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Jiang Y, Yan R, Wang X. PlantNh-Kcr: a deep learning model for predicting non-histone crotonylation sites in plants. Plant Methods 2024; 20:28. [PMID: 38360730 PMCID: PMC10870457 DOI: 10.1186/s13007-024-01157-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/07/2024] [Indexed: 02/17/2024]
Abstract
BACKGROUND Lysine crotonylation (Kcr) is a crucial protein post-translational modification found in histone and non-histone proteins. It plays a pivotal role in regulating diverse biological processes in both animals and plants, including gene transcription and replication, cell metabolism and differentiation, as well as photosynthesis. Despite the significance of Kcr, detection of Kcr sites through biological experiments is often time-consuming, expensive, and only a fraction of crotonylated peptides can be identified. This reality highlights the need for efficient and rapid prediction of Kcr sites through computational methods. Currently, several machine learning models exist for predicting Kcr sites in humans, yet models tailored for plants are rare. Furthermore, no downloadable Kcr site predictors or datasets have been developed specifically for plants. To address this gap, it is imperative to integrate existing Kcr sites detected in plant experiments and establish a dedicated computational model for plants. RESULTS Most plant Kcr sites are located on non-histones. In this study, we collected non-histone Kcr sites from five plants, including wheat, tabacum, rice, peanut, and papaya. We then conducted a comprehensive analysis of the amino acid distribution surrounding these sites. To develop a predictive model for plant non-histone Kcr sites, we combined a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and attention mechanism to build a deep learning model called PlantNh-Kcr. On both five-fold cross-validation and independent tests, PlantNh-Kcr outperformed multiple conventional machine learning models and other deep learning models. Furthermore, we conducted an analysis of species-specific effect on the PlantNh-Kcr model and found that a general model trained using data from multiple species outperforms species-specific models. CONCLUSION PlantNh-Kcr represents a valuable tool for predicting plant non-histone Kcr sites. We expect that this model will aid in addressing key challenges and tasks in the study of plant crotonylation sites.
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Affiliation(s)
- Yanming Jiang
- College of Mathematics and Computer Sciences, Shanxi Normal University, Taiyuan, 030031, China
| | - Renxiang Yan
- The Key Laboratory of Marine Enzyme Engineering of Fujian Province, Fuzhou University, Fuzhou, 350002, China
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350002, China
| | - Xiaofeng Wang
- College of Mathematics and Computer Sciences, Shanxi Normal University, Taiyuan, 030031, China.
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2
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Dhakal P, Tayara H, Chong KT. An ensemble of stacking classifiers for improved prediction of miRNA-mRNA interactions. Comput Biol Med 2023; 164:107242. [PMID: 37473564 DOI: 10.1016/j.compbiomed.2023.107242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 06/21/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Abstract
MicroRNAs (miRNAs) are small non-coding RNA molecules that play a crucial role in regulating gene expression at the post-transcriptional level by binding to potential target sites of messenger RNAs (mRNAs), facilitated by the Argonaute family of proteins. Selecting the conservative candidate target sites (CTS) is a challenging step, considering that most of the existing computational algorithms primarily focus on canonical site types, which is a time-consuming and inefficient utilization of miRNA target site interactions. We developed a stacking classifier algorithm that addresses the CTS selection criteria using feature-encoding techniques that generates feature vectors, including k-mer nucleotide composition, dinucleotide composition, pseudo-nucleotide composition, and sequence order coupling. This innovative stacking classifier algorithm surpassed previous state-of-the-art algorithms in predicting functional miRNA targets. We evaluated the performance of the proposed model on 10 independent test datasets and obtained an average accuracy of 79.77%, which is a significant improvement of 7.26 % over previous models. This improvement shows that the proposed method has great potential for distinguishing highly functional miRNA targets and can serve as a valuable tool in biomedical and drug development research.
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Affiliation(s)
- Priyash Dhakal
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, 54896, Jeollabuk-do, Republic of Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju-si, 54896, Jeollabuk-do, Republic of Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, 54896, Jeollabuk-do, Republic of Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju-si, 54896, Jeollabuk-do, Republic of Korea.
