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Article ; Online: A comprehensive survey on protein-ligand binding site prediction.

Xia, Ying / Pan, Xiaoyong / Shen, Hong-Bin

2024 Volume 86, Page(s) 102793

Abstract: Protein-ligand binding site prediction is critical for protein function annotation and drug discovery. Biological experiments are time-consuming and require significant equipment, materials, and labor resources. Developing accurate and efficient ... ...

Abstract	Protein-ligand binding site prediction is critical for protein function annotation and drug discovery. Biological experiments are time-consuming and require significant equipment, materials, and labor resources. Developing accurate and efficient computational methods for protein-ligand interaction prediction is essential. Here, we summarize the key challenges associated with ligand binding site (LBS) prediction and introduce recently published methods from their input features, computational algorithms, and ligand types. Furthermore, we investigate the specificity of allosteric site identification as a particular LBS type. Finally, we discuss the prospective directions for machine learning-based LBS prediction in the near future.
Language	English
Publishing date	2024-03-05
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1068353-7
ISSN	1879-033X ; 0959-440X
ISSN (online)	1879-033X
ISSN	0959-440X
DOI	10.1016/j.sbi.2024.102793
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Article ; Online: Heterogeneous sampled subgraph neural networks with knowledge distillation to enhance double-blind compound-protein interaction prediction.

Xia, Ying / Pan, Xiaoyong / Shen, Hong-Bin

Structure (London, England : 1993)

2024

Abstract: Identifying binding compounds against a target protein is crucial for large-scale virtual screening in drug development. Recently, network-based methods have been developed for compound-protein interaction (CPI) prediction. However, they are difficult to ...

Abstract	Identifying binding compounds against a target protein is crucial for large-scale virtual screening in drug development. Recently, network-based methods have been developed for compound-protein interaction (CPI) prediction. However, they are difficult to be applied to unseen (i.e., never-seen-before) proteins and compounds. In this study, we propose SgCPI to incorporate local known interacting networks to predict CPI interactions. SgCPI randomly samples the local CPI network of the query compound-protein pair as a subgraph and applies a heterogeneous graph neural network (HGNN) to embed the active/inactive message of the subgraph. For unseen compounds and proteins, SgCPI-KD takes SgCPI as the teacher model to distillate its knowledge by estimating the potential neighbors. Experimental results indicate: (1) the sampled subgraphs of the CPI network introduce efficient knowledge for unseen molecular prediction with the HGNNs, and (2) the knowledge distillation strategy is beneficial to the double-blind interaction prediction by estimating molecular neighbors and distilling knowledge.
Language	English
Publishing date	2024-03-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	1213087-4
ISSN	1878-4186 ; 0969-2126
ISSN (online)	1878-4186
ISSN	0969-2126
DOI	10.1016/j.str.2024.02.004
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Article ; Online: TFvelo: gene regulation inspired RNA velocity estimation.

Li, Jiachen / Pan, Xiaoyong / Yuan, Ye / Shen, Hong-Bin

Nature communications

2024 Volume 15, Issue 1, Page(s) 1387

Abstract: RNA velocity is closely related with cell fate and is an important indicator for the prediction of cell states with elegant physical explanation derived from single-cell RNA-seq data. Most existing RNA velocity models aim to extract dynamics from the ... ...

Abstract	RNA velocity is closely related with cell fate and is an important indicator for the prediction of cell states with elegant physical explanation derived from single-cell RNA-seq data. Most existing RNA velocity models aim to extract dynamics from the phase delay between unspliced and spliced mRNA for each individual gene. However, unspliced/spliced mRNA abundance may not provide sufficient signal for dynamic modeling, leading to poor fit in phase portraits. Motivated by the idea that RNA velocity could be driven by the transcriptional regulation, we propose TFvelo, which expands RNA velocity concept to various single-cell datasets without relying on splicing information, by introducing gene regulatory information. Our experiments on synthetic data and multiple scRNA-Seq datasets show that TFvelo can accurately fit genes dynamics on phase portraits, and effectively infer cell pseudo-time and trajectory from RNA abundance data. TFvelo opens a robust and accurate avenue for modeling RNA velocity for single cell data.
MeSH term(s)	RNA/genetics ; RNA Splicing/genetics ; RNA, Messenger/genetics ; Sequence Analysis, RNA ; Single-Cell Analysis ; Gene Expression Profiling
Chemical Substances	RNA (63231-63-0) ; RNA, Messenger
Language	English
Publishing date	2024-02-15
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-024-45661-w
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Article ; Online: LigBind: Identifying Binding Residues for Over 1000 Ligands with Relation-Aware Graph Neural Networks.

