networks

Cited by 4 publications

References 33 publications

Protein–Peptide Binding Site Detection Using 3D Convolutional Neural Networks

Protein–Peptide Binding Site Detection Using 3D Convolutional Neural Networks

AlphaFold2-aware protein-DNA binding site prediction using graph transformer

Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning

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