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Zhang Z, Li F, Zhao J, Zheng C. CapsNetYY1: identifying YY1-mediated chromatin loops based on a capsule network architecture. BMC Genomics 2023; 24:448. [PMID: 37559017 PMCID: PMC10410878 DOI: 10.1186/s12864-023-09217-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 02/28/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Previous studies have identified that chromosome structure plays a very important role in gene control. The transcription factor Yin Yang 1 (YY1), a multifunctional DNA binding protein, could form a dimer to mediate chromatin loops and active enhancer-promoter interactions. The deletion of YY1 or point mutations at the YY1 binding sites significantly inhibit the enhancer-promoter interactions and affect gene expression. To date, only a few computational methods are available for identifying YY1-mediated chromatin loops. RESULTS We proposed a novel model named CapsNetYY1, which was based on capsule network architecture to identify whether a pair of YY1 motifs can form a chromatin loop. Firstly, we encode the DNA sequence using one-hot encoding method. Secondly, multi-scale convolution layer is used to extract local features of the sequence, and bidirectional gated recurrent unit is used to learn the features across time steps. Finally, capsule networks (convolution capsule layer and digital capsule layer) used to extract higher level features and recognize YY1-mediated chromatin loops. Compared with DeepYY1, the only prediction for YY1-mediated chromatin loops, our model CapsNetYY1 achieved the better performance on the independent datasets (AUC [Formula: see text]). CONCLUSION The results indicate that CapsNetYY1 is an excellent method for identifying YY1-mediated chromatin loops. We believe that the CapsNetYY1 method will be used for predictive classification of other DNA sequences.
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Affiliation(s)
- Zhimin Zhang
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China
| | - Fenglin Li
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China
| | - Jianping Zhao
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China.
| | - Chunhou Zheng
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Information Materials and Intelligent Sensing Laboratory of Anhui Province, and School of Artificial Intelligence, Anhui University, Hefei, China.
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Wang R, Chung CR, Huang HD, Lee TY. Identification of species-specific RNA N6-methyladinosine modification sites from RNA sequences. Brief Bioinform 2023; 24:7008797. [PMID: 36715277 DOI: 10.1093/bib/bbac573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/11/2022] [Accepted: 11/24/2022] [Indexed: 01/31/2023] Open
Abstract
N6-methyladinosine (m6A) modification is the most abundant co-transcriptional modification in eukaryotic RNA and plays important roles in cellular regulation. Traditional high-throughput sequencing experiments used to explore functional mechanisms are time-consuming and labor-intensive, and most of the proposed methods focused on limited species types. To further understand the relevant biological mechanisms among different species with the same RNA modification, it is necessary to develop a computational scheme that can be applied to different species. To achieve this, we proposed an attention-based deep learning method, adaptive-m6A, which consists of convolutional neural network, bi-directional long short-term memory and an attention mechanism, to identify m6A sites in multiple species. In addition, three conventional machine learning (ML) methods, including support vector machine, random forest and logistic regression classifiers, were considered in this work. In addition to the performance of ML methods for multi-species prediction, the optimal performance of adaptive-m6A yielded an accuracy of 0.9832 and the area under the receiver operating characteristic curve of 0.98. Moreover, the motif analysis and cross-validation among different species were conducted to test the robustness of one model towards multiple species, which helped improve our understanding about the sequence characteristics and biological functions of RNA modifications in different species.
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Affiliation(s)
- Rulan Wang
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
| | - Chia-Ru Chung
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
- School of Life Sciences, University of Science and Technology of China, 230026, Hefei, Anhui, P.R. China
| | - Hsien-Da Huang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
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Hu W, Zhang W, Zhou Y, Luo Y, Sun X, Xu H, Shi S, Li T, Xu Y, Yang Q, Qiu Y, Zhu F, Dai H. MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction. J Chem Inf Model 2023; 63:1626-1636. [PMID: 36802582 DOI: 10.1021/acs.jcim.2c01656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.