Xia, Ying / Pan, Xiaoyong / Shen, Hong-Bin

Journal of molecular biology

2023 Volume 435, Issue 13, Page(s) 168091

Abstract: Identifying the interactions between proteins and ligands is significant for drug discovery and design. Considering the diverse binding patterns of ligands, the ligand-specific methods are trained per ligand to predict binding residues. However, most of ... ...

Abstract	Identifying the interactions between proteins and ligands is significant for drug discovery and design. Considering the diverse binding patterns of ligands, the ligand-specific methods are trained per ligand to predict binding residues. However, most of the existing ligand-specific methods ignore shared binding preferences among various ligands and generally only cover a limited number of ligands with a sufficient number of known binding proteins. In this study, we propose a relation-aware framework LigBind with graph-level pre-training to enhance the ligand-specific binding residue predictions for 1159 ligands, which can effectively cover the ligands with a few known binding proteins. LigBind first pre-trains a graph neural network-based feature extractor for ligand-residue pairs and relation-aware classifiers for similar ligands. Then, LigBind is fine-tuned with ligand-specific binding data, where a domain adaptive neural network is designed to automatically leverage the diversity and similarity of various ligand-binding patterns for accurate binding residue prediction. We construct ligand-specific benchmark datasets of 1159 ligands and 16 unseen ligands, which are used to evaluate the effectiveness of LigBind. The results demonstrate the LigBind's efficacy on large-scale ligand-specific benchmark datasets, and it generalizes well to unseen ligands. LigBind also enables accurate identification of the ligand-binding residues in the main protease, papain-like protease and the RNA-dependent RNA polymerase of SARS-CoV-2. The web server and source codes of LigBind are available at http://www.csbio.sjtu.edu.cn/bioinf/LigBind/ and https://github.com/YYingXia/LigBind/ for academic use.
MeSH term(s)	Humans ; Binding Sites ; Ligands ; Neural Networks, Computer ; Protein Binding ; SARS-CoV-2 ; Viral Proteins
Chemical Substances	Ligands ; Viral Proteins
Language	English
Publishing date	2023-04-12
Publishing country	Netherlands
Document type	Dataset ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2023.168091
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Article ; Online: De novo drug design by iterative multiobjective deep reinforcement learning with graph-based molecular quality assessment.

Fang, Yi / Pan, Xiaoyong / Shen, Hong-Bin

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 4

Abstract: Motivation: Generating molecules of high quality and drug-likeness in the vast chemical space is a big challenge in the drug discovery. Most existing molecule generative methods focus on diversity and novelty of molecules, but ignoring drug potentials ... ...

Abstract	Motivation: Generating molecules of high quality and drug-likeness in the vast chemical space is a big challenge in the drug discovery. Most existing molecule generative methods focus on diversity and novelty of molecules, but ignoring drug potentials of the generated molecules during the generation process. Results: In this study, we present a novel de novo multiobjective quality assessment-based drug design approach (QADD), which integrates an iterative refinement framework with a novel graph-based molecular quality assessment model on drug potentials. QADD designs a multiobjective deep reinforcement learning pipeline to generate molecules with multiple desired properties iteratively, where a graph neural network-based model for accurate molecular quality assessment on drug potentials is introduced to guide molecule generation. Experimental results show that QADD can jointly optimize multiple molecular properties with a promising performance and the quality assessment module is capable of guiding the generated molecules with high drug potentials. Furthermore, applying QADD to generate novel molecules binding to a biological target protein DRD2 also demonstrates the algorithm's efficacy. Availability and implementation: QADD is freely available online for academic use at https://github.com/yifang000/QADD or http://www.csbio.sjtu.edu.cn/bioinf/QADD.
MeSH term(s)	Neural Networks, Computer ; Models, Molecular ; Proteins ; Drug Design
Chemical Substances	Proteins
Language	English
Publishing date	2023-03-24
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad157
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Article ; Online: ELMo4m6A: A Contextual Language Embedding-Based Predictor for Detecting RNA N6-Methyladenosine Sites.

Fan, Yongxian / Sun, Guicong / Pan, Xiaoyong

IEEE/ACM transactions on computational biology and bioinformatics

2023 Volume 20, Issue 2, Page(s) 944–954

Abstract: N6-methyladenosine (m6A) is a universal post-transcriptional modification of RNAs, and it is widely involved in various biological processes. Identifying m6A modification sites accurately is indispensable to further investigate m6A-mediated biological ... ...

Abstract	N6-methyladenosine (m6A) is a universal post-transcriptional modification of RNAs, and it is widely involved in various biological processes. Identifying m6A modification sites accurately is indispensable to further investigate m6A-mediated biological functions. How to better represent RNA sequences is crucial for building effective computational methods for detecting m6A modification sites. However, traditional encoding methods require complex biological prior knowledge and are time-consuming. Furthermore, most of the existing m6A sites prediction methods are limited to single species, and few methods are able to predict m6A sites across different species and tissues. Thus, it is necessary to design a more efficient computational method to predict m6A sites across multiple species and tissues. In this paper, we proposed ELMo4m6A, a contextual language embedding-based method for predicting m6A sites from RNA sequences without any prior knowledge. ELMo4m6A first learns embeddings of RNA sequences using a language model ELMo, then uses a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) to identify m6A sites. The results of 5-fold cross-validation and independent testing demonstrate that ELMo4m6A is superior to state-of-the-art methods. Moreover, we applied integrated gradients to find potential sequence patterns contributing to m6A sites.
MeSH term(s)	RNA/genetics ; Adenosine/genetics ; Neural Networks, Computer ; Sequence Analysis, RNA/methods
Chemical Substances	RNA (63231-63-0) ; Adenosine (K72T3FS567)
Language	English
Publishing date	2023-04-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2022.3173323
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Article ; Online: Application value of bedside ultrasound for assessing volume responsiveness in patients with septic shock

He Hao / Pan Nifang / Zhou Xiaoyong

Vojnosanitetski Pregled, Vol 80, Iss 5, Pp 439-

2023 Volume 445

Abstract: Background/Aim. Septic shock (SS) is a complication that can occur as a consequence of an infection. As the effective circulating blood volume is of great importance in these cases, keeping constant track of the blood volume parameter is essential. The ... ...

Abstract	Background/Aim. Septic shock (SS) is a complication that can occur as a consequence of an infection. As the effective circulating blood volume is of great importance in these cases, keeping constant track of the blood volume parameter is essential. The aim of this study was to explore the application value of bedside ultrasound for assessing volume responsiveness (VR) in patients with SS. Methods. A total of 102 patients with SS were selected. The volume load (VL) test was performed, and based on the results of the test, the patients were divided into two groups. The first group was the response (R) group, which had an increase in stroke volume (ΔSV) ≥ 15% after the VL test, and the second was the non-response (NR) group, with ΔSV < 15% after the VL test. There were 54 patients in the R group and 48 in the NR group. Hemodynamic parameters were compared before and after the VL test. The correlation between ΔSV and each hemodynamic index was explored by Pearson’s analysis. The receiver operating characteristic (ROC) curves were plotted for some of the parameters. Results. Before the VL test, retro-hepatic (RH) inferior vena cava (IVC) (RHIVC) distensibility (ΔRHIVC1) index, respiratory variation in RHIVC (ΔRHIVC2) index, respiratory variation in aortic (AO) blood flow peak velocity (ΔVpeakAO) index, respiratory variation in brachial artery (BA) blood flow peak velocity (ΔVpeakBA) index, and respiratory variation in common femoral artery (CFA) blood flow peak velocity (ΔVpeakCFA) index were all higher in the R group than those in the NR group (p < 0.05), while heart rate (HR), mean arterial pressure (MAP), and central venous pressure (CVP) were similar in both groups (p > 0.05). After the VL test, the R group had significantly decreased values of HR and the ΔRHIVC1, ΔRHIVC2, ΔVpeakAO, ΔVpeakBA, and ΔVpeakCFA indices, while the MAP and CVP values (p < 0.05) were increased. The NR group had a significantly decreased value of CVP (p < 0.05), while no significant changes were noticed in the values of ...
Keywords	blood volume ; hemodynamic monitoring ; infusions ; intravenous ; saline solution ; shock ; septic ; ultrasonography ; Medicine (General) ; R5-920
Subject code	610
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Military Health Department, Ministry of Defance, Serbia
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Adsorption and sensing performance of air pollutants on a β-TeO