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Affiliation(s)
- Wei Hu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Huimin Xu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Teng Li
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Yichao Xu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Qianqian Yang
- Department of Pharmacy, Affiliated Hangzhou First Peoples Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.,Clinical Pharmacy Research Center, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Feng Zhu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Haibin Dai
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.,Clinical Pharmacy Research Center, Zhejiang University School of Medicine, Hangzhou 310009, China
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Li W, Wang J, Luo Y, Bezabih TT. Multi-dimensional feature recognition model based on capsule network for ubiquitination site prediction. PeerJ 2022; 10:e14427. [PMID: 36523471 PMCID: PMC9745908 DOI: 10.7717/peerj.14427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 10/30/2022] [Indexed: 12/12/2022] Open
Abstract
Ubiquitination is an important post-translational modification of proteins that regulates many cellular activities. Traditional experimental methods for identification are costly and time-consuming, so many researchers have proposed computational methods for ubiquitination site prediction in recent years. However, traditional machine learning methods focus on feature engineering and are not suitable for large-scale proteomic data. In addition, deep learning methods are mostly based on convolutional neural networks and fuse multiple coding approaches to achieve classification prediction. This cannot effectively identify potential fine-grained features of the input data and has limitations in the representation of dependencies between low-level features and high-level features. A multi-dimensional feature recognition model based on a capsule network (MDCapsUbi) was proposed to predict protein ubiquitination sites. The proposed module consisting of convolution operations and channel attention was used to recognize coarse-grained features in the sequence dimension and the feature map dimension. The capsule network module consisting of capsule vectors was used to identify fine-grained features and classify ubiquitinated sites. With ten-fold cross-validation, the MDCapsUbi achieved 91.82% accuracy, 91.39% sensitivity, 92.24% specificity, 0.837 MCC, 0.918 F-Score and 0.97 AUC. Experimental results indicated that the proposed method outperformed other ubiquitination site prediction technologies.
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Affiliation(s)
- Weimin Li
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Jie Wang
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Yin Luo
- School of Life Sciences, East China Normal University, Shanghai, China
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Mansoor M, Nauman M, Rehman HU, Omar M. Gene Ontology Capsule GAN: an improved architecture for protein function prediction. PeerJ Comput Sci 2022; 8:e1014. [PMID: 36092003 PMCID: PMC9454774 DOI: 10.7717/peerj-cs.1014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
Proteins are the core of all functions pertaining to living things. They consist of an extended amino acid chain folding into a three-dimensional shape that dictates their behavior. Currently, convolutional neural networks (CNNs) have been pivotal in predicting protein functions based on protein sequences. While it is a technology crucial to the niche, the computation cost and translational invariance associated with CNN make it impossible to detect spatial hierarchies between complex and simpler objects. Therefore, this research utilizes capsule networks to capture spatial information as opposed to CNNs. Since capsule networks focus on hierarchical links, they have a lot of potential for solving structural biology challenges. In comparison to the standard CNNs, our results exhibit an improvement in accuracy. Gene Ontology Capsule GAN (GOCAPGAN) achieved an F1 score of 82.6%, a precision score of 90.4% and recall score of 76.1%.
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Liang Y, Wu Y, Zhang Z, Liu N, Peng J, Tang J. Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction. BMC Bioinformatics 2022; 23:258. [PMID: 35768759 PMCID: PMC9241225 DOI: 10.1186/s12859-022-04789-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 06/10/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND DNA N4-methylcytosine is part of the restrictive modification system, which works by regulating some biological processes, for example, the initiation of DNA replication, mismatch repair and inactivation of transposon. However, using experimental methods to detect 4mC sites is time-consuming and expensive. Besides, considering the huge differences in the number of 4mC samples among different species, it is challenging to achieve a robust multi-species 4mC site prediction performance. Hence, it is of great significance to develop effective computational tools to identify 4mC sites. RESULTS This work proposes a flexible deep learning-based framework to predict 4mC sites, called Hyb4mC. Hyb4mC adopts the DNA2vec method for sequence embedding, which captures more efficient and comprehensive information compared with the sequence-based feature method. Then, two different subnets are used for further analysis: Hyb_Caps and Hyb_Conv. Hyb_Caps is composed of a capsule neural network and can generalize from fewer samples. Hyb_Conv combines the attention mechanism with a text convolutional neural network for further feature learning. CONCLUSIONS Extensive benchmark tests have shown that Hyb4mC can significantly enhance the performance of predicting 4mC sites compared with the recently proposed methods.
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Affiliation(s)
- Ying Liang
- College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China.
| | - Yanan Wu
- College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Zequn Zhang
- College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Niannian Liu
- College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Jun Peng
- College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Jianjun Tang
- College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China
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