Wang, Ying / Guo, Shiying / Xu, Xiaoyong / Pan, Jing / Hu, Jingguo / Zhang, Shengli

Physical chemistry chemical physics : PCCP

2023 Volume 26, Issue 1, Page(s) 612–620

Abstract: Two-dimensional (2D) β- ... ...

Abstract	Two-dimensional (2D) β-TeO
Language	English
Publishing date	2023-12-21
Publishing country	England
Document type	Journal Article
ZDB-ID	1476244-4
ISSN	1463-9084 ; 1463-9076
ISSN (online)	1463-9084
ISSN	1463-9076
DOI	10.1039/d3cp04400a
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Article: Clinical and genetic analysis of essential hypertension with mitochondrial tRNA

Guo, Meili / He, Yunfan / Chen, Ade / Zhuang, Zaishou / Pan, Xiaoyong / Guan, Minxin

Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences

2024 Volume 53, Issue 2, Page(s) 184–193

Abstract: Objectives: To investigate the role of m.4435A>G and : Methods: A hypertensive patient with m.4435A>G and : Results: Mitochondrial genome sequencing showed that all maternal members carried a highly conserved m.4435A>G mutation. The m.4435A>G ... ...

Title translation	携带线粒体tRNA
Abstract	Objectives: To investigate the role of m.4435A>G and Methods: A hypertensive patient with m.4435A>G and Results: Mitochondrial genome sequencing showed that all maternal members carried a highly conserved m.4435A>G mutation. The m.4435A>G mutation might affect the secondary structure and folding free energy of mitochondrial tRNA and change its stability, which may influence the anticodon ring structure. Compared with the control group, the cell lines carrying m.4435A>G and Conclusions: The
MeSH term(s)	Humans ; Essential Hypertension/genetics ; Mutation ; Male ; Reactive Oxygen Species/metabolism ; Membrane Potential, Mitochondrial/genetics ; Mitochondria/genetics ; RNA, Transfer/genetics ; RNA, Transfer, Met/genetics ; Genome, Mitochondrial ; Female
Chemical Substances	Reactive Oxygen Species ; RNA, Transfer (9014-25-9) ; RNA, Transfer, Met
Language	Chinese
Publishing date	2024-04-01
Publishing country	China
Document type	Journal Article ; Case Reports ; Research Support, Non-U.S. Gov't
ISSN	1008-9292
ISSN	1008-9292
DOI	10.3724/zdxbyxb-2023-0571
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Article ; Online: The mechanism of enhanced photocatalytic activity for water-splitting of ReS

Pan, Jing / Zhang, Wannian / Xu, Xiaoyong / Hu, Jingguo

RSC advances

2021 Volume 11, Issue 37, Page(s) 23055–23063

Abstract: To enhance the photocatalytic water splitting performance of 2D ... ...

Abstract	To enhance the photocatalytic water splitting performance of 2D ReS
Language	English
Publishing date	2021-06-30
Publishing country	England
Document type	Journal Article
ISSN	2046-2069
ISSN (online)	2046-2069
DOI	10.1039/d1ra03821d